changeset 6:33816f44fc7d draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/flexynesis commit 6b520305ec30e6dc37eba92c67a5368cea0fc5ad
author bgruening
date Wed, 23 Jul 2025 07:49:41 +0000
parents 466b593fd87e
children
files convert.py flexynesis.xml flexynesis_plot.py flexynesis_utils.py index_to_name.py macros.xml test-data/feature_importance.tabular test-data/labels_survival.tabular test-data/non_flexy_label.tabular test-data/test/clin test-data/train/clin
diffstat 11 files changed, 3876 insertions(+), 2479 deletions(-) [+]
line wrap: on
line diff
--- a/convert.py	Fri Jul 04 14:57:40 2025 +0000
+++ b/convert.py	Wed Jul 23 07:49:41 2025 +0000
@@ -8,12 +8,16 @@
 def tabular_to_csv(tabular_file, csv_file):
     """Convert tabular (TSV) to CSV"""
     data = pd.read_csv(tabular_file, sep="\t")
+    if data.columns[0] == '' or data.columns[0].startswith('Unnamed:'):
+        data.columns = ['ID'] + list(data.columns[1:])
     data.to_csv(csv_file, index=False)
 
 
 def csv_to_tabular(csv_file, tabular_file):
     """Convert CSV to tabular (TSV)"""
     data = pd.read_csv(csv_file)
+    if data.columns[0] == '' or data.columns[0].startswith('Unnamed:'):
+        data.columns = ['ID'] + list(data.columns[1:])
     data.to_csv(tabular_file, sep="\t", index=False)
 
 
--- a/flexynesis.xml	Fri Jul 04 14:57:40 2025 +0000
+++ b/flexynesis.xml	Wed Jul 23 07:49:41 2025 +0000
@@ -7,15 +7,18 @@
     <expand macro="requirements"/>
     <required_files>
         <include path="flexynesis_plot.py" />
+        <include path="flexynesis_utils.py" />
+        <include path="convert.py" />
+        <include path="index_to_name.py" />
     </required_files>
     <command detect_errors="exit_code"><![CDATA[
         @CHECK_NON_COMMERCIAL_USE@
         mkdir -p input/test input/train output &&
         ln -s '$train_clin' input/train/clin.tabular &&
-        python $__tool_directory__/convert.py input/train/clin.tabular input/train/clin.csv &&
+        python '$__tool_directory__/convert.py' input/train/clin.tabular input/train/clin.csv &&
 
         ln -s '$test_clin' input/test/clin.tabular &&
-        python $__tool_directory__/convert.py input/test/clin.tabular input/test/clin.csv &&
+        python '$__tool_directory__/convert.py' input/test/clin.tabular input/test/clin.csv &&
 
         #if str($assay_main) != '':
             #set $name = str($assay_main.replace(" ", "_"))
@@ -46,54 +49,61 @@
             #if str($element.train_omics) != 'None' and str($element.test_omics) != 'None':
                 #if str($element.assay) != '':
                     #set $i = str($element.assay.replace(" ", "_"))
-                #end if
-                ln -s '${element.train_omics}' input/train/omics_${i}.tabular &&
-                python '$__tool_directory__/convert.py' input/train/omics_${i}.tabular input/train/omics_${i}.csv &&
+                    ln -s '${element.train_omics}' input/train/${i}.tabular &&
+                    python '$__tool_directory__/convert.py' input/train/${i}.tabular input/train/${i}.csv &&
 
-                ln -s '${element.test_omics}' input/test/omics_${i}.tabular &&
-                python '$__tool_directory__/convert.py' input/test/omics_${i}.tabular input/test/omics_${i}.csv &&
+                    ln -s '${element.test_omics}' input/test/${i}.tabular &&
+                    python '$__tool_directory__/convert.py' input/test/${i}.tabular input/test/${i}.csv &&
 
-                $data_names.append("omics_" + str($i))
-                #if str($training_type.model) == 'cm_train':
-                    #if str($element.layer) == 'input':
-                        $input_layers.append("omics_" + str($i))
-                    #else
-                        $output_layers.append("omics_" + str($i))
+                    $data_names.append(str($i))
+                    #if str($training_type.model) == 'cm_train':
+                        #if str($element.layer) == 'input':
+                            $input_layers.append(str($i))
+                        #else
+                            $output_layers.append(str($i))
+                        #end if
+                    #end if
+                #else
+                    ln -s '${element.train_omics}' input/train/omics_${i}.tabular &&
+                    python '$__tool_directory__/convert.py' input/train/omics_${i}.tabular input/train/omics_${i}.csv &&
+
+                    ln -s '${element.test_omics}' input/test/omics_${i}.tabular &&
+                    python '$__tool_directory__/convert.py' input/test/omics_${i}.tabular input/test/omics_${i}.csv &&
+
+                    $data_names.append("omics_" + str($i))
+                    #if str($training_type.model) == 'cm_train':
+                        #if str($element.layer) == 'input':
+                            $input_layers.append("omics_" + str($i))
+                        #else
+                            $output_layers.append("omics_" + str($i))
+                        #end if
                     #end if
                 #end if
             #end if
         #end for
-        ## set target variables
         #if str($training_type.model) == 's_train':
-            #if len($targets) > 0:
-                target_variables="" &&
-                #for $i, $element in enumerate($targets)
-                    target_name=`$__tool_directory__/index_to_name.py input/train/clin.csv $element.target_variables` &&
-                    #if $i == 0:
-                        target_variables="\$target_name" &&
-                    #else:
-                        target_variables="\$target_variables,\$target_name" &&
-                    #end if
-                #end for
-                echo "Target variables: \$target_variables" &&
+            ## set target variables
+            #if str($target_variables) != 'None':
+                targets=`'$__tool_directory__/index_to_name.py' input/train/clin.csv $target_variables` &&
+                echo "Target variables: \$targets" &&
             #end if
             ## set survival variables
             #if str($surv_event_var) != 'None' and str($surv_time_var) != 'None':
-                surv_event_var=`$__tool_directory__/index_to_name.py input/train/clin.csv $surv_event_var` &&
-                echo "Survival event variable: \$surv_event_var" &&
-                surv_time_var=`$__tool_directory__/index_to_name.py input/train/clin.csv $surv_time_var` &&
-                echo "Survival time variable: \$surv_time_var" &&
+                survival_event=`'$__tool_directory__/index_to_name.py' input/train/clin.csv $surv_event_var` &&
+                ## check if survival variable is numerical
+                echo "Survival event variable: \$survival_event" &&
+                python '$__tool_directory__/flexynesis_utils.py' --util validate_survival --clin input/train/clin.csv --clin_variable \$survival_event &&
+                survival_time=`$__tool_directory__/index_to_name.py input/train/clin.csv $surv_time_var` &&
+                echo "Survival time variable: \$survival_time" &&
             #end if
-            ## set target value for plots
-            #if str($surv_event_var) != 'None' and str($surv_time_var) != 'None' and len($targets) > 0:
-                target_value="\$target_variables,\$surv_event_var" &&
-                echo "Target value for plots: \$target_value" &&
-            #else if str($surv_event_var) == 'None' and str($surv_time_var) == 'None' and len($targets) > 0:
-                target_value="\$target_variables" &&
-                echo "Target value for plots: \$target_value" &&
-            #else if str($surv_event_var) != 'None' and str($surv_time_var) != 'None' and len($targets) == 0:
-                target_value="\$surv_event_var" &&
-                echo "Target value for plots: \$target_value" &&
+            ## set clinical variables for Cox model
+            #if str($plot.plot_cox_conditional.plot_cox) == 'yes':
+                #if str($plot.plot_cox_conditional.clinical_variables) != 'None':
+                    covars=`'$__tool_directory__/index_to_name.py' input/train/clin.csv $plot.plot_cox_conditional.clinical_variables` &&
+                    ## check if covariates is numerical
+                    echo "Clinical variables for Cox model: \$covars" &&
+                    python '$__tool_directory__/flexynesis_utils.py' --util validate_covariate --clin input/train/clin.csv --clin_variable \$covars &&
+                #end if
             #end if
         #end if
 
@@ -107,12 +117,12 @@
                 --string_node_name $string_node_name
             #end if
             #if str($training_type.model) == 's_train':
-                #if len($targets) > 0:
-                    --target_variables \$target_variables
+                #if str($target_variables) != 'None':
+                    --target_variables \$targets
                 #end if
                 #if str($surv_event_var) != 'None' and str($surv_time_var) != 'None':
-                    --surv_event_var \$surv_event_var
-                    --surv_time_var \$surv_time_var
+                    --surv_event_var \$survival_event
+                    --surv_time_var \$survival_time
                 #end if
             #end if
             #if str($training_type.model) == 'cm_train':
@@ -142,143 +152,151 @@
         && for file in output/*.csv; do
             if [ -f "\$file" ]; then
                 basename="\${file%.csv}";
-                python $__tool_directory__/convert.py "\$file" "\${basename}.tabular";
+                python '$__tool_directory__/convert.py' "\$file" "\${basename}.tabular";
                 echo "Converted \$file to \${basename}.tabular";
             fi
         done
-        #if str($plot.plot_embeddings_conditional.plot_embeddings) == 'yes':
-                && python $__tool_directory__/flexynesis_plot.py
-                    --plot_type dimred
-                    --embeddings output/job.embeddings_test.tabular
+        #if str($training_type.model) == 's_train':
+            #if str($plot.plot_embeddings_conditional.plot_embeddings) == 'yes':
+                    && python '$__tool_directory__/flexynesis_plot.py'
+                        --plot_type dimred
+                        --embeddings output/job.embeddings_test.tabular
+                        --labels output/job.predicted_labels.tabular
+                        --method $plot.plot_embeddings_conditional.method
+                        #if str($training_type.model) == 's_train':
+                            #if str($target_variables) != 'None':
+                                --target_value \$targets
+                            #end if
+                        #end if
+                        --output_dir plots
+                        --format $plot.plot_embeddings_conditional.format
+                        --dpi $plot.plot_embeddings_conditional.dpi
+            #end if
+            #if str($plot.plot_km_conditional.plot_km) == 'yes':
+                #if str($surv_event_var) == '' or str($surv_time_var) == '':
+                    && echo "Survival event and time variables must be specified for Kaplan-Meier plots."
+                    && exit 1
+                #else
+                    && python '$__tool_directory__/flexynesis_plot.py'
+                        --plot_type kaplan_meier
+                        --labels output/job.predicted_labels.tabular
+                        --survival_data input/test/clin.csv
+                        --surv_event_var \$survival_event
+                        --surv_time_var \$survival_time
+                        --output_dir plots
+                        --format $plot.plot_km_conditional.format
+                        --dpi $plot.plot_km_conditional.dpi
+                #end if
+            #end if
+            #if str($plot.plot_cox_conditional.plot_cox) == 'yes':
+                #if str($surv_event_var) == '' or str($surv_time_var) == '':
+                    && echo "Survival event and time variables must be specified for cox plots."
+                    && exit 1
+                #else
+                    && python '$__tool_directory__/flexynesis_plot.py'
+                        --plot_type cox
+                        #if str($feature_importance_method) == 'GradientShap':
+                            --important_features output/job.feature_importance.GradientShap.tabular
+                        #else if str($feature_importance_method) == 'IntegratedGradients':
+                            --important_features output/job.feature_importance.IntegratedGradients.tabular
+                        #else
+                            --important_features output/job.feature_importance.IntegratedGradients.tabular
+                        #end if
+                        --clinical_train input/train/clin.csv
+                        --clinical_test input/test/clin.csv
+                        #if str($plot.plot_cox_conditional.omics_name) != '':
+                            #set $omics_name = str($plot.plot_cox_conditional.omics_name.replace(" ", "_"))
+                            --omics_train input/train/${omics_name}.csv
+                            --omics_test input/test/${omics_name}.csv
+                            --layer ${omics_name}
+                        #else
+                            --omics_train input/train/${data_names}.csv
+                            --omics_test input/test/${data_names}.csv
+                            --layer ${data_names}
+                        #end if
+                        --surv_time_var \$survival_time
+                        --surv_event_var \$survival_event
+                        #if str($plot.plot_cox_conditional.clinical_variables) != 'None':
+                            --clinical_variables \$covars
+                        #end if
+                        --top_features $plot.plot_cox_conditional.top_features
+                        #if str($plot.plot_cox_conditional.crossval_conditional.crossval) == 'yes':
+                            --crossval
+                        #end if
+                        #if str($plot.plot_cox_conditional.crossval_conditional.crossval) == 'yes':
+                            --n_splits $plot.plot_cox_conditional.crossval_conditional.n_splits
+                        #end if
+                        --random_state 42
+                        --output_dir plots
+                        --format $plot.plot_cox_conditional.format
+                        --dpi $plot.plot_cox_conditional.dpi
+                #end if
+            #end if
+            #if str($plot.plot_scatter_conditional.plot_scatter) == 'yes':
+                && python '$__tool_directory__/flexynesis_plot.py'
+                    --plot_type scatter
                     --labels output/job.predicted_labels.tabular
-                    --method $plot.plot_embeddings_conditional.method
                     #if str($training_type.model) == 's_train':
-                        #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                            --target_value \$target_value
+                        #if str($target_variables) != 'None':
+                            --target_value \$targets
                         #end if
                     #end if
                     --output_dir plots
-                    --format $plot.plot_embeddings_conditional.format
-                    --dpi $plot.plot_embeddings_conditional.dpi
-        #end if
-        #if str($plot.plot_km_conditional.plot_km) == 'yes':
-            #if str($surv_event_var) == '' or str($surv_time_var) == '':
-                && echo "Survival event and time variables must be specified for Kaplan-Meier plots."
-                && exit 1
-            #else
-                && python $__tool_directory__/flexynesis_plot.py
-                    --plot_type kaplan_meier
-                    --labels output/job.predicted_labels.tabular
-                    --survival_data input/test/clin.csv
-                    --surv_event_var \$surv_event_var
-                    --surv_time_var \$surv_time_var
-                    --event_value $plot.plot_km_conditional.event_value
-                    --output_dir plots
-                    --format $plot.plot_km_conditional.format
-                    --dpi $plot.plot_km_conditional.dpi
+                    --format $plot.plot_scatter_conditional.format
+                    --dpi $plot.plot_scatter_conditional.dpi
             #end if
-        #end if
-        #if str($plot.plot_cox_conditional.plot_cox) == 'yes':
-            #if str($surv_event_var) == '' or str($surv_time_var) == '':
-                && echo "Survival event and time variables must be specified for cox plots."
-                && exit 1
-            #else
-                && python $__tool_directory__/flexynesis_plot.py
-                    --plot_type cox
-                    --model output/job.final_model.pth
-                    --clinical_train input/train/clin.csv
-                    --clinical_test input/test/clin.csv
-                    #if str($plot.plot_cox_conditional.omics_name) != '':
-                        #set $name = str($plot.plot_cox_conditional.omics_name.replace(" ", "_"))
-                        --omics_train input/train/${name}.csv
-                        --omics_test input/test/${name}.csv
-                    #else
-                        --omics_train input/train/main.csv
-                        --omics_test input/test/main.csv
+            #if str($plot.plot_concordance_conditional.plot_concordance) == 'yes':
+                && python '$__tool_directory__/flexynesis_plot.py'
+                    --plot_type concordance_heatmap
+                    --labels output/job.predicted_labels.tabular
+                    #if str($training_type.model) == 's_train':
+                        #if str($target_variables) != 'None':
+                            --target_value \$targets
+                        #end if
                     #end if
-                    --surv_time_var \$surv_time_var
-                    --surv_event_var \$surv_event_var
-                    #if str($plot.plot_cox_conditional.clinical_variables) != '':
-                        --clinical_variables $str($plot.plot_cox_conditional.clinical_variables)
-                    #end if
-                    --top_features $plot.plot_cox_conditional.top_features
-                    --event_value $plot.plot_cox_conditional.event_value
-                    #if str($plot.plot_cox_conditional.crossval_conditional.crossval) == 'yes':
-                        --crossval
-                    #end if
-                   #if str($plot.plot_cox_conditional.crossval_conditional.crossval) == 'yes':
-                        --n_splits $plot.plot_cox_conditional.crossval_conditional.n_splits
-                    #end if
-                    --random_state 42
                     --output_dir plots
-                    --format $plot.plot_cox_conditional.format
-                    --dpi $plot.plot_cox_conditional.dpi
+                    --format $plot.plot_concordance_conditional.format
+                    --dpi $plot.plot_concordance_conditional.dpi
             #end if
-        #end if
-        #if str($plot.plot_scatter_conditional.plot_scatter) == 'yes':
-            && python $__tool_directory__/flexynesis_plot.py
-                --plot_type scatter
-                --labels output/job.predicted_labels.tabular
-                #if str($training_type.model) == 's_train':
-                    #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                        --target_value \$target_value
-                    #end if
-                #end if
-                --output_dir plots
-                --format $plot.plot_scatter_conditional.format
-                --dpi $plot.plot_scatter_conditional.dpi
-        #end if
-        #if str($plot.plot_concordance_conditional.plot_concordance) == 'yes':
-            && python $__tool_directory__/flexynesis_plot.py
-                --plot_type concordance_heatmap
-                --labels output/job.predicted_labels.tabular
-                #if str($training_type.model) == 's_train':
-                    #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                        --target_value \$target_value
+            #if str($plot.plot_pr_curves_conditional.plot_pr_curves) == 'yes':
+                && python '$__tool_directory__/flexynesis_plot.py'
+                    --plot_type pr_curve
+                    --labels output/job.predicted_labels.tabular
+                    #if str($training_type.model) == 's_train':
+                        #if str($target_variables) != 'None':
+                            --target_value \$targets
+                        #end if
                     #end if
-                #end if
-                --output_dir plots
-                --format $plot.plot_concordance_conditional.format
-                --dpi $plot.plot_concordance_conditional.dpi
-        #end if
-        #if str($plot.plot_pr_curves_conditional.plot_pr_curves) == 'yes':
-            && python $__tool_directory__/flexynesis_plot.py
-                --plot_type pr_curve
-                --labels output/job.predicted_labels.tabular
-                #if str($training_type.model) == 's_train':
-                    #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                        --target_value \$target_value
+                    --output_dir plots
+                    --format $plot.plot_pr_curves_conditional.format
+                    --dpi $plot.plot_pr_curves_conditional.dpi
+            #end if
+            #if str($plot.plot_roc_curves_conditional.plot_roc_curves) == 'yes':
+                && python '$__tool_directory__/flexynesis_plot.py'
+                    --plot_type roc_curve
+                    --labels output/job.predicted_labels.tabular
+                    #if str($training_type.model) == 's_train':
+                        #if str($target_variables) != 'None':
+                            --target_value \$targets
+                        #end if
                     #end if
-                #end if
-                --output_dir plots
-                --format $plot.plot_pr_curves_conditional.format
-                --dpi $plot.plot_pr_curves_conditional.dpi
-        #end if
-        #if str($plot.plot_roc_curves_conditional.plot_roc_curves) == 'yes':
-            && python $__tool_directory__/flexynesis_plot.py
-                --plot_type roc_curve
-                --labels output/job.predicted_labels.tabular
-                #if str($training_type.model) == 's_train':
-                    #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                        --target_value \$target_value
+                    --output_dir plots
+                    --format $plot.plot_roc_curves_conditional.format
+                    --dpi $plot.plot_roc_curves_conditional.dpi
+            #end if
+            #if str($plot.plot_boxplot_conditional.plot_boxplot) == 'yes':
+                && python '$__tool_directory__/flexynesis_plot.py'
+                    --plot_type box_plot
+                    --labels output/job.predicted_labels.tabular
+                    #if str($training_type.model) == 's_train':
+                        #if str($target_variables) != 'None':
+                            --target_value \$targets
+                        #end if
                     #end if
-                #end if
-                --output_dir plots
-                --format $plot.plot_roc_curves_conditional.format
-                --dpi $plot.plot_roc_curves_conditional.dpi
-        #end if
-        #if str($plot.plot_boxplot_conditional.plot_boxplot) == 'yes':
-            && python $__tool_directory__/flexynesis_plot.py
-                --plot_type box_plot
-                --labels output/job.predicted_labels.tabular
-                #if str($training_type.model) == 's_train':
-                    #if len($targets) > 0 or (str($surv_event_var) != 'None' and str($surv_time_var) != 'None'):
-                        --target_value \$target_value
-                    #end if
-                #end if
-                --output_dir plots
-                --format $plot.plot_boxplot_conditional.format
-                --dpi $plot.plot_boxplot_conditional.dpi
+                    --output_dir plots
+                    --format $plot.plot_boxplot_conditional.format
+                    --dpi $plot.plot_boxplot_conditional.dpi
+            #end if
         #end if
     ]]></command>
     <inputs>
@@ -328,12 +346,119 @@
                     <when value="RandomSurvivalForest"/>
                     <when value="XGBoost"/>
                 </conditional>
-                <repeat name="targets" min="0" title="Target variables">
-                    <param argument="--target_variables" type="data_column" data_ref="train_clin" optional="false" label="Column name in the train clinical data to use for predictions, multiple targets are allowed"/>
-                </repeat>
+                <param argument="--target_variables" type="data_column" data_ref="train_clin" optional="true" multiple="true" label="Column name in the train clinical data to use for predictions, multiple targets are allowed"/>
                 <param argument="--surv_event_var" type="data_column" data_ref="train_clin" optional="true" label="Column name in the train clinical data to use as survival event"/>
                 <param argument="--surv_time_var" type="data_column" data_ref="train_clin" optional="true" label="Column name in the train clinical data to use as survival time"/>
                 <expand macro="advanced"/>
+                <section name="plot" title="Visualization">
+                    <conditional name="plot_embeddings_conditional">
+                        <param name="plot_embeddings" type="select" label="Generate embeddings plot?" help="Generate PCA or UMAP plot of the test dataset">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <param name="method" type="select" label="Transformation method">
+                                <option value="pca" selected="true">PCA</option>
+                                <option value="umap">UMAP</option>
+                            </param>
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_km_conditional">
+                        <param name="plot_km" type="select" label="Generate kaplan meier curves plot?" help="Generate KM curves plot of risk subtype based on predicted events probabilities">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_cox_conditional">
+                        <param name="plot_cox" type="select" label="Generate hazard ratio plot?" help="Performs Cox regression on data based on top features and selected clinical covariates.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <param name="omics_name" type="text" label="Omics layer to use for cox input" optional="true" help="If not specified, the first omics layer will be used.">
+                                <expand macro="sanitizer_printable"/>
+                            </param>
+                            <param argument="--clinical_variables" type="data_column" data_ref="train_clin" optional="true" multiple="true" label="Columns in the train clinical data to use as clinical covariates and include in the Cox model (e.g SEX, AGE, ...), multiple covariates are allowed"/>
+                            <param argument="--top_features" type="integer" min="1" value="20" label="Number of top important features to include in Cox model"/>
+                            <conditional name="crossval_conditional">
+                                <param name="crossval" type="select" label="Performs K-fold cross-validation?">
+                                    <option value="yes">Yes</option>
+                                    <option value="no" selected="true">No</option>
+                                </param>
+                                <when value="yes">
+                                    <param name="n_splits" type="integer" min="2" value="5" label="Number of folds for cross-validation"/>
+                                </when>
+                                <when value="no"/>
+                            </conditional>
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_scatter_conditional">
+                        <param name="plot_scatter" type="select" label="Generate scatter plot?" help="Generates a scatter plot of numeric true and predicted labels for all available targets.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_concordance_conditional">
+                        <param name="plot_concordance" type="select" label="Generate concordance heatmap plot?" help="Generates a plot showing the concordance between true and predicted labels for all targets.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_pr_curves_conditional">
+                        <param name="plot_pr_curves" type="select" label="Generate precision-recall curves plot?" help="Generates precision-recall curves for all available targets.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_roc_curves_conditional">
+                        <param name="plot_roc_curves" type="select" label="Generate ROC curves plot?" help="Generates ROC curves for all available targets.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                    <conditional name="plot_boxplot_conditional">
+                        <param name="plot_boxplot" type="select" label="Generate boxplot?" help="Generates a boxplot for all available targets.">
+                            <option value="yes">Yes</option>
+                            <option value="no" selected="true">No</option>
+                        </param>
+                        <when value="no">
+                        </when>
+                        <when value="yes">
+                            <expand macro="plots_common_param"/>
+                        </when>
+                    </conditional>
+                </section>
             </when>
             <when value="us_train">
                 <expand macro="main_inputs"/>
@@ -364,131 +489,14 @@
                 <expand macro="advanced"/>
             </when>
         </conditional>
-        <section name="plot" title="Visualization">
-            <conditional name="plot_embeddings_conditional">
-                <param name="plot_embeddings" type="select" label="Generate embeddings plot?" help="Generate PCA or UMAP plot of the test dataset">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <param name="method" type="select" label="Transformation method">
-                        <option value="pca" selected="true">PCA</option>
-                        <option value="umap">UMAP</option>
-                    </param>
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_km_conditional">
-                <param name="plot_km" type="select" label="Generate kaplan meier curves plot?" help="Generate KM curves plot of risk subtype based on predicted events probabilities">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <param name="event_value" type="text" label="Event value" optional="false" help="The value in the event column that indicates an event (e.g. death) has occurred.">
-                        <expand macro="sanitizer_printable"/>
-                    </param>
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_cox_conditional">
-                <param name="plot_cox" type="select" label="Generate hazard ratio plot?" help="Performs Cox regression on data based on top features and selected clinical covariates.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <param name="omics_name" type="text" label="Omics layer to use for cox input" optional="false" help="If not specified, the first omics layer will be used.">
-                        <expand macro="sanitizer_printable"/>
-                    </param>
-                    <param name="clinical_variables" type="text" label="Clinical covariates" optional="true" help="Comma-separated list of clinical covariates to include in the Cox model (e.g SEX, AGE, ...).">
-                        <expand macro="sanitizer_printable"/>
-                    </param>
-                    <param argument="--top_features" type="integer" min="1" value="20" label="Number of top important features to include in Cox model"/>
-                    <param name="event_value" type="text" label="Event value" optional="false" help="The value in the event column that indicates an event (e.g. death) has occurred.">
-                        <expand macro="sanitizer_printable"/>
-                    </param>
-                    <conditional name="crossval_conditional">
-                        <param name="crossval" type="select" label="Performs K-fold cross-validation?">
-                            <option value="yes">Yes</option>
-                            <option value="no" selected="true">No</option>
-                        </param>
-                        <when value="yes">
-                            <param name="n_splits" type="integer" min="2" value="5" label="Number of folds for cross-validation"/>
-                        </when>
-                        <when value="no"/>
-                    </conditional>
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_scatter_conditional">
-                <param name="plot_scatter" type="select" label="Generate scatter plot?" help="Generates a scatter plot of numeric true and predicted labels for all available targets.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_concordance_conditional">
-                <param name="plot_concordance" type="select" label="Generate concordance heatmap plot?" help="Generates a plot showing the concordance between true and predicted labels for all targets.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_pr_curves_conditional">
-                <param name="plot_pr_curves" type="select" label="Generate precision-recall curves plot?" help="Generates precision-recall curves for all available targets.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_roc_curves_conditional">
-                <param name="plot_roc_curves" type="select" label="Generate ROC curves plot?" help="Generates ROC curves for all available targets.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-            <conditional name="plot_boxplot_conditional">
-                <param name="plot_boxplot" type="select" label="Generate boxplot?" help="Generates a boxplot for all available targets.">
-                    <option value="yes">Yes</option>
-                    <option value="no" selected="true">No</option>
-                </param>
-                <when value="no">
-                </when>
-                <when value="yes">
-                    <expand macro="plots_common_param"/>
-                </when>
-            </conditional>
-        </section>
     </inputs>
     <outputs>
         <collection name="results" type="list" label="${tool.name} on ${on_string}: results">
             <discover_datasets pattern="(?P&lt;name&gt;.+)\.tabular$" format="tabular" directory="output"/>
         </collection>
-        <collection name="plots" type="list" label="${tool.name} on ${on_string}: embeddings plots">
+        <collection name="plots" type="list" label="${tool.name} on ${on_string}: plots">
             <discover_datasets pattern="__name_and_ext__" directory="plots"/>
-            <filter>plot['plot_embeddings_conditional']['plot_embeddings'] == 'yes' or plot['plot_km_conditional']['plot_km'] == 'yes' or plot['plot_cox_conditional']['plot_cox'] == 'yes' or plot['plot_scatter_conditional']['plot_scatter'] == 'yes' or plot['plot_concordance_conditional']['plot_concordance'] == 'yes' or plot['plot_pr_curves_conditional']['plot_pr_curves'] == 'yes' or plot['plot_roc_curves_conditional']['plot_roc_curves'] == 'yes' or plot['plot_boxplot_conditional']['plot_boxplot'] == 'yes'</filter>
+            <filter>training_type['model'] == 's_train' and (training_type['plot']['plot_embeddings_conditional']['plot_embeddings'] == 'yes' or training_type['plot']['plot_km_conditional']['plot_km'] == 'yes' or training_type['plot']['plot_cox_conditional']['plot_cox'] == 'yes' or training_type['plot']['plot_scatter_conditional']['plot_scatter'] == 'yes' or training_type['plot']['plot_concordance_conditional']['plot_concordance'] == 'yes' or training_type['plot']['plot_pr_curves_conditional']['plot_pr_curves'] == 'yes' or training_type['plot']['plot_roc_curves_conditional']['plot_roc_curves'] == 'yes' or training_type['plot']['plot_boxplot_conditional']['plot_boxplot'] == 'yes')</filter>
         </collection>
     </outputs>
     <tests>
@@ -510,9 +518,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -547,7 +553,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -580,9 +586,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -650,9 +654,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="4"/>
-                </repeat>
+                <param name="target_variables" value="4"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -687,7 +689,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -743,7 +745,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -788,17 +790,17 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.test_decoded.omics_foo">
+                <element name="job.test_decoded.foo">
                     <assert_contents>
                         <has_n_lines n="23"/>
                     </assert_contents>
                 </element>
-                <element name="job.train_decoded.omics_foo">
+                <element name="job.train_decoded.foo">
                     <assert_contents>
                         <has_n_lines n="23"/>
                     </assert_contents>
@@ -826,9 +828,7 @@
                     <param name="string_organism" value="9606"/>
                     <param name="string_node_name" value="gene_name"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -863,7 +863,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -919,7 +919,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_f_oo">
+                <element name="job.feature_logs.f_oo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -944,9 +944,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="XGBoost"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -979,9 +977,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                     <param name="feature_importance_method" value="IntegratedGradients"/>
@@ -1010,7 +1006,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -1048,21 +1044,19 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
+                <param name="target_variables" value="3"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
+                <section name="plot">
+                    <conditional name="plot_embeddings_conditional">
+                        <param name="plot_embeddings" value="yes"/>
+                        <param name="method" value="pca"/>
+                        <param name="format" value="jpg"/>
+                        <param name="dpi" value="300"/>
+                    </conditional>
+                </section>
             </conditional>
-            <section name="plot">
-                <conditional name="plot_embeddings_conditional">
-                    <param name="plot_embeddings" value="yes"/>
-                    <param name="method" value="pca"/>
-                    <param name="format" value="jpg"/>
-                    <param name="dpi" value="300"/>
-                </conditional>
-            </section>
             <output_collection name="results" type="list">
                 <element name="job.embeddings_test">
                     <assert_contents>
@@ -1093,7 +1087,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
@@ -1137,7 +1131,6 @@
                 <section name="plot">
                     <conditional name="plot_km_conditional">
                         <param name="plot_km" value="yes"/>
-                        <param name="event_value" value="1:DECEASED"/>
                         <param name="format" value="jpg"/>
                         <param name="dpi" value="300"/>
                     </conditional>
@@ -1161,16 +1154,15 @@
                     <conditional name="plot_cox_conditional">
                         <param name="plot_cox" value="yes"/>
                         <param name="omics_name" value="bar"/>
-                        <param name="clinical_variables" value="Crizotinib,   Erlotinib"/>
+                        <param name="clinical_variables" value="2,3"/>
                         <param name="top_features" value="10"/>
-                        <param name="event_value" value="1:DECEASED"/>
                         <param name="format" value="jpg"/>
                         <param name="dpi" value="300"/>
                     </conditional>
                 </section>
             </expand>
             <output_collection name="plots" type="list">
-                <element name="job.final_model_cox_hazard_ratios">
+                <element name="job.feature_importance.IntegratedGradients_cox_hazard_ratios">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="972,737" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1216,24 +1208,17 @@
             <output_collection name="plots" type="list">
                 <element name="job.predicted_labels_concordance_Erlotinib">
                     <assert_contents>
-                        <has_image_center_of_mass center_of_mass="1547,1620" eps="30"/>
+                        <has_image_center_of_mass center_of_mass="1547,1590" eps="30"/>
                         <has_image_channels channels="3"/>
-                        <has_image_height height="3060" delta="50"/>
+                        <has_image_height height="3000" delta="50"/>
                         <has_image_width width="3192" delta="50"/>
                     </assert_contents>
                 </element>
-                <element name="job.predicted_labels_concordance_OS_STATUS">
-                    <assert_contents>
-                        <has_image_channels channels="3"/>
-                        <has_image_height height="2558" delta="50"/>
-                        <has_image_width width="2794" delta="50"/>
-                    </assert_contents>
-                </element>
             </output_collection>
         </test>
         <!-- test 15: pr curve plot -->
         <test expect_num_outputs="2">
-            <expand macro="common_test">
+            <expand macro="common_test_class">
                 <section name="plot">
                     <conditional name="plot_pr_curves_conditional">
                         <param name="plot_pr_curves" value="yes"/>
@@ -1243,7 +1228,7 @@
                 </section>
             </expand>
             <output_collection name="plots" type="list">
-                <element name="job.predicted_labels_pr_curves_OS_STATUS">
+                <element name="job.predicted_labels_pr_curves_class">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="975,732" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1255,7 +1240,7 @@
         </test>
         <!-- test 16: roc curve plot -->
         <test expect_num_outputs="2">
-            <expand macro="common_test">
+            <expand macro="common_test_class">
                 <section name="plot">
                     <conditional name="plot_roc_curves_conditional">
                         <param name="plot_roc_curves" value="yes"/>
@@ -1265,7 +1250,7 @@
                 </section>
             </expand>
             <output_collection name="plots" type="list">
-                <element name="job.predicted_labels_roc_curves_OS_STATUS">
+                <element name="job.predicted_labels_roc_curves_class">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="970,730" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1277,7 +1262,7 @@
         </test>
         <!-- test 17: boxplot plot -->
         <test expect_num_outputs="2">
-            <expand macro="common_test">
+            <expand macro="common_test_class">
             <section name="plot">
                 <conditional name="plot_boxplot_conditional">
                     <param name="plot_boxplot" value="yes"/>
@@ -1287,7 +1272,7 @@
             </section>
             </expand>
             <output_collection name="plots" type="list">
-                <element name="job.predicted_labels_box_plot_OS_STATUS_0_LIVING">
+                <element name="job.predicted_labels_box_plot_class_buz">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="1485,882" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1295,7 +1280,7 @@
                         <has_image_width width="2967" delta="20"/>
                     </assert_contents>
                 </element>
-                <element name="job.predicted_labels_box_plot_OS_STATUS_1_DECEASED">
+                <element name="job.predicted_labels_box_plot_class_qux">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="1485,882" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1312,9 +1297,8 @@
                     <conditional name="plot_cox_conditional">
                         <param name="plot_cox" value="yes"/>
                         <param name="omics_name" value="bar"/>
-                        <param name="clinical_variables" value="Crizotinib,   Erlotinib"/>
+                        <param name="clinical_variables" value="2,3"/>
                         <param name="top_features" value="10"/>
-                        <param name="event_value" value="1:DECEASED"/>
                         <conditional name="crossval_conditional">
                             <param name="crossval" value="yes"/>
                             <param name="n_splits" value="5"/>
@@ -1325,7 +1309,7 @@
                 </section>
             </expand>
             <output_collection name="plots" type="list">
-                <element name="job.final_model_cox_hazard_ratios">
+                <element name="job.feature_importance.IntegratedGradients_cox_hazard_ratios">
                     <assert_contents>
                         <has_image_center_of_mass center_of_mass="972,737" eps="20"/>
                         <has_image_channels channels="3"/>
@@ -1353,12 +1337,7 @@
                 <conditional name="model_class">
                     <param name="model_class" value="DirectPred"/>
                 </conditional>
-                <repeat name="targets">
-                    <param name="target_variables" value="3"/>
-                </repeat>
-                <repeat name="targets">
-                    <param name="target_variables" value="2"/>
-                </repeat>
+                <param name="target_variables" value="3,2"/>
                 <section name="advanced">
                     <param name="hpo_iter" value="1"/>
                 </section>
@@ -1400,7 +1379,7 @@
                         <has_n_lines n="25"/>
                     </assert_contents>
                 </element>
-                <element name="job.feature_logs.omics_foo">
+                <element name="job.feature_logs.foo">
                     <assert_contents>
                         <has_n_lines n="25"/>
                     </assert_contents>
--- a/flexynesis_plot.py	Fri Jul 04 14:57:40 2025 +0000
+++ b/flexynesis_plot.py	Wed Jul 23 07:49:41 2025 +0000
@@ -11,10 +11,8 @@
 import numpy as np
 import pandas as pd
 import seaborn as sns
-import torch
 from flexynesis import (
     build_cox_model,
-    get_important_features,
     plot_dim_reduced,
     plot_hazard_ratios,
     plot_kaplan_meier_curves,
@@ -55,35 +53,13 @@
         elif file_ext in ['.tsv', '.txt', '.tab', '.tabular']:
             df = pd.read_csv(labels_input, sep='\t')
 
-        # Check if this is the specific format with sample_id, known_label, predicted_label
-        required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
-        if all(col in df.columns for col in required_cols):
-            return df
-        else:
-            raise ValueError(f"Labels file {labels_input} does not contain required columns: {required_cols}")
+        print(f"available columns: {df.columns.tolist()}")
+        return df
 
     except Exception as e:
         raise ValueError(f"Error loading labels from {labels_input}: {e}") from e
 
 
-def load_survival_data(survival_path):
-    """Load survival data from a file. First column should be sample_id"""
-    try:
-        # Determine file extension
-        file_ext = Path(survival_path).suffix.lower()
-
-        if file_ext == '.csv':
-            df = pd.read_csv(survival_path, index_col=0)
-        elif file_ext in ['.tsv', '.txt', '.tab', '.tabular']:
-            df = pd.read_csv(survival_path, sep='\t', index_col=0)
-        else:
-            raise ValueError(f"Unsupported file extension: {file_ext}")
-        return df
-
-    except Exception as e:
-        raise ValueError(f"Error loading survival data from {survival_path}: {e}") from e
-
-
 def load_omics(omics_path):
     """Load omics data from a file. First column should be features"""
     try:
@@ -102,19 +78,17 @@
         raise ValueError(f"Error loading omics data from {omics_path}: {e}") from e
 
 
-def load_model(model_path):
-    """Load flexynesis model from pickle file"""
-    try:
-        with open(model_path, 'rb') as f:
-            model = torch.load(f, weights_only=False)
-        return model
-    except Exception as e:
-        raise ValueError(f"Error loading model from {model_path}: {e}") from e
+def match_samples_to_embeddings(sample_names, labels):
+    """Filter label data to match sample names in the embeddings"""
+    # Create a DataFrame from sample_names to preserve order
+    sample_df = pd.DataFrame({'sample_names': sample_names})
 
+    # left_join
+    first_column = labels.columns[0]
+    df_matched = sample_df.merge(labels, left_on='sample_names', right_on=first_column, how='left')
 
-def match_samples_to_embeddings(sample_names, label_data):
-    """Filter label data to match sample names in the embeddings"""
-    df_matched = label_data[label_data['sample_id'].isin(sample_names)]
+    # remove sample_names to keep the initial structure
+    df_matched = df_matched.drop('sample_names', axis=1)
     return df_matched
 
 
@@ -214,124 +188,149 @@
 def generate_dimred_plots(embeddings, matched_labels, args, output_dir, output_name_base):
     """Generate dimensionality reduction plots"""
 
-    # Parse target values from comma-separated string
-    if args.target_value:
-        target_values = [val.strip() for val in args.target_value.split(',')]
-    else:
-        # If no target values specified, use all unique variables
-        target_values = matched_labels['variable'].unique().tolist()
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in matched_labels.columns for col in required_cols)
+
+    if not args.color:
+        if is_flexynesis_format:
+            print("Detected flexynesis labels format")
+            print(f"Generating {args.method.upper()} plots for known and predicted labels...")
+        else:
+            print("Labels are not in flexynesis format (Custom labels), please specify a color variable with --color")
+
+        # Parse target values from comma-separated string
+        if args.target_value:
+            target_values = [val.strip() for val in args.target_value.split(',')]
+        else:
+            # If no target values specified, use all unique variables
+            target_values = matched_labels['variable'].unique().tolist()
+
+        print(f"Generating {args.method.upper()} plots for {len(target_values)} target variable(s): {', '.join(target_values)}")
 
-    print(f"Generating {args.method.upper()} plots for {len(target_values)} target variable(s): {', '.join(target_values)}")
+        # Check variables
+        available_vars = matched_labels['variable'].unique()
+        missing_vars = [var for var in target_values if var not in available_vars]
+
+        if missing_vars:
+            print(f"Warning: The following target variables were not found in the data: {', '.join(missing_vars)}")
+            print(f"Available variables: {', '.join(available_vars)}")
+
+        # Filter to only process available variables
+        valid_vars = [var for var in target_values if var in available_vars]
 
-    # Check variables
-    available_vars = matched_labels['variable'].unique()
-    missing_vars = [var for var in target_values if var not in available_vars]
+        if not valid_vars:
+            raise ValueError(f"None of the specified target variables were found in the data. Available: {', '.join(available_vars)}")
+
+        # Generate plots for each valid target variable
+        for var in valid_vars:
+            print(f"\nPlotting variable: {var}")
 
-    if missing_vars:
-        print(f"Warning: The following target variables were not found in the data: {', '.join(missing_vars)}")
-        print(f"Available variables: {', '.join(available_vars)}")
+            # Filter matched labels for current variable
+            var_labels = matched_labels[matched_labels['variable'] == var].copy()
+            var_labels = var_labels.drop_duplicates(subset='sample_id')
+
+            if var_labels.empty:
+                print(f"Warning: No data found for variable '{var}', skipping...")
+                continue
 
-    # Filter to only process available variables
-    valid_vars = [var for var in target_values if var in available_vars]
+            # Auto-detect color type
+            known_color_type = detect_color_type(var_labels['known_label'])
+            predicted_color_type = detect_color_type(var_labels['predicted_label'])
+
+            print(f"  Auto-detected color types - Known: {known_color_type}, Predicted: {predicted_color_type}")
 
-    if not valid_vars:
-        raise ValueError(f"None of the specified target variables were found in the data. Available: {', '.join(available_vars)}")
+            try:
+                # Plot 1: Known labels
+                print(f"  Creating known labels plot for {var}...")
+                fig_known = plot_dim_reduced(
+                    matrix=embeddings,
+                    labels=var_labels['known_label'],
+                    method=args.method,
+                    color_type=known_color_type
+                )
+
+                output_path_known = output_dir / f"{output_name_base}_{var}_known.{args.format}"
+                print(f"  Saving known labels plot to: {output_path_known.name}")
+                fig_known.save(output_path_known, dpi=args.dpi, bbox_inches='tight')
 
-    # Generate plots for each valid target variable
-    for var in valid_vars:
-        print(f"\nPlotting variable: {var}")
+                # Plot 2: Predicted labels
+                print(f"  Creating predicted labels plot for {var}...")
+                fig_predicted = plot_dim_reduced(
+                    matrix=embeddings,
+                    labels=var_labels['predicted_label'],
+                    method=args.method,
+                    color_type=predicted_color_type
+                )
+
+                output_path_predicted = output_dir / f"{output_name_base}_{var}_predicted.{args.format}"
+                print(f"  Saving predicted labels plot to: {output_path_predicted.name}")
+                fig_predicted.save(output_path_predicted, dpi=args.dpi, bbox_inches='tight')
 
-        # Filter matched labels for current variable
-        var_labels = matched_labels[matched_labels['variable'] == var].copy()
-        var_labels = var_labels.drop_duplicates(subset='sample_id')
+                print(f"  ✓ Successfully created plots for variable '{var}'")
+
+            except Exception as e:
+                print(f"  ✗ Error creating plots for variable '{var}': {e}")
+                continue
 
-        if var_labels.empty:
-            print(f"Warning: No data found for variable '{var}', skipping...")
-            continue
+        print(f"\nDimensionality reduction plots completed for {len(valid_vars)} variable(s)!")
+
+    else:
+        # check if the color variable exists in matched_labels
+        if args.color not in matched_labels.columns:
+            raise ValueError(f"Color variable '{args.color}' not found in matched labels. Available columns: {matched_labels.columns.tolist()}")
 
         # Auto-detect color type
-        known_color_type = detect_color_type(var_labels['known_label'])
-        predicted_color_type = detect_color_type(var_labels['predicted_label'])
-
-        print(f"  Auto-detected color types - Known: {known_color_type}, Predicted: {predicted_color_type}")
+        color_type = detect_color_type(matched_labels[args.color])
 
-        try:
-            # Plot 1: Known labels
-            print(f"  Creating known labels plot for {var}...")
-            fig_known = plot_dim_reduced(
-                matrix=embeddings,
-                labels=var_labels['known_label'],
-                method=args.method,
-                color_type=known_color_type
-            )
-
-            output_path_known = output_dir / f"{output_name_base}_{var}_known.{args.format}"
-            print(f"  Saving known labels plot to: {output_path_known.name}")
-            fig_known.save(output_path_known, dpi=args.dpi, bbox_inches='tight')
+        print(f"  Auto-detected color type: {color_type}")
 
-            # Plot 2: Predicted labels
-            print(f"  Creating predicted labels plot for {var}...")
-            fig_predicted = plot_dim_reduced(
-                matrix=embeddings,
-                labels=var_labels['predicted_label'],
-                method=args.method,
-                color_type=predicted_color_type
-            )
+        # Plot: Specified color column
+        print(f"  Creating plot for {args.color}...")
+        fig = plot_dim_reduced(
+            matrix=embeddings,
+            labels=matched_labels[args.color],
+            method=args.method,
+            color_type=color_type
+        )
 
-            output_path_predicted = output_dir / f"{output_name_base}_{var}_predicted.{args.format}"
-            print(f"  Saving predicted labels plot to: {output_path_predicted.name}")
-            fig_predicted.save(output_path_predicted, dpi=args.dpi, bbox_inches='tight')
-
-            print(f"  ✓ Successfully created plots for variable '{var}'")
+        output_path = output_dir / f"{output_name_base}_{args.color}.{args.format}"
+        print(f"  Saving plot to: {output_path.name}")
+        fig.save(output_path, dpi=args.dpi, bbox_inches='tight')
 
-        except Exception as e:
-            print(f"  ✗ Error creating plots for variable '{var}': {e}")
-            continue
-
-    print(f"\nDimensionality reduction plots completed for {len(valid_vars)} variable(s)!")
+        print(f"  ✓ Successfully created plot for variable '{args.color}'")
 
 
-def generate_km_plots(survival_data, label_data, args, output_dir, output_name_base):
+def generate_km_plots(survival_data, labels, args, output_dir, output_name_base):
     """Generate Kaplan-Meier plots"""
+
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if not is_flexynesis_format:
+        raise ValueError(f"Labels are not in flexynesis format (Custom labels). Please provide a valid label file with the required columns, {required_cols}.")
+
     print("Generating Kaplan-Meier curves of risk subtypes...")
 
-    # Reset index and rename the index column to sample_id
-    survival_data = survival_data.reset_index()
     if survival_data.columns[0] != 'sample_id':
         survival_data = survival_data.rename(columns={survival_data.columns[0]: 'sample_id'})
 
-    # Convert survival event column to binary (0/1) based on event_value
     # Check if the event column exists
     if args.surv_event_var not in survival_data.columns:
         raise ValueError(f"Column '{args.surv_event_var}' not found in survival data")
 
-    # Convert to string for comparison to handle mixed types
-    survival_data[args.surv_event_var] = survival_data[args.surv_event_var].astype(str)
-    event_value_str = str(args.event_value)
-
-    # Create binary event column (1 if matches event_value, 0 otherwise)
-    survival_data[f'{args.surv_event_var}_binary'] = (
-        survival_data[args.surv_event_var] == event_value_str
-    ).astype(int)
-
-    # Filter for survival category and class_label == '1:DECEASED'
-    label_data['class_label'] = label_data['class_label'].astype(str)
-
-    label_data = label_data[(label_data['variable'] == args.surv_event_var) & (label_data['class_label'] == event_value_str)]
-
-    # check survival data
-    for col in [args.surv_time_var, args.surv_event_var]:
-        if col not in survival_data.columns:
-            raise ValueError(f"Column '{col}' not found in survival data")
+    labels = labels[(labels['variable'] == args.surv_event_var)]
 
     # Merge survival data with labels
-    df_deceased = pd.merge(survival_data, label_data, on='sample_id', how='inner')
+    df_deceased = pd.merge(survival_data, labels, on='sample_id', how='inner')
+    df_deceased = df_deceased.dropna(subset=[args.surv_time_var, args.surv_event_var])
 
     if df_deceased.empty:
         raise ValueError("No matching samples found after merging survival and label data.")
 
     # Get risk scores
-    risk_scores = df_deceased['probability'].values
+    risk_scores = df_deceased['predicted_label'].values
 
     # Compute groups (e.g., median split)
     quantiles = np.quantile(risk_scores, [0.5])
@@ -340,7 +339,7 @@
 
     fig_known = plot_kaplan_meier_curves(
         durations=df_deceased[args.surv_time_var],
-        events=df_deceased[f'{args.surv_event_var}_binary'],
+        events=df_deceased[args.surv_event_var],
         categorical_variable=group_labels
     )
 
@@ -351,12 +350,21 @@
     print("Kaplan-Meier plot saved successfully!")
 
 
-def generate_cox_plots(model, clinical_train, clinical_test, omics_train, omics_test, args, output_dir, output_name_base):
+def generate_cox_plots(important_features, clinical_train, clinical_test, omics_train, omics_test, args, output_dir, output_name_base):
     """Generate Cox proportional hazards plots"""
     print("Generating Cox proportional hazards analysis...")
 
+    # Check if this is the specific format with target_variable, importance
+    required_cols = ['target_variable', 'layer', 'importance']
+    is_flexynesis_format = all(col in important_features.columns for col in required_cols)
+
+    if not is_flexynesis_format:
+        raise ValueError(f"Labels are not in flexynesis format (Custom labels). Please provide a valid important_features file with the required columns, {required_cols}.")
+
     # Parse clinical variables
-    clinical_vars = [var.strip() for var in args.clinical_variables.split(',')]
+    clinical_vars = []
+    if args.clinical_variables:
+        clinical_vars = [var.strip() for var in args.clinical_variables.split(',')]
 
     # Validate that survival variables are included
     required_vars = [args.surv_time_var, args.surv_event_var]
@@ -382,10 +390,22 @@
     # Get top survival markers
     print(f"Extracting top {args.top_features} important features for {args.surv_event_var}...")
     try:
-        imp = get_important_features(model,
-                                     var=args.surv_event_var,
-                                     top=args.top_features
-                                     )['name'].unique().tolist()
+        print(f"Loading {args.top_features} important features from: {args.important_features}")
+        imp_features = load_labels(args.important_features)
+        imp_features = imp_features[imp_features['target_variable'] == args.surv_event_var]
+        if args.layer not in imp_features['layer'].unique():
+            print(f"Available class labels: {imp_features['layer'].unique()}")
+            raise ValueError(f"Class label '{args.layer}' not found in important features data: {args.important_features}")
+        imp_features = imp_features[imp_features['layer'] == args.layer]
+        if imp_features.empty:
+            raise ValueError(f"No important features found for target variable '{args.surv_event_var}' in {args.important_features}")
+        imp_features = imp_features.sort_values(by='importance', ascending=False)
+
+        if len(imp_features) < args.top_features:
+            raise ValueError(f"Requested top {args.top_features} features, but only {len(imp_features)} available in {args.important_features}")
+
+        imp = imp_features['name'].unique().tolist()[0:args.top_features]
+
         print(f"Top features: {', '.join(imp)}")
     except Exception as e:
         raise ValueError(f"Error getting important features: {e}")
@@ -418,15 +438,6 @@
     if df.empty:
         raise ValueError("No samples remain after filtering for survival data")
 
-    # Convert survival event column to binary (0/1) based on event_value
-    # Convert to string for comparison to handle mixed types
-    df[args.surv_event_var] = df[args.surv_event_var].astype(str)
-    event_value_str = str(args.event_value)
-
-    df[f'{args.surv_event_var}'] = (
-        df[args.surv_event_var] == event_value_str
-    ).astype(int)
-
     # Build Cox model
     print(f"Building Cox model with time variable: {args.surv_time_var}, event variable: {args.surv_event_var}")
     try:
@@ -459,124 +470,190 @@
     """Generate scatter plot of known vs predicted labels"""
     print("Generating scatter plots of known vs predicted labels...")
 
-    # Parse target values from comma-separated string
-    if args.target_value:
-        target_values = [val.strip() for val in args.target_value.split(',')]
-    else:
-        # If no target values specified, use all unique variables
-        target_values = labels['variable'].unique().tolist()
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if is_flexynesis_format:
+        # Parse target values from comma-separated string
+        if args.target_value:
+            target_values = [val.strip() for val in args.target_value.split(',')]
+        else:
+            # If no target values specified, use all unique variables
+            target_values = labels['variable'].unique().tolist()
+
+        print(f"Processing target values: {target_values}")
 
-    print(f"Processing target values: {target_values}")
+        successful_plots = 0
+        skipped_plots = 0
+
+        for target_value in target_values:
+            print(f"\nProcessing target value: '{target_value}'")
+
+            # Filter labels for the current target value
+            target_labels = labels[labels['variable'] == target_value]
+
+            if target_labels.empty:
+                print(f"  Warning: No data found for target value '{target_value}' - skipping")
+                skipped_plots += 1
+                continue
+
+            # Check if labels are numeric and convert
+            true_values = pd.to_numeric(target_labels['known_label'], errors='coerce')
+            predicted_values = pd.to_numeric(target_labels['predicted_label'], errors='coerce')
 
-    successful_plots = 0
-    skipped_plots = 0
+            if true_values.isna().all() or predicted_values.isna().all():
+                print(f"No valid numeric values found for known or predicted labels in '{target_value}'")
+                skipped_plots += 1
+                continue
+
+            try:
+                print(f"  Generating scatter plot for '{target_value}'...")
+                fig = plot_scatter(true_values, predicted_values)
 
-    for target_value in target_values:
-        print(f"\nProcessing target value: '{target_value}'")
+                # Create output filename with target value
+                safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
+                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
+
+                output_path = output_dir / output_filename
+                print(f"  Saving scatter plot to: {output_path.absolute()}")
+                fig.save(output_path, dpi=args.dpi, bbox_inches='tight')
+
+                successful_plots += 1
+                print(f"  Scatter plot for '{target_value}' generated successfully!")
 
-        # Filter labels for the current target value
-        target_labels = labels[labels['variable'] == target_value]
+            except Exception as e:
+                print(f"  Error generating plot for '{target_value}': {str(e)}")
+                skipped_plots += 1
+
+        # Summary
+        print("  Summary:")
+        print(f"  Successfully generated: {successful_plots} plots")
+        print(f"  Skipped: {skipped_plots} plots")
 
-        if target_labels.empty:
-            print(f"  Warning: No data found for target value '{target_value}' - skipping")
-            skipped_plots += 1
-            continue
+        if successful_plots == 0:
+            raise ValueError("No scatter plots could be generated. Check your data and target values.")
+
+        print("Scatter plot generation completed!")
+
+    if not is_flexynesis_format:
+        print("Labels are not in flexynesis format (Custom labels)")
+
+        if not args.true_label or not args.predicted_label:
+            raise ValueError("For custom labels, please specify --true_label and --predicted_label arguments.")
 
         # Check if labels are numeric and convert
-        true_values = pd.to_numeric(target_labels['known_label'], errors='coerce')
-        predicted_values = pd.to_numeric(target_labels['predicted_label'], errors='coerce')
+        true_values = pd.to_numeric(labels[args.true_label], errors='coerce')
+        predicted_values = pd.to_numeric(labels[args.predicted_label], errors='coerce')
 
         if true_values.isna().all() or predicted_values.isna().all():
-            print(f"No valid numeric values found for known or predicted labels in '{target_value}'")
-            skipped_plots += 1
-            continue
+            print("No valid numeric values found for known or predicted labels")
 
         try:
-            print(f"  Generating scatter plot for '{target_value}'...")
+            print("  Generating scatter plot...")
             fig = plot_scatter(true_values, predicted_values)
 
             # Create output filename with target value
-            safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
-            if len(target_values) > 1:
-                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
-            else:
-                output_filename = f"{output_name_base}.{args.format}"
+            output_filename = f"{output_name_base}.{args.format}"
 
             output_path = output_dir / output_filename
             print(f"  Saving scatter plot to: {output_path.absolute()}")
             fig.save(output_path, dpi=args.dpi, bbox_inches='tight')
 
-            successful_plots += 1
-            print(f"  Scatter plot for '{target_value}' generated successfully!")
-
         except Exception as e:
-            print(f"  Error generating plot for '{target_value}': {str(e)}")
-            skipped_plots += 1
+            print(f"  Error generating plot: {str(e)}")
 
-    # Summary
-    print("  Summary:")
-    print(f"  Successfully generated: {successful_plots} plots")
-    print(f"  Skipped: {skipped_plots} plots")
-
-    if successful_plots == 0:
-        raise ValueError("No scatter plots could be generated. Check your data and target values.")
-
-    print("Scatter plot generation completed!")
+        print("Scatter plot generation completed!")
 
 
 def generate_label_concordance_heatmap(labels, args, output_dir, output_name_base):
     """Generate label concordance heatmap"""
     print("Generating label concordance heatmaps...")
 
-    # Parse target values from comma-separated string
-    if args.target_value:
-        target_values = [val.strip() for val in args.target_value.split(',')]
-    else:
-        # If no target values specified, use all unique variables
-        target_values = labels['variable'].unique().tolist()
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if is_flexynesis_format:
+        # Parse target values from comma-separated string
+        if args.target_value:
+            target_values = [val.strip() for val in args.target_value.split(',')]
+        else:
+            # If no target values specified, use all unique variables
+            target_values = labels['variable'].unique().tolist()
 
-    print(f"Processing target values: {target_values}")
+        print(f"Processing target values: {target_values}")
+
+        for target_value in target_values:
+            print(f"\nProcessing target value: '{target_value}'")
+
+            # Filter labels for the current target value
+            target_labels = labels[labels['variable'] == target_value]
+
+            if target_labels.empty:
+                print(f"  Warning: No data found for target value '{target_value}' - skipping")
+                continue
+
+            true_values = target_labels['known_label'].tolist()
+            predicted_values = target_labels['predicted_label'].tolist()
 
-    for target_value in target_values:
-        print(f"\nProcessing target value: '{target_value}'")
+            try:
+                print(f"  Generating heatmap for '{target_value}'...")
+                fig = plot_label_concordance_heatmap(true_values, predicted_values)
+                plt.close(fig)
 
-        # Filter labels for the current target value
-        target_labels = labels[labels['variable'] == target_value]
+                # Create output filename with target value
+                safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
+                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
+
+                output_path = output_dir / output_filename
+                print(f"  Saving heatmap to: {output_path.absolute()}")
+                fig.savefig(output_path, dpi=args.dpi, bbox_inches='tight')
 
-        if target_labels.empty:
-            print(f"  Warning: No data found for target value '{target_value}' - skipping")
-            continue
+            except Exception as e:
+                print(f"  Error generating heatmap for '{target_value}': {str(e)}")
+                continue
+
+        print("Label concordance heatmap generated successfully!")
 
-        true_values = target_labels['known_label'].tolist()
-        predicted_values = target_labels['predicted_label'].tolist()
+    if not is_flexynesis_format:
+        print("Labels are not in flexynesis format (Custom labels)")
+
+        if not args.true_label or not args.predicted_label:
+            raise ValueError("For custom labels, please specify --true_label and --predicted_label arguments.")
+
+        true_values = labels[args.true_label].tolist()
+        predicted_values = labels[args.predicted_label].tolist()
 
         try:
-            print(f"  Generating heatmap for '{target_value}'...")
+            print("  Generating heatmap for...")
             fig = plot_label_concordance_heatmap(true_values, predicted_values)
             plt.close(fig)
 
             # Create output filename with target value
-            safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
-            if len(target_values) > 1:
-                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
-            else:
-                output_filename = f"{output_name_base}.{args.format}"
+            output_filename = f"{output_name_base}.{args.format}"
 
             output_path = output_dir / output_filename
             print(f"  Saving heatmap to: {output_path.absolute()}")
             fig.savefig(output_path, dpi=args.dpi, bbox_inches='tight')
 
         except Exception as e:
-            print(f"  Error generating heatmap for '{target_value}': {str(e)}")
-            continue
+            print(f"  Error generating heatmap': {str(e)}")
 
-    print("Label concordance heatmap generated successfully!")
+        print("Label concordance heatmap generated successfully!")
 
 
 def generate_pr_curves(labels, args, output_dir, output_name_base):
     """Generate precision-recall curves"""
     print("Generating precision-recall curves...")
 
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if not is_flexynesis_format:
+        raise ValueError(f"Labels are not in flexynesis format (Custom labels). Please provide a valid label file with the required columns, {required_cols}.")
+
     # Parse target values from comma-separated string
     if args.target_value:
         target_values = [val.strip() for val in args.target_value.split(',')]
@@ -707,10 +784,7 @@
 
             # Create output filename with target value
             safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
-            if len(target_values) > 1:
-                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
-            else:
-                output_filename = f"{output_name_base}.{args.format}"
+            output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
 
             output_path = output_dir / output_filename
             print(f"  Saving precision-recall curve to: {output_path.absolute()}")
@@ -729,6 +803,13 @@
     """Generate ROC curves"""
     print("Generating ROC curves...")
 
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if not is_flexynesis_format:
+        raise ValueError(f"Labels are not in flexynesis format (Custom labels). Please provide a valid label file with the required columns, {required_cols}.")
+
     # Parse target values from comma-separated string
     if args.target_value:
         target_values = [val.strip() for val in args.target_value.split(',')]
@@ -859,10 +940,7 @@
 
             # Create output filename with target value
             safe_target_name = target_value.replace('/', '_').replace('\\', '_').replace(' ', '_')
-            if len(target_values) > 1:
-                output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
-            else:
-                output_filename = f"{output_name_base}.{args.format}"
+            output_filename = f"{output_name_base}_{safe_target_name}.{args.format}"
 
             output_path = output_dir / output_filename
             print(f"  Saving ROC curve to: {output_path.absolute()}")
@@ -880,6 +958,13 @@
 def generate_box_plots(labels, args, output_dir, output_name_base):
     """Generate box plots for model predictions"""
 
+    # Check if this is the specific format with sample_id, known_label, predicted_label
+    required_cols = ['sample_id', 'variable', 'class_label', 'probability', 'known_label', 'predicted_label']
+    is_flexynesis_format = all(col in labels.columns for col in required_cols)
+
+    if not is_flexynesis_format:
+        raise ValueError(f"Labels are not in flexynesis format (Custom labels). Please provide a valid label file with the required columns, {required_cols}.")
+
     print("Generating box plots...")
 
     # Parse target values from comma-separated string
@@ -993,6 +1078,8 @@
                         help="Path to input data embeddings file (CSV or tabular format). Required for dimred plots.")
     parser.add_argument("--method", type=str, default='pca', choices=['pca', 'umap'],
                         help="Transformation method ('pca' or 'umap'). Default is 'pca'. Used for dimred plots.")
+    parser.add_argument("--color", type=str, default=None,
+                        help="User-defined color for the plot.")
 
     # Arguments for Kaplan-Meier
     parser.add_argument("--survival_data", type=str,
@@ -1001,12 +1088,10 @@
                         help="Column name for survival time")
     parser.add_argument("--surv_event_var", type=str, required=False,
                         help="Column name for survival event")
-    parser.add_argument("--event_value", type=str, required=False,
-                        help="Value in event column that represents an event (e.g., 'DECEASED')")
 
     # Arguments for Cox analysis
-    parser.add_argument("--model", type=str,
-                        help="Path to trained flexynesis model (pickle file). Required for cox plots.")
+    parser.add_argument("--important_features", type=str,
+                        help="Path to calculated feature importance file. Required for cox plots.")
     parser.add_argument("--clinical_train", type=str,
                         help="Path to training dataset (pickle file). Required for cox plots.")
     parser.add_argument("--clinical_test", type=str,
@@ -1025,11 +1110,18 @@
                         help="Number of folds for cross-validation. Default is 5")
     parser.add_argument("--random_state", type=int, default=42,
                         help="Random seed for reproducibility. Default is 42")
+    parser.add_argument("--layer", type=str, default=None,
+                        help="Class label for filtering important features.")
 
     # Arguments for dimred, scatter plot, heatmap, PR curves, ROC curves, and box plots
     parser.add_argument("--target_value", type=str, default=None,
                         help="Target value for scatter plot.")
 
+    # Arguments for scatter plots and concordance heatmaps
+    parser.add_argument("--true_label", type=str, default=None,
+                        help="Column name for true labels in scatter plots and concordance heatmaps.")
+    parser.add_argument("--predicted_label", type=str, default=None,
+                        help="Column name for predicted labels in scatter plots and concordance heatmaps.")
     # Common arguments
     parser.add_argument("--output_dir", type=str, default='output',
                         help="Output directory. Default is 'output'")
@@ -1073,14 +1165,12 @@
                 raise ValueError("--surv_time_var is required for Kaplan-Meier plots")
             if not args.surv_event_var:
                 raise ValueError("--surv_event_var is required for Kaplan-Meier plots")
-            if not args.event_value:
-                raise ValueError("--event_value is required for Kaplan-Meier plots")
 
         if args.plot_type in ['cox']:
-            if not args.model:
-                raise ValueError("--model is required when plot_type is 'cox'")
-            if not os.path.isfile(args.model):
-                raise FileNotFoundError(f"Model file not found: {args.model}")
+            if not args.important_features:
+                raise ValueError("--important_features is required when plot_type is 'cox'")
+            if not os.path.isfile(args.important_features):
+                raise FileNotFoundError(f"Important features file not found: {args.important_features}")
             if not args.clinical_train:
                 raise ValueError("--clinical_train is required when plot_type is 'cox'")
             if not os.path.isfile(args.clinical_train):
@@ -1102,17 +1192,17 @@
             if not args.surv_event_var:
                 raise ValueError("--surv_event_var is required for Cox plots")
             if not args.clinical_variables:
-                raise ValueError("--clinical_variables is required for Cox plots")
+                print("--clinical_variables is not set for Cox plots")
             if not isinstance(args.top_features, int) or args.top_features <= 0:
                 raise ValueError("--top_features must be a positive integer")
-            if not args.event_value:
-                raise ValueError("--event_value is required for Kaplan-Meier plots")
             if not args.crossval:
                 args.crossval = False
             if not isinstance(args.n_splits, int) or args.n_splits <= 0:
                 raise ValueError("--n_splits must be a positive integer")
             if not isinstance(args.random_state, int):
                 raise ValueError("--random_state must be an integer")
+            if not args.layer:
+                print("--layer is not specified, using all classes from labels")
 
         if args.plot_type in ['scatter']:
             if not args.labels:
@@ -1174,7 +1264,7 @@
                 survival_name = Path(args.survival_data).stem
                 output_name_base = f"{survival_name}_km"
             elif args.plot_type == 'cox':
-                model_name = Path(args.model).stem
+                model_name = Path(args.important_features).stem
                 output_name_base = f"{model_name}_cox"
             elif args.plot_type == 'scatter':
                 labels_name = Path(args.labels).stem
@@ -1196,33 +1286,34 @@
         if args.plot_type in ['dimred']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
             # Load embeddings data
             print(f"Loading embeddings from: {args.embeddings}")
             embeddings, sample_names = load_embeddings(args.embeddings)
             print(f"embeddings shape: {embeddings.shape}")
 
             # Match samples to embeddings
-            matched_labels = match_samples_to_embeddings(sample_names, label_data)
+            matched_labels = match_samples_to_embeddings(sample_names, labels)
             print(f"Successfully matched {len(matched_labels)} samples for dimensionality reduction")
-
+            print(f"Matched labels shape: {matched_labels.shape}")
+            print(f"Columns in matched labels: {matched_labels.columns.tolist()}")
             generate_dimred_plots(embeddings, matched_labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['kaplan_meier']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
             # Load survival data
             print(f"Loading survival data from: {args.survival_data}")
-            survival_data = load_survival_data(args.survival_data)
+            survival_data = load_labels(args.survival_data)
             print(f"Survival data shape: {survival_data.shape}")
 
-            generate_km_plots(survival_data, label_data, args, output_dir, output_name_base)
+            generate_km_plots(survival_data, labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['cox']:
-            # Load model and datasets
-            print(f"Loading model from: {args.model}")
-            model = load_model(args.model)
+            # Load important_features and datasets
+            print(f"Loading important features from: {args.important_features}")
+            important_features = load_labels(args.important_features)
             print(f"Loading training dataset from: {args.clinical_train}")
             clinical_train = load_omics(args.clinical_train)
             print(f"Loading test dataset from: {args.clinical_test}")
@@ -1232,42 +1323,42 @@
             print(f"Loading test omics dataset from: {args.omics_test}")
             omics_test = load_omics(args.omics_test)
 
-            generate_cox_plots(model, clinical_train, clinical_test, omics_test, omics_train, args, output_dir, output_name_base)
+            generate_cox_plots(important_features, clinical_train, clinical_test, omics_test, omics_train, args, output_dir, output_name_base)
 
         elif args.plot_type in ['scatter']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
 
-            generate_plot_scatter(label_data, args, output_dir, output_name_base)
+            generate_plot_scatter(labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['concordance_heatmap']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
 
-            generate_label_concordance_heatmap(label_data, args, output_dir, output_name_base)
+            generate_label_concordance_heatmap(labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['pr_curve']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
 
-            generate_pr_curves(label_data, args, output_dir, output_name_base)
+            generate_pr_curves(labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['roc_curve']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
 
-            generate_roc_curves(label_data, args, output_dir, output_name_base)
+            generate_roc_curves(labels, args, output_dir, output_name_base)
 
         elif args.plot_type in ['box_plot']:
             # Load labels
             print(f"Loading labels from: {args.labels}")
-            label_data = load_labels(args.labels)
+            labels = load_labels(args.labels)
 
-            generate_box_plots(label_data, args, output_dir, output_name_base)
+            generate_box_plots(labels, args, output_dir, output_name_base)
 
         print("All plots generated successfully!")
 
--- a/flexynesis_utils.py	Fri Jul 04 14:57:40 2025 +0000
+++ b/flexynesis_utils.py	Wed Jul 23 07:49:41 2025 +0000
@@ -163,14 +163,54 @@
             continue
 
 
+def validate_numeric_column(df, column_names, require_integer=False):
+    """ Validate that a column(s) in the DataFrame contains numeric values. """
+    if isinstance(column_names, str):
+        # Handle comma-separated string: "col1,col2,col3"
+        if ',' in column_names:
+            column_names = [col.strip() for col in column_names.split(',')]
+        else:
+            # Single column name
+            column_names = [column_names]
+
+    # Validate each column
+    for column_name in column_names:
+        if column_name not in df.columns:
+            raise ValueError(f"Column '{column_name}' not found in DataFrame.")
+
+        try:
+            numeric_col = pd.to_numeric(df[column_name], errors='raise')
+        except Exception as e:
+            raise ValueError(f"Non-numeric values found in column '{column_name}': {e}")
+
+        if require_integer:
+            # Check if all non-null values are equivalent to integers
+            non_null_values = numeric_col.dropna()
+            if not (non_null_values == non_null_values.round()).all():
+                raise ValueError(f"Column '{column_name}' contains non-integer numeric values.")
+            print(f"Column '{column_name}': All values are integers or integer-equivalent floats.")
+        else:
+            print(f"Column '{column_name}': All values are numeric (integers and floats accepted).")
+
+
+def validate_survival(df, column_names):
+    """Validate survival column(s) (integer)."""
+    validate_numeric_column(df, column_names, require_integer=True)
+
+
+def validate_covariate(df, column_names):
+    """Validate covariate column(s) (numeric)."""
+    validate_numeric_column(df, column_names, require_integer=False)
+
+
 def main():
     parser = argparse.ArgumentParser(description='Flexynesis extra utilities')
 
     parser.add_argument("--util", type=str, required=True,
-                        choices=['split', 'binarize'],
-                        help="Utility function: 'split' for spiting data to train and test, 'binarize' for creating a binarized matrix from a mutation data")
+                        choices=['split', 'binarize', 'validate_survival', 'validate_covariate'],
+                        help="Utility function: 'split' for spiting data to train and test, 'binarize' for creating a binarized matrix from a mutation data, 'validate_survival' for validating survival data.")
 
-    # Arguments for split
+    # Arguments for split (clin also for validate_survival and validate_covariate)
     parser.add_argument('--clin', required=False,
                         help='Path to clinical data CSV file (samples in rows)')
     parser.add_argument('--omics', required=False,
@@ -186,7 +226,11 @@
     parser.add_argument('--sample_idx', type=int, default=1,
                         help='Column index for samples in mutation data (default: 1)')
 
-    # common arguments
+    # Arguments for validate_survival and validate_covariate
+    parser.add_argument('--clin_variable', type=str, required=False,
+                        help='Column name for clinical variable (e.g., death, SEX, ...)')
+
+    # common arguments (binarize and split)
     parser.add_argument('--out', default='.',
                         help='Output directory (default: current directory)')
 
@@ -196,7 +240,7 @@
         # validate utility function
         if not args.util:
             raise ValueError("Utility function must be specified")
-        if args.util not in ['split', 'binarize']:
+        if args.util not in ['split', 'binarize', 'validate_survival', 'validate_covariate']:
             raise ValueError(f"Invalid utility function: {args.util}")
 
         if args.util == 'split':
@@ -221,6 +265,16 @@
             if args.gene_idx < 0 or args.sample_idx < 0:
                 raise ValueError("Gene and sample indices must be non-negative integers")
 
+        elif args.util == 'validate_survival' or args.util == 'validate_covariate':
+            # Validate clinical data file
+            if not args.clin:
+                raise ValueError("Clinical data file must be provided")
+            if not os.path.isfile(args.clin):
+                raise FileNotFoundError(f"Clinical file not found: {args.clin}")
+            # Validate survival event variable
+            if not args.clin_variable:
+                raise ValueError("Survival event variable must be specified")
+
         # Create output directory if it doesn't exist
         if not os.path.exists(args.out):
             os.makedirs(args.out)
@@ -248,6 +302,22 @@
             binarized_matrix.to_csv(output_file, sep='\t')
             print(f"Binarized mutation matrix saved to {output_file}")
 
+        elif args.util == 'validate_survival':
+            clin_df = read_data(args.clin, index=False)
+            if clin_df.empty:
+                raise ValueError("Clinical data file is empty")
+
+            # Validate survival event variable
+            validate_survival(clin_df, args.clin_variable)
+
+        elif args.util == 'validate_covariate':
+            clin_df = read_data(args.clin, index=False)
+            if clin_df.empty:
+                raise ValueError("Clinical data file is empty")
+
+            # Validate clinical variable
+            validate_covariate(clin_df, args.clin_variable)
+
     except Exception as e:
         print(f"Error: {e}", file=sys.stderr)
         sys.exit(1)
--- a/index_to_name.py	Fri Jul 04 14:57:40 2025 +0000
+++ b/index_to_name.py	Wed Jul 23 07:49:41 2025 +0000
@@ -1,25 +1,51 @@
 #!/usr/bin/env python
 
 import sys
+from pathlib import Path
 
 import pandas as pd
 
 
-def get_column_name(file_path, index):
+def get_column_names(file_path, indices):
     """
-    Get the column name based on an index from a tabular file.
+    Get column names based on multiple indices from a tabular file.
     """
-    data = pd.read_csv(file_path, sep=",")
-    index = index - 1  # Convert to zero-based index
-    if index < 0 or index >= len(data.columns):
-        print(f"Error: Index {index+1} is out of range. File has {len(data.columns)} columns (1-{len(data.columns)}).")
-        return None
-    return data.columns[index].strip()
+    try:
+        file_ext = Path(file_path).suffix.lower()
+        sep = ',' if file_ext == '.csv' else '\t'
+
+        if file_ext in ['.csv', '.tsv', '.txt', '.tab', '.tabular']:
+            data = pd.read_csv(file_path, sep=sep)
+        else:
+            raise ValueError(f"Unsupported file extension: {file_ext}")
+
+    except Exception as e:
+        raise ValueError(f"Error loading data from {file_path}: {e}") from e
+
+    column_names = []
+
+    for index in indices:
+        zero_based_index = index - 1  # Convert to zero-based index
+        if zero_based_index < 0 or zero_based_index >= len(data.columns):
+            print(f"Error: Index {index} is out of range. File has {len(data.columns)} columns (1-{len(data.columns)}).")
+            return None
+        column_names.append(data.columns[zero_based_index].strip())
+
+    return column_names
 
 
 if __name__ == "__main__":
 
     file_path = sys.argv[1]
-    index = int(sys.argv[2])
+    indices_str = sys.argv[2]
 
-    print(get_column_name(file_path, index))
+    # Parse comma-separated indices
+    try:
+        indices = [int(i.strip()) for i in indices_str.split(',')]
+    except ValueError:
+        print("Error: All indices must be integers.")
+        sys.exit(1)
+
+    result = get_column_names(file_path, indices)
+    if result is not None:
+        print(','.join(result))
--- a/macros.xml	Fri Jul 04 14:57:40 2025 +0000
+++ b/macros.xml	Wed Jul 23 07:49:41 2025 +0000
@@ -1,6 +1,6 @@
 <macros>
     <token name="@TOOL_VERSION@">0.2.20</token>
-    <token name="@VERSION_SUFFIX@">2</token>
+    <token name="@VERSION_SUFFIX@">3</token>
     <token name="@PROFILE@">24.1</token>
     <xml name="requirements">
         <requirements>
@@ -91,14 +91,24 @@
             </param>
         </section>
     </xml>
-    <xml name="plots_common_param">
-        <yield/>
+    <xml name="plots_common_format">
         <param name="format" type="select" label="Output format">
             <option value="jpg" selected="true">jpg</option>
             <option value="png">png</option>
             <option value="pdf">pdf</option>
+            <yield/>
+        </param>
+    </xml>
+    <xml name="plots_common_param">
+        <yield/>
+        <expand macro="plots_common_format"/>
+        <param name="dpi" type="integer" min="0" max="1200" value="300" label="DPI"/>
+    </xml>
+    <xml name="plots_common_param_svg">
+        <yield/>
+        <expand macro="plots_common_format">
             <option value="svg">svg</option>
-        </param>
+        </expand>
         <param name="dpi" type="integer" min="0" max="1200" value="300" label="DPI"/>
     </xml>
     <xml name="plots_common_input">
@@ -229,16 +239,14 @@
             <conditional name="model_class">
                 <param name="model_class" value="DirectPred"/>
             </conditional>
-            <repeat name="targets">
-                <param name="target_variables" value="3"/>
-            </repeat>
+            <param name="target_variables" value="3"/>
             <param name="surv_event_var" value="15"/>
             <param name="surv_time_var" value="14"/>
             <section name="advanced">
                 <param name="hpo_iter" value="1"/>
             </section>
+            <yield/>
         </conditional>
-        <yield/>
         <output_collection name="results" type="list">
             <element name="job.embeddings_test">
                 <assert_contents>
@@ -269,7 +277,7 @@
                     <has_n_lines n="25"/>
                 </assert_contents>
             </element>
-            <element name="job.feature_logs.omics_foo">
+            <element name="job.feature_logs.foo">
                 <assert_contents>
                     <has_n_lines n="25"/>
                 </assert_contents>
@@ -289,6 +297,81 @@
             </element>
         </output_collection>
     </xml>
+    <xml name="common_test_class">
+        <param name="non_commercial_use" value="True"/>
+        <conditional name="training_type">
+            <param name="model" value="s_train"/>
+            <param name="train_clin" value="train/clin" ftype="tabular"/>
+            <param name="test_clin" value="test/clin" ftype="tabular"/>
+            <param name="train_omics_main" value="train/gex" ftype="tabular"/>
+            <param name="test_omics_main" value="test/gex" ftype="tabular"/>
+            <param name="assay_main" value="bar"/>
+            <repeat name="omics">
+                <param name="train_omics" value="train/cnv" ftype="tabular"/>
+                <param name="test_omics" value="test/cnv" ftype="tabular"/>
+                <param name="assay" value="foo"/>
+            </repeat>
+            <conditional name="model_class">
+                <param name="model_class" value="DirectPred"/>
+            </conditional>
+            <param name="target_variables" value="16"/>
+            <param name="surv_event_var" value="15"/>
+            <param name="surv_time_var" value="14"/>
+            <section name="advanced">
+                <param name="hpo_iter" value="1"/>
+            </section>
+            <yield/>
+        </conditional>
+        <output_collection name="results" type="list">
+            <element name="job.embeddings_test">
+                <assert_contents>
+                    <has_n_lines n="50"/>
+                </assert_contents>
+            </element>
+            <element name="job.embeddings_train">
+                <assert_contents>
+                    <has_n_lines n="50"/>
+                </assert_contents>
+            </element>
+            <element name="job.feature_importance.GradientShap">
+                <assert_contents>
+                    <has_text_matching expression="class\t0\tbuz\tbar\tA2M\t"/>
+                    <has_text_matching expression="class\t0\tbuz\tbar\tABCC4\t"/>
+                    <has_text_matching expression="GradientShap"/>
+                </assert_contents>
+            </element>
+            <element name="job.feature_importance.IntegratedGradients">
+                <assert_contents>
+                    <has_text_matching expression="class\t0\tbuz\tbar\tA2M\t"/>
+                    <has_text_matching expression="class\t0\tbuz\tbar\tABCC4\t"/>
+                    <has_text_matching expression="IntegratedGradients"/>
+                </assert_contents>
+            </element>
+            <element name="job.feature_logs.bar">
+                <assert_contents>
+                    <has_n_lines n="25"/>
+                </assert_contents>
+            </element>
+            <element name="job.feature_logs.foo">
+                <assert_contents>
+                    <has_n_lines n="25"/>
+                </assert_contents>
+            </element>
+            <element name="job.predicted_labels">
+                <assert_contents>
+                    <has_text_matching expression="source_dataset:A-704\tclass\t"/>
+                    <has_text_matching expression="target_dataset:KMRC-20\tclass\t"/>
+                </assert_contents>
+            </element>
+            <element name="job.stats">
+                <assert_contents>
+                    <has_text_matching expression="DirectPred\tclass\tcategorical\tbalanced_acc\t"/>
+                    <has_text_matching expression="DirectPred\tclass\tcategorical\tf1_score\t"/>
+                    <has_text_matching expression="DirectPred\tclass\tcategorical\taverage_auroc\t"/>
+                </assert_contents>
+            </element>
+        </output_collection>
+    </xml>
     <token name="@COMMON_HELP@">
 
 .. class:: warningmark
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/feature_importance.tabular	Wed Jul 23 07:49:41 2025 +0000
@@ -0,0 +1,45 @@
+target_variable	target_class	target_class_label	layer	name	importance	explainer
+OS_STATUS	0		gex	A2M	0.08044747	GradientShap
+OS_STATUS	0		gex	AADAC	0.052168246	GradientShap
+OS_STATUS	0		gex	AADAT	0.071941234	GradientShap
+OS_STATUS	0		gex	AATK	0.08380627	GradientShap
+OS_STATUS	0		gex	ABAT	0.056403544	GradientShap
+OS_STATUS	0		gex	ABCA1	0.089042485	GradientShap
+OS_STATUS	0		gex	ABCA3	0.05821233	GradientShap
+OS_STATUS	0		gex	ABCA5	0.04674581	GradientShap
+OS_STATUS	0		gex	ABCB1	0.018299112	GradientShap
+OS_STATUS	0		gex	ABCC1	0.10249834	GradientShap
+OS_STATUS	0		gex	ABCC2	0.08114307	GradientShap
+OS_STATUS	0		gex	ABCC3	0.066190474	GradientShap
+OS_STATUS	0		gex	ABCC4	0.060095534	GradientShap
+OS_STATUS	0		gex	ABCC5	0.06730405	GradientShap
+OS_STATUS	0		gex	ABCC9	0.0676594	GradientShap
+OS_STATUS	0		gex	ABCG2	0.021599974	GradientShap
+OS_STATUS	0		gex	ABHD2	0.14912239	GradientShap
+OS_STATUS	0		gex	ABHD6	0.093939394	GradientShap
+OS_STATUS	0		gex	ABI1	0.15956701	GradientShap
+OS_STATUS	0		gex	ABI3BP	0.07714649	GradientShap
+OS_STATUS	0		gex	ABL1	0.06781345	GradientShap
+OS_STATUS	0		gex	ABL2	0.053579647	GradientShap
+OS_STATUS	0		cnv	A2M	0.029520346	GradientShap
+OS_STATUS	0		cnv	AADAC	0.033482064	GradientShap
+OS_STATUS	0		cnv	AADAT	0.054546494	GradientShap
+OS_STATUS	0		cnv	AATK	0.02869266	GradientShap
+OS_STATUS	0		cnv	ABAT	0.04758611	GradientShap
+OS_STATUS	0		cnv	ABCA1	0.04938787	GradientShap
+OS_STATUS	0		cnv	ABCA3	0.05460553	GradientShap
+OS_STATUS	0		cnv	ABCA5	0.025420863	GradientShap
+OS_STATUS	0		cnv	ABCB1	0.034484893	GradientShap
+OS_STATUS	0		cnv	ABCB4	0.032653462	GradientShap
+OS_STATUS	0		cnv	ABCC1	0.030871375	GradientShap
+OS_STATUS	0		cnv	ABCC2	0.0406215	GradientShap
+OS_STATUS	0		cnv	ABCC3	0.046062056	GradientShap
+OS_STATUS	0		cnv	ABCC4	0.034546487	GradientShap
+OS_STATUS	0		cnv	ABCC5	0.036066007	GradientShap
+OS_STATUS	0		cnv	ABCC9	0.054897416	GradientShap
+OS_STATUS	0		cnv	ABCG2	0.024533378	GradientShap
+OS_STATUS	0		cnv	ABHD2	0.041559853	GradientShap
+OS_STATUS	0		cnv	ABHD6	0.041626643	GradientShap
+OS_STATUS	0		cnv	ABI3BP	0.02400354	GradientShap
+OS_STATUS	0		cnv	ABL1	0.02385166	GradientShap
+OS_STATUS	0		cnv	ABL2	0.03914579	GradientShap
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/labels_survival.tabular	Wed Jul 23 07:49:41 2025 +0000
@@ -0,0 +1,98 @@
+sample_id	variable	class_label	probability	known_label	predicted_label	split	method
+source_dataset:CA46	OS_STATUS			0.0	-0.1806577444076538	train	DirectPred
+source_dataset:GaMG	OS_STATUS			0.0	-0.2735559344291687	train	DirectPred
+source_dataset:CHP-126	OS_STATUS			1.0	-0.158678337931633	train	DirectPred
+source_dataset:C-33 A	OS_STATUS			0.0	-0.0930734723806381	train	DirectPred
+source_dataset:SK-N-SH	OS_STATUS			1.0	0.0803708285093307	train	DirectPred
+source_dataset:Ishikawa (Heraklio) 02 ER-	OS_STATUS			1.0	0.2717544734477997	train	DirectPred
+source_dataset:NCI-H2810	OS_STATUS			1.0	0.7725857496261597	train	DirectPred
+source_dataset:HEC-1	OS_STATUS			0.0	-0.4454052746295929	train	DirectPred
+source_dataset:LOX-IMVI	OS_STATUS			1.0	0.704920768737793	train	DirectPred
+source_dataset:C32 [Human melanoma]	OS_STATUS			1.0	0.1715598702430725	train	DirectPred
+source_dataset:TE-5	OS_STATUS			1.0	0.3775416016578674	train	DirectPred
+source_dataset:Caki-1	OS_STATUS			0.0	0.0848075971007347	train	DirectPred
+source_dataset:EW-24	OS_STATUS			0.0	0.2063406258821487	train	DirectPred
+source_dataset:NCI-H841	OS_STATUS			1.0	1.0794814825057983	train	DirectPred
+source_dataset:NCI-H2087	OS_STATUS			0.0	-0.0288252718746662	train	DirectPred
+source_dataset:AM-38	OS_STATUS			1.0	0.709104061126709	train	DirectPred
+source_dataset:A-704	OS_STATUS			0.0	-0.7821771502494812	train	DirectPred
+source_dataset:JVM-2	OS_STATUS			1.0	1.0423946380615234	train	DirectPred
+source_dataset:SW1463	OS_STATUS			0.0	-1.2924553155899048	train	DirectPred
+source_dataset:HuCC-T1	OS_STATUS			0.0	-0.1352562308311462	train	DirectPred
+source_dataset:MHH-PREB-1	OS_STATUS			1.0	1.033470869064331	train	DirectPred
+source_dataset:KM12	OS_STATUS			0.0	-0.3434832692146301	train	DirectPred
+source_dataset:MOLM-13	OS_STATUS			1.0	0.6091863512992859	train	DirectPred
+source_dataset:NCI-H748	OS_STATUS			1.0	0.461676687002182	train	DirectPred
+source_dataset:SNG-M	OS_STATUS			0.0	-0.5159832239151001	train	DirectPred
+source_dataset:L-540	OS_STATUS			1.0	1.5666080713272097	train	DirectPred
+source_dataset:NB(TU)1	OS_STATUS			1.0	-0.474257230758667	train	DirectPred
+source_dataset:NCI-H2342	OS_STATUS			1.0	-0.1848507672548294	train	DirectPred
+source_dataset:BICR 31	OS_STATUS			0.0	-0.2494427412748336	train	DirectPred
+source_dataset:SaOS-2	OS_STATUS			0.0	-0.4057216346263885	train	DirectPred
+source_dataset:MOLT-4	OS_STATUS			1.0	0.6046818494796753	train	DirectPred
+source_dataset:HCC1806	OS_STATUS			1.0	-0.040439385920763	train	DirectPred
+source_dataset:CTB-1	OS_STATUS			1.0	1.6053680181503296	train	DirectPred
+source_dataset:NCI-H28	OS_STATUS			0.0	0.3380573987960815	train	DirectPred
+source_dataset:VMRC-RCZ	OS_STATUS			0.0	-0.6231226921081543	train	DirectPred
+source_dataset:FU-OV-1	OS_STATUS			0.0	-1.6908191442489624	train	DirectPred
+source_dataset:KTCTL-1M	OS_STATUS			1.0	0.4334348440170288	train	DirectPred
+source_dataset:ME-1 [Human leukemia]	OS_STATUS			0.0	-0.3174859881401062	train	DirectPred
+source_dataset:EFM-19	OS_STATUS			1.0	0.1237960085272789	train	DirectPred
+source_dataset:A-673	OS_STATUS			1.0	0.216995969414711	train	DirectPred
+source_dataset:NCI-H211	OS_STATUS			1.0	0.0461718328297138	train	DirectPred
+source_dataset:NUGC-3	OS_STATUS			1.0	-0.0305876992642879	train	DirectPred
+source_dataset:PA-1	OS_STATUS			0.0	-0.4507119059562683	train	DirectPred
+source_dataset:Kelly	OS_STATUS			0.0	0.1544368863105774	train	DirectPred
+source_dataset:BB65-RCC	OS_STATUS			1.0	0.2762856185436249	train	DirectPred
+source_dataset:NCI-H196	OS_STATUS			1.0	-0.476017415523529	train	DirectPred
+source_dataset:NCI-H345	OS_STATUS			0.0	-0.4505922794342041	train	DirectPred
+source_dataset:WSU-DLCL2	OS_STATUS			0.0	0.1862639188766479	train	DirectPred
+target_dataset:Reh	OS_STATUS			1.0	0.2421486228704452	test	DirectPred
+target_dataset:TE-15	OS_STATUS			1.0	0.1572644859552383	test	DirectPred
+target_dataset:AMO1	OS_STATUS			1.0	-0.3161105513572693	test	DirectPred
+target_dataset:TE-4	OS_STATUS			0.0	0.4426631331443786	test	DirectPred
+target_dataset:SNU-C1	OS_STATUS			1.0	0.5407190918922424	test	DirectPred
+target_dataset:Hep 3B2.1-7	OS_STATUS			0.0	0.0586175434291362	test	DirectPred
+target_dataset:SU-DHL-6	OS_STATUS			0.0	0.4308116137981415	test	DirectPred
+target_dataset:A-204	OS_STATUS			1.0	0.0546496361494064	test	DirectPred
+target_dataset:Panc 08.13	OS_STATUS			0.0	0.4611753821372986	test	DirectPred
+target_dataset:59M	OS_STATUS			1.0	1.1866464614868164	test	DirectPred
+target_dataset:ONS-76	OS_STATUS			0.0	-0.3515381813049316	test	DirectPred
+target_dataset:IM95	OS_STATUS			1.0	0.6300694346427917	test	DirectPred
+target_dataset:Hs 746.T	OS_STATUS			0.0	-0.270365446805954	test	DirectPred
+target_dataset:TM-31	OS_STATUS			1.0	-0.3712011277675628	test	DirectPred
+target_dataset:VCaP	OS_STATUS			1.0	2.49214506149292	test	DirectPred
+target_dataset:CMK-86	OS_STATUS			1.0	0.0789867863059043	test	DirectPred
+target_dataset:B-CPAP	OS_STATUS			0.0	-0.1938754618167877	test	DirectPred
+target_dataset:SCC-9	OS_STATUS			0.0	0.4904790818691253	test	DirectPred
+target_dataset:TO 175.T	OS_STATUS			1.0	0.0342572703957557	test	DirectPred
+target_dataset:TE-9	OS_STATUS			0.0	0.2002457380294799	test	DirectPred
+target_dataset:KMRC-20	OS_STATUS			0.0	-0.0571640506386756	test	DirectPred
+target_dataset:U-251MG	OS_STATUS			1.0	-0.2494360655546188	test	DirectPred
+target_dataset:Hs 695T	OS_STATUS			1.0	-0.0931628495454788	test	DirectPred
+target_dataset:Huh-7	OS_STATUS			0.0	-0.1090110093355178	test	DirectPred
+target_dataset:COV434	OS_STATUS			1.0	0.4086113274097442	test	DirectPred
+target_dataset:SNU-C2A	OS_STATUS			0.0	-0.2564590275287628	test	DirectPred
+target_dataset:CL-11	OS_STATUS			1.0	0.1929528713226318	test	DirectPred
+target_dataset:Becker	OS_STATUS			1.0	-0.616310715675354	test	DirectPred
+target_dataset:HPAF-II	OS_STATUS			1.0	-0.1382206976413726	test	DirectPred
+target_dataset:JIMT-1	OS_STATUS			1.0	-0.3265259861946106	test	DirectPred
+target_dataset:IST-Mes1	OS_STATUS			1.0	-1.4989557266235352	test	DirectPred
+target_dataset:TC-71	OS_STATUS			0.0	-0.7402385473251343	test	DirectPred
+target_dataset:TE-14	OS_STATUS			0.0	0.8502606749534607	test	DirectPred
+target_dataset:NCI-H929	OS_STATUS			1.0	-0.4053770005702972	test	DirectPred
+target_dataset:SNU-449	OS_STATUS			1.0	-0.2718466222286224	test	DirectPred
+target_dataset:NCI-H2171	OS_STATUS			1.0	0.2422662377357483	test	DirectPred
+target_dataset:KLE	OS_STATUS			0.0	0.9158359169960022	test	DirectPred
+target_dataset:A101D	OS_STATUS			0.0	0.6820786595344543	test	DirectPred
+target_dataset:RVH-421	OS_STATUS			1.0	0.7860141396522522	test	DirectPred
+target_dataset:OVKATE	OS_STATUS			1.0	-0.9652036428451538	test	DirectPred
+target_dataset:MOLP-2	OS_STATUS			0.0	0.1615296602249145	test	DirectPred
+target_dataset:NUGC-3	OS_STATUS			1.0	-0.2149223238229751	test	DirectPred
+target_dataset:UACC-812	OS_STATUS			0.0	-0.1816692650318145	test	DirectPred
+target_dataset:Panc 03.27	OS_STATUS			0.0	0.2970135807991028	test	DirectPred
+target_dataset:SU.86.86	OS_STATUS			0.0	-0.4632374048233032	test	DirectPred
+target_dataset:CAL-29	OS_STATUS			0.0	-0.1858485490083694	test	DirectPred
+target_dataset:Hs 571.T	OS_STATUS			0.0	0.7476100325584412	test	DirectPred
+target_dataset:CGTH-W-1	OS_STATUS			1.0	0.2606591284275055	test	DirectPred
+target_dataset:SK-MM-2	OS_STATUS			1.0	0.4870605766773224	test	DirectPred
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/non_flexy_label.tabular	Wed Jul 23 07:49:41 2025 +0000
@@ -0,0 +1,1001 @@
+ID	orig_ident	nCount_RNA	nFeature_RNA	nCount_ADT	nFeature_ADT	lane	donor	celltype_l1	celltype_l2	RNA_weight	louvain_cluster	optimal_kmeans_cluster
+b_CTTCTCTAGAGTACAT-1	bmcite	2049	692	4282	25	HumanHTO10	batch2	T cell	CD4 Naive	0.31392012141395	8.0	3
+b_TTAACTCCACAAGACG-1	bmcite	1928	848	4026	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.37053806684038	5.0	5
+b_ATCCGAACATTCCTCG-1	bmcite	2175	672	3315	25	HumanHTO5	batch2	T cell	CD4 Memory	0.372423562532135	9.0	3
+a_AGGTCATCAATGTTGC-1	bmcite	2781	1042	4968	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.59516921668449	6.0	5
+a_CTTAACTCAGGTCCAC-1	bmcite	2651	837	3558	25	HumanHTO1	batch1	B cell	Naive B	0.344135017028519	2.0	1
+b_AGTGTCAAGACCACGA-1	bmcite	1820	659	6085	25	HumanHTO3	batch2	T cell	gdT	0.443421574374962	4.0	3
+b_AGATTGCAGAACAATC-1	bmcite	2468	810	5565	25	HumanHTO3	batch2	T cell	CD8 Memory_2	0.105109276773238	9.0	7
+a_TTGAACGAGGCTACGA-1	bmcite	2619	763	6994	25	HumanHTO3	batch1	T cell	CD4 Naive	0.401795282104621	4.0	3
+a_AGATTGCAGTACGATA-1	bmcite	1176	548	4365	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.448263243217848	5.0	5
+b_GCTGCGATCGCTTGTC-1	bmcite	3127	896	6345	25	HumanHTO3	batch2	T cell	CD4 Memory	0.403488308312786	9.0	3
+b_ACGCCAGAGGCGATAC-1	bmcite	2467	736	5454	25	HumanHTO1	batch2	T cell	CD4 Naive	0.328619391425333	4.0	3
+a_TGCTACCTCAGGCGAA-1	bmcite	1109	396	3621	25	HumanHTO2	batch1	T cell	CD4 Naive	0.393931757245713	8.0	9
+b_CTGGTCTTCTTTACAC-1	bmcite	4623	1257	660	25	HumanHTO9	batch2	Progenitor cells	Prog_Mk	0.597852416930292	3.0	0
+b_CCTTCGACAAGGTTCT-1	bmcite	6779	1704	1272	25	HumanHTO3	batch2	Progenitor cells	Prog_RBC	0.860182963053854	3.0	0
+b_GATCGTACATAAAGGT-1	bmcite	17138	3376	556	25	HumanHTO6	batch2	Progenitor cells	Prog_RBC	0.829102219658132	3.0	2
+b_TAAGTGCTCTCTAGGA-1	bmcite	1092	562	3505	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.534777228524069	5.0	9
+b_GAACATCCACGGCCAT-1	bmcite	10578	2322	1352	25	HumanHTO9	batch2	Progenitor cells	LMPP	0.599548095925508	3.0	0
+a_GCGCCAAGTGCAACTT-1	bmcite	2234	664	5867	25	HumanHTO2	batch1	T cell	CD4 Naive	0.449554326355043	4.0	3
+b_CACTCCATCCGTTGTC-1	bmcite	1135	520	3405	25	HumanHTO5	batch2	NK	NK	0.0575009735691949	7.0	8
+a_TGAAAGAGTATTAGCC-1	bmcite	12075	2946	365	24	HumanHTO6	batch1	Progenitor cells	Prog_RBC	0.595919353907761	3.0	0
+b_ACTATCTCACGAAACG-1	bmcite	1937	578	2160	25	HumanHTO8	batch2	T cell	CD8 Naive	0.0175685330685356	8.0	9
+a_CCTCAGTAGCCGTCGT-1	bmcite	1434	658	7499	25	HumanHTO1	batch1	Mono/DC	CD16 Mono	0.465167634817538	5.0	5
+a_TAGGCATGTGAAATCA-1	bmcite	1351	579	1399	24	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.606080824335995	5.0	9
+a_GTCATTTGTTGGTTTG-1	bmcite	2497	746	4383	25	HumanHTO2	batch1	T cell	CD8 Naive	0.0454389485234447	0.0	7
+a_CTGCGGATCTACTTAC-1	bmcite	2754	1048	5485	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.256298694459809	6.0	5
+b_CTACACCCAGCTCGAC-1	bmcite	2199	803	5582	25	HumanHTO1	batch2	T cell	CD8 Effector_1	0.188115500863771	1.0	8
+b_CACCTTGGTCAAGCGA-1	bmcite	5776	386	506	25	HumanHTO9	batch2	Progenitor cells	Prog_RBC	0.999999996455823	5.0	9
+b_AGACGTTTCTTGTATC-1	bmcite	1905	679	2396	25	HumanHTO6	batch2	B cell	Naive B	0.0894204419119653	2.0	1
+a_CGCGTTTAGGCAATTA-1	bmcite	1717	590	4796	25	HumanHTO5	batch1	T cell	CD4 Naive	0.438044727048042	8.0	3
+a_GTATTCTAGACAAAGG-1	bmcite	8031	2100	706	25	HumanHTO7	batch1	Progenitor cells	Prog_RBC	0.554247960618285	3.0	0
+a_CGGACGTCACTTACGA-1	bmcite	1263	470	4603	24	HumanHTO10	batch1	T cell	CD8 Naive	0.0889417380068424	1.0	9
+b_CTACATTAGTTACGGG-1	bmcite	2306	764	2249	25	HumanHTO10	batch2	B cell	Naive B	0.418770924676896	2.0	1
+a_CACAGTACAGGAATCG-1	bmcite	1496	708	3349	25	HumanHTO1	batch1	T cell	CD8 Memory_2	0.48974344403023	1.0	9
+b_CGCGGTAGTGCAACGA-1	bmcite	2217	925	5861	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.40667657240433	6.0	5
+b_ACTGTCCGTTATCGGT-1	bmcite	2593	822	8720	25	HumanHTO8	batch2	T cell	CD8 Memory_1	0.491460509943522	0.0	7
+b_TCAACGATCTTGCATT-1	bmcite	2137	630	1329	24	HumanHTO1	batch2	B cell	Memory B	0.518918013058558	2.0	9
+b_CACAGTAGTTCGGGCT-1	bmcite	4564	993	4095	25	HumanHTO3	batch2	B cell	Memory B	0.508174124203651	2.0	1
+b_CTGGTCTAGATATACG-1	bmcite	3565	1087	9121	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.167057789834775	2.0	4
+b_CTTCTCTGTACGCACC-1	bmcite	1736	739	6076	25	HumanHTO9	batch2	T cell	CD8 Memory_2	0.0114482247277674	1.0	7
+b_AGATTGCCATATGGTC-1	bmcite	12427	2523	2608	25	HumanHTO3	batch2	Progenitor cells	LMPP	0.704700611275804	3.0	0
+a_GCAGCCAAGGACATTA-1	bmcite	2488	814	4531	25	HumanHTO2	batch1	T cell	CD8 Effector_2	0.139008784852406	1.0	7
+b_ACAGCTAGTTGTTTGG-1	bmcite	2189	693	7588	25	HumanHTO3	batch2	T cell	CD8 Naive	0.13280589335841	0.0	7
+b_CGACCTTTCCGTAGGC-1	bmcite	4173	1340	5025	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.479771954003309	6.0	5
+a_ATCATGGTCCACGAAT-1	bmcite	1660	739	8767	25	HumanHTO6	batch1	T cell	CD8 Effector_2	0.494150134495232	1.0	9
+b_AGTCTTTGTAACGTTC-1	bmcite	2587	971	5219	25	HumanHTO5	batch2	T cell	gdT	0.403094441402978	9.0	3
+a_ACTGCTCTCGTTTATC-1	bmcite	2711	688	6181	25	HumanHTO3	batch1	T cell	CD4 Naive	0.298234172572891	4.0	3
+a_GGTATTGGTCCCGACA-1	bmcite	1050	517	3332	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.652954061682893	5.0	5
+b_ACCCACTCAGCAGTTT-1	bmcite	2454	990	9401	25	HumanHTO2	batch2	T cell	CD8 Effector_2	0.491395963708134	1.0	8
+b_TCAATCTAGGCATTGG-1	bmcite	2941	813	8567	25	HumanHTO8	batch2	T cell	CD8 Naive	0.0001196008665698	0.0	7
+b_GACGTGCTCAAACCGT-1	bmcite	3248	880	5749	25	HumanHTO5	batch2	T cell	CD4 Memory	0.20429435706125	9.0	3
+a_CTTAACTCAATGGAGC-1	bmcite	2708	989	4471	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.448202110043575	6.0	5
+b_GTTTCTACACACCGAC-1	bmcite	1439	541	1579	25	HumanHTO3	batch2	B cell	Naive B	0.294758523855775	2.0	9
+b_AGAGCTTCACTTCGAA-1	bmcite	2012	668	1555	25	HumanHTO1	batch2	B cell	Naive B	0.31111695704543	2.0	9
+b_ATCCGAATCAATCTCT-1	bmcite	4109	1121	6889	25	HumanHTO8	batch2	T cell	CD4 Memory	0.548434690638223	9.0	3
+a_CTGGTCTCATAGTAAG-1	bmcite	2382	735	5354	25	HumanHTO8	batch1	T cell	CD4 Naive	0.157333830000122	4.0	3
+b_TCGAGGCGTGATGTGG-1	bmcite	2557	804	7056	25	HumanHTO4	batch2	T cell	CD8 Memory_1	0.0522987142322724	1.0	7
+a_AACCATGAGCCCTAAT-1	bmcite	1045	469	2404	23	HumanHTO7	batch1	T cell	CD8 Memory_2	0.0467785755552097	1.0	9
+b_CGTCTACAGCTAGTGG-1	bmcite	4681	1450	4879	25	HumanHTO1	batch2	Mono/DC	cDC2	0.584332901637685	6.0	5
+a_CACAGTATCTCAAGTG-1	bmcite	2440	694	8669	25	HumanHTO1	batch1	T cell	CD8 Naive	0.219897480170146	0.0	7
+a_AGACGTTCACGACTCG-1	bmcite	1562	618	4284	25	HumanHTO1	batch1	T cell	gdT	0.433892816065045	1.0	7
+a_CGCCAAGAGTTGAGAT-1	bmcite	2506	740	8689	25	HumanHTO8	batch1	T cell	CD8 Naive	0.297574191105179	0.0	7
+b_TACGGATAGCGCTCCA-1	bmcite	1823	719	5889	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.571978640465639	5.0	5
+a_TCGCGTTAGCCAGTAG-1	bmcite	1429	782	5143	25	HumanHTO2	batch1	NK	NK	0.0276896782107172	7.0	8
+b_CTCGGAGTCCTCATTA-1	bmcite	1199	546	3968	25	HumanHTO8	batch2	T cell	gdT	0.409748964604033	8.0	9
+b_ATTCTACGTGCAGTAG-1	bmcite	1798	617	2218	25	HumanHTO2	batch2	B cell	Memory B	0.470440132923639	2.0	1
+b_CACCACTTCGGCCGAT-1	bmcite	4591	1049	5477	25	HumanHTO6	batch2	T cell	CD8 Naive	0.369689469971331	0.0	7
+a_CCCAATCTCGTCTGAA-1	bmcite	2179	716	3323	25	HumanHTO10	batch1	B cell	Memory B	0.372144996576224	2.0	1
+b_GGGCATCTCTAGAGTC-1	bmcite	2481	665	3581	25	HumanHTO2	batch2	T cell	Treg	0.244696251844991	4.0	3
+b_CAGCGACAGGACAGCT-1	bmcite	2652	752	4618	25	HumanHTO6	batch2	T cell	CD4 Naive	0.10002190957875	4.0	3
+b_CGTGAGCTCACGAAGG-1	bmcite	3568	1244	6048	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.312204929138188	6.0	5
+b_CGAGCCAAGTAACCCT-1	bmcite	2316	837	8169	25	HumanHTO8	batch2	T cell	CD8 Effector_1	0.324120003503774	1.0	8
+b_CAACTAGGTCGCTTCT-1	bmcite	1611	719	3085	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.432501308016742	5.0	5
+a_AGGTCCGGTAATAGCA-1	bmcite	1202	408	2857	25	HumanHTO5	batch1	T cell	CD4 Naive	0.178995220270131	8.0	9
+b_TGGACGCTCCCAAGAT-1	bmcite	1828	801	4131	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.428455564061014	5.0	5
+a_CACTCCAAGGTAGCTG-1	bmcite	2065	795	5078	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.39632933291045	6.0	5
+b_CTCTACGTCCGTCATC-1	bmcite	2144	882	3676	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.46149746233062	5.0	5
+b_TACACGAGTGTAATGA-1	bmcite	3311	898	5049	25	HumanHTO9	batch2	T cell	CD4 Naive	0.386287321870562	4.0	3
+b_GGGCACTGTGGTGTAG-1	bmcite	1187	591	3979	25	HumanHTO3	batch2	NK	NK	0.394293085988754	7.0	8
+a_AGTGTCAAGCTAGCCC-1	bmcite	1971	693	2461	25	HumanHTO8	batch1	B cell	Naive B	0.346756223501555	2.0	1
+b_TGTTCCGAGGGTGTGT-1	bmcite	2156	669	3962	25	HumanHTO8	batch2	T cell	CD4 Naive	0.421627838016442	8.0	3
+b_CCATTCGAGGCAAAGA-1	bmcite	2595	1003	7814	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.129630687602614	6.0	5
+a_ACACCAAAGTTCGCGC-1	bmcite	2531	637	6206	25	HumanHTO10	batch1	T cell	CD8 Naive	0.161433642324282	0.0	7
+a_TGCTGCTGTCAAAGAT-1	bmcite	2049	797	8374	25	HumanHTO7	batch1	T cell	CD8 Effector_1	0.179667141340457	1.0	7
+a_ACAGCCGAGTCCCACG-1	bmcite	1172	544	6888	24	HumanHTO9	batch1	T cell	CD8 Effector_1	0.203751618795346	7.0	8
+a_GGCGACTTCGTGTAGT-1	bmcite	1443	526	1130	25	HumanHTO4	batch1	B cell	Memory B	0.26073263687184	2.0	9
+a_CGAACATTCAGAAATG-1	bmcite	1740	715	8076	25	HumanHTO10	batch1	T cell	gdT	0.360174771746628	8.0	9
+b_AGCTTGAGTCCAAGTT-1	bmcite	1451	447	800	25	HumanHTO1	batch2	B cell	Memory B	0.541451041528931	2.0	9
+a_TGAGCATAGCAATATG-1	bmcite	3061	1145	5290	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.570934212756715	6.0	5
+b_TTCGAAGCAGTGAGTG-1	bmcite	1705	597	1621	25	HumanHTO6	batch2	T cell	CD4 Memory	0.225322281147406	8.0	9
+a_AGCCTAAGTTGATTCG-1	bmcite	2268	844	4766	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.350071117671598	6.0	5
+a_GGAACTTAGTTCGCAT-1	bmcite	16860	3557	920	25	HumanHTO1	batch1	Progenitor cells	Prog_RBC	0.867001924147845	3.0	2
+a_TGCTACCAGTGCGTGA-1	bmcite	1173	634	6356	25	HumanHTO3	batch1	NK	NK	0.451894255773478	7.0	8
+a_CAGGTGCGTTACCAGT-1	bmcite	1390	568	1517	25	HumanHTO8	batch1	Mono/DC	pDC	0.468417557197722	8.0	9
+b_ACACCCTTCTTATCTG-1	bmcite	3983	1281	4959	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.591816380653894	6.0	5
+a_CCTCAGTTCGTATCAG-1	bmcite	2924	939	7857	25	HumanHTO6	batch1	T cell	Treg	0.415592612846656	9.0	3
+a_CTACACCAGAGACTTA-1	bmcite	2228	704	3086	25	HumanHTO7	batch1	T cell	CD4 Naive	0.270932213079433	8.0	9
+b_ATGGGAGCAGCTGCTG-1	bmcite	1442	692	3208	25	HumanHTO6	batch2	Progenitor cells	GMP	0.608799115328132	5.0	9
+a_CACACCTAGTACGACG-1	bmcite	1300	462	3566	25	HumanHTO2	batch1	T cell	CD4 Naive	0.290655173628729	8.0	9
+b_CCTTACGAGAGTGACC-1	bmcite	2332	677	2725	25	HumanHTO1	batch2	B cell	Memory B	0.354326494515786	2.0	1
+a_AAAGATGGTCCTAGCG-1	bmcite	2750	739	7433	25	HumanHTO6	batch1	T cell	CD8 Naive	0.358807405342593	0.0	7
+b_GGGATGATCAACACGT-1	bmcite	1631	573	4115	24	HumanHTO1	batch2	T cell	CD4 Memory	0.260037296265539	8.0	3
+a_TACTTACCACGGTAGA-1	bmcite	2591	966	3924	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.39883542562566	6.0	5
+b_CACAAACGTGACGCCT-1	bmcite	2511	863	7663	25	HumanHTO6	batch2	T cell	CD8 Memory_2	0.246492055809144	1.0	7
+b_TGCCCATGTCAATACC-1	bmcite	1197	411	1705	24	HumanHTO6	batch2	T cell	CD4 Memory	0.0331322196907149	8.0	9
+a_CTGAAACAGTCTTGCA-1	bmcite	2098	680	6431	25	HumanHTO4	batch1	T cell	CD8 Naive	0.481091045486634	0.0	7
+a_TCGAGGCCATGCCTTC-1	bmcite	6512	1900	4210	25	HumanHTO1	batch1	Mono/DC	cDC2	0.477866772690528	10.0	0
+a_CTCGTACTCCTTCAAT-1	bmcite	3313	864	3616	25	HumanHTO7	batch1	B cell	Naive B	0.497618260274116	2.0	1
+b_AGATTGCAGTGGACGT-1	bmcite	2231	889	4373	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.326750498873398	5.0	5
+a_CTCGTCATCAGCTCTC-1	bmcite	1333	451	4007	25	HumanHTO6	batch1	T cell	CD4 Naive	0.370792767747482	8.0	3
+b_GGCGACTTCTCTGCTG-1	bmcite	1705	728	3011	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.373322497855532	5.0	5
+b_AAAGCAATCGAATCCA-1	bmcite	3296	1082	4474	25	HumanHTO5	batch2	B cell	Memory B	0.40211476507183	2.0	1
+b_CCGGTAGCACAGACAG-1	bmcite	1133	550	3524	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.46231598541307	5.0	9
+a_ACACTGATCACTCCTG-1	bmcite	2573	774	9562	24	HumanHTO6	batch1	T cell	CD8 Naive	0.568598722375859	0.0	7
+b_TCAGATGTCCGAATGT-1	bmcite	4636	1077	7778	25	HumanHTO2	batch2	T cell	CD8 Naive	0.258672308299719	0.0	7
+b_TACTTACGTTGTGGCC-1	bmcite	4956	1269	5650	25	HumanHTO3	batch2	B cell	Memory B	0.414569969713185	2.0	1
+b_AGTGTCAAGATGTGGC-1	bmcite	2941	1123	5307	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.289519978762451	6.0	5
+a_AGCGTATAGTTGAGAT-1	bmcite	2552	773	9090	25	HumanHTO9	batch1	T cell	CD8 Naive	0.0006640517487924	0.0	7
+b_CACTCCAGTCCTCTTG-1	bmcite	3589	885	5697	25	HumanHTO4	batch2	T cell	CD4 Memory	0.439797632779748	9.0	3
+b_TTAGGACGTTTAGCTG-1	bmcite	1990	879	375	24	HumanHTO9	batch2	Progenitor cells	Prog_B 2	0.670848975590045	2.0	9
+a_AATCCAGTCAGTTGAC-1	bmcite	1198	592	1436	24	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.565175176470544	5.0	9
+a_CTAGAGTAGATATGCA-1	bmcite	7021	1972	3921	25	HumanHTO7	batch1	Mono/DC	cDC2	0.893093095088027	10.0	0
+a_ACTATCTGTAGGCATG-1	bmcite	3941	1300	3950	25	HumanHTO3	batch1	Progenitor cells	GMP	0.453921786328864	5.0	0
+a_TACTCATCATCACGAT-1	bmcite	2042	807	5331	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.462208478943571	6.0	5
+b_TACTCGCCATATGGTC-1	bmcite	2738	799	5834	25	HumanHTO6	batch2	T cell	CD4 Naive	0.0149621526375575	4.0	3
+b_GTGCTTCGTCACTTCC-1	bmcite	1802	638	3142	24	HumanHTO9	batch2	T cell	CD4 Memory	0.331589533061901	8.0	9
+a_CAGCTGGTCGGTCCGA-1	bmcite	1266	446	4933	25	HumanHTO5	batch1	T cell	CD8 Naive	0.510767902568236	1.0	9
+b_ATCGAGTAGCTGTTCA-1	bmcite	1449	499	1697	25	HumanHTO3	batch2	B cell	Naive B	0.530342207118178	2.0	9
+b_CACAGTACAGCTGTTA-1	bmcite	2307	938	5944	25	HumanHTO7	batch2	T cell	CD8 Effector_1	0.140997992030116	1.0	7
+b_GCGGGTTGTGGTACAG-1	bmcite	1352	454	1278	24	HumanHTO7	batch2	T cell	CD4 Memory	0.333474837774775	8.0	9
+a_ATGAGGGAGTTGAGTA-1	bmcite	2089	892	3729	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.511959985064731	5.0	5
+a_GGCGTGTGTCAACTGT-1	bmcite	1098	443	448	24	HumanHTO9	batch1	Progenitor cells	Prog_RBC	0.972998981309476	1.0	9
+b_GATGAGGAGATCGATA-1	bmcite	2210	829	3504	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.484261096090884	5.0	5
+a_CTCGGAGAGAGTGAGA-1	bmcite	1902	610	4287	25	HumanHTO5	batch1	T cell	CD4 Naive	0.195847798789919	8.0	3
+a_CGGAGTCCAAGAGTCG-1	bmcite	3001	809	11387	25	HumanHTO7	batch1	T cell	CD8 Naive	0.38931606613572	0.0	7
+a_AAGGAGCGTTATCCGA-1	bmcite	2544	763	6038	25	HumanHTO10	batch1	T cell	CD4 Naive	0.338615879122532	4.0	3
+a_TTTACTGGTCCGTTAA-1	bmcite	3629	1148	9869	25	HumanHTO8	batch1	Mono/DC	CD16 Mono	0.581964973506353	6.0	10
+b_AGGCCGTTCAAGGCTT-1	bmcite	2634	707	5163	25	HumanHTO8	batch2	T cell	CD4 Naive	0.406735491787255	4.0	3
+a_AGCTCTCAGAGGACGG-1	bmcite	1364	658	6073	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.335631766220628	6.0	5
+b_TTAGGACAGCTAACTC-1	bmcite	2078	746	11677	25	HumanHTO3	batch2	T cell	CD8 Effector_2	0.413748357077322	1.0	7
+a_ATTACTCCACCCATTC-1	bmcite	1730	645	7326	25	HumanHTO5	batch1	T cell	CD8 Effector_1	0.291799347955643	1.0	7
+a_CGTCAGGCATCTATGG-1	bmcite	1862	714	4098	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.395956149131107	5.0	5
+a_TCTTTCCCATGCATGT-1	bmcite	1469	671	2093	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.467969002673903	5.0	9
+a_AACTTTCTCTCTAGGA-1	bmcite	2472	921	4143	25	HumanHTO1	batch1	T cell	MAIT	0.482448583831979	1.0	7
+a_GACGTGCGTTGGAGGT-1	bmcite	1549	529	5728	25	HumanHTO1	batch1	T cell	CD4 Naive	0.457751360582166	8.0	3
+a_GGACGTCAGTGTCTCA-1	bmcite	2038	811	2120	23	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.496925949443648	7.0	9
+a_TCTGGAAAGAAACCTA-1	bmcite	1791	559	5536	25	HumanHTO5	batch1	T cell	CD8 Naive	0.478658857263175	1.0	7
+a_GTACTTTAGCACACAG-1	bmcite	2253	868	6392	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.555647493184456	6.0	5
+a_CTCGAAAAGAGAACAG-1	bmcite	1746	624	3563	25	HumanHTO8	batch1	T cell	CD4 Memory	0.471527144396384	9.0	3
+b_GAGTCCGCAGGCTGAA-1	bmcite	2143	733	411	25	HumanHTO10	batch2	Progenitor cells	LMPP	0.596128179024898	3.0	9
+b_ACTGATGGTATAAACG-1	bmcite	2561	764	4572	24	HumanHTO2	batch2	T cell	CD4 Naive	0.192958446825841	4.0	3
+b_TGAGAGGAGATGGGTC-1	bmcite	1046	526	3763	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.617072862071286	5.0	5
+a_TCAGCTCGTAGTGAAT-1	bmcite	3078	1053	4425	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.481604958357418	6.0	5
+b_TTCTTAGTCGGCGCAT-1	bmcite	3339	791	2471	25	HumanHTO5	batch2	B cell	Memory B	0.259165575208107	2.0	1
+b_TGACAACCAGCTTCGG-1	bmcite	2910	1033	3349	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.540591993607668	5.0	5
+a_AAATGCCAGACGCACA-1	bmcite	1916	724	4378	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.415985763836541	6.0	5
+a_GAACGGAAGCGAAGGG-1	bmcite	1226	558	5739	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.548015165141014	6.0	5
+a_CTAGCCTCAGTATGCT-1	bmcite	1799	748	1677	24	HumanHTO4	batch1	Progenitor cells	GMP	0.630119750427255	3.0	9
+a_TACTTACGTAATTGGA-1	bmcite	2219	879	5103	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.416000560959804	6.0	5
+b_CCACCTAGTAGAGCTG-1	bmcite	2924	1030	2492	25	HumanHTO3	batch2	Progenitor cells	GMP	0.600458244849183	5.0	9
+b_CTTCTCTCATGCTAGT-1	bmcite	1299	657	3588	25	HumanHTO2	batch2	Progenitor cells	GMP	0.610839839694017	5.0	9
+b_TACACGAAGGATGTAT-1	bmcite	4571	1555	9691	25	HumanHTO7	batch2	Mono/DC	CD16 Mono	0.474508393308215	7.0	10
+b_CACCAGGTCTAACTCT-1	bmcite	6160	1460	7300	25	HumanHTO2	batch2	B cell	Memory B	0.420954663430094	2.0	1
+b_GTGAAGGAGTGGAGTC-1	bmcite	2683	770	5562	25	HumanHTO2	batch2	T cell	CD4 Naive	0.300186578694696	9.0	3
+a_CGTAGGCCACATCTTT-1	bmcite	1046	523	3545	24	HumanHTO5	batch1	NK	NK	0.29634658575893	7.0	8
+a_TACGGTATCGCAAACT-1	bmcite	2214	662	5803	25	HumanHTO10	batch1	T cell	CD8 Memory_1	0.315058280612611	9.0	7
+b_CGAGCACGTGTGTGCC-1	bmcite	1655	828	4405	25	HumanHTO1	batch2	NK	NK	0.48193869180011	7.0	8
+a_TGCTACCTCTTAGAGC-1	bmcite	1510	543	4946	25	HumanHTO7	batch1	T cell	CD4 Memory	0.310195713375417	9.0	3
+b_GTCTTCGTCAATACCG-1	bmcite	15680	3016	939	25	HumanHTO10	batch2	Progenitor cells	Prog_RBC	0.651055779218375	3.0	2
+b_CTCTGGTGTTCGCTAA-1	bmcite	3162	606	545	25	HumanHTO10	batch2	Progenitor cells	Prog_RBC	0.870122399100094	5.0	9
+a_ATGTGTGGTACCCAAT-1	bmcite	1021	487	2336	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.397664681303928	5.0	9
+b_CGAGCACGTTAGATGA-1	bmcite	2789	781	7677	25	HumanHTO1	batch2	T cell	CD8 Naive	0.240815949375243	0.0	7
+b_CGGTTAACAATGTAAG-1	bmcite	3017	804	8517	25	HumanHTO8	batch2	T cell	CD8 Naive	0.502227776249158	0.0	7
+a_TTGACTTCAAGCTGTT-1	bmcite	1626	639	4150	24	HumanHTO1	batch1	T cell	MAIT	0.530174433423901	1.0	7
+a_AAAGATGCATTTCAGG-1	bmcite	1058	424	1161	25	HumanHTO8	batch1	B cell	Naive B	0.530736005123354	2.0	9
+b_AGCTCCTTCAGTGTTG-1	bmcite	1633	696	4481	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.48158977074823	5.0	5
+a_CACCTTGCATCTGGTA-1	bmcite	5567	1474	2729	25	HumanHTO8	batch1	Mono/DC	cDC2	0.475457381031117	10.0	0
+b_TCAGGTACATCCGCGA-1	bmcite	4744	1510	17602	25	HumanHTO3	batch2	Mono/DC	CD16 Mono	0.219584189654032	7.0	4
+b_ACACCCTCACAGTCGC-1	bmcite	1984	566	2483	25	HumanHTO5	batch2	B cell	Naive B	0.0908600309708169	2.0	1
+b_CCAATCCGTCATTAGC-1	bmcite	1738	515	2247	25	HumanHTO7	batch2	T cell	CD4 Naive	0.368169659077267	8.0	9
+a_GCAAACTGTGCCTGGT-1	bmcite	3932	875	10224	25	HumanHTO4	batch1	T cell	CD8 Naive	0.57031669976168	0.0	7
+a_TCAGGATCAGATGGGT-1	bmcite	1523	715	3030	25	HumanHTO9	batch1	B cell	Memory B	0.264936568557769	2.0	1
+b_CGAACATCATCTCCCA-1	bmcite	1022	424	980	25	HumanHTO7	batch2	NK	NK	0.132412536014243	1.0	9
+b_GCCTCTAGTCGGCTCA-1	bmcite	2291	905	2892	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.267509352498575	5.0	5
+a_CACACAACACTATCTT-1	bmcite	1417	669	3896	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.578674013175226	5.0	5
+b_TATCAGGTCATCGCTC-1	bmcite	8658	2279	5093	25	HumanHTO2	batch2	Mono/DC	cDC2	0.550588002080302	10.0	0
+a_TGCGTGGTCTTACCTA-1	bmcite	3680	1326	7810	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.544740366037996	6.0	5
+b_TTTATGCGTTCGTTGA-1	bmcite	1330	486	1932	25	HumanHTO4	batch2	T cell	CD4 Memory	0.249324431127912	8.0	9
+a_CACACCTGTACCGTTA-1	bmcite	2163	719	4750	25	HumanHTO6	batch1	T cell	CD4 Naive	0.425919277164563	4.0	3
+b_AACCATGAGCGTAGTG-1	bmcite	2468	966	5472	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.54837785625788	6.0	5
+b_GGGATGATCCGAAGAG-1	bmcite	3130	871	4961	25	HumanHTO6	batch2	T cell	CD4 Naive	0.301834746640096	9.0	3
+a_GGACATTTCAAGGCTT-1	bmcite	1780	721	3171	24	HumanHTO5	batch1	T cell	MAIT	0.503552822307	1.0	7
+a_AGTGGGACACACTGCG-1	bmcite	1061	483	2779	25	HumanHTO5	batch1	Progenitor cells	GMP	0.824949017437656	5.0	9
+a_ACTGATGTCCTCAATT-1	bmcite	3087	1065	4252	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.506899405622601	6.0	5
+a_ATTTCTGGTCCTGCTT-1	bmcite	4556	1831	10811	25	HumanHTO2	batch1	T cell	CD4 Memory	0.41687584231573	9.0	3
+a_TCTCATAGTAGCTTGT-1	bmcite	1320	565	4102	25	HumanHTO6	batch1	T cell	CD8 Memory_2	0.396797667982434	1.0	9
+a_ACCTTTAAGTGGTAGC-1	bmcite	3318	1124	3597	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.355634434086172	6.0	5
+a_CGAGCACGTCTAGTCA-1	bmcite	1953	748	3928	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.754695205897785	5.0	5
+a_GCGCAACCATATGAGA-1	bmcite	2950	898	4855	25	HumanHTO10	batch1	T cell	CD4 Memory	0.345824012433704	9.0	3
+a_AAGCCGCCAAGAGTCG-1	bmcite	1135	506	1995	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.509970582002025	5.0	9
+a_GTGTGCGGTACCGCTG-1	bmcite	3004	1029	4827	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.378721313787347	6.0	5
+b_CTGATCCTCGCGTTTC-1	bmcite	2317	984	7905	25	HumanHTO10	batch2	NK	NK	0.135246956899416	7.0	8
+b_ACCGTAAGTCCGAAGA-1	bmcite	3328	1040	4207	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.387668834538214	6.0	5
+a_CAGCTAACAGACACTT-1	bmcite	1939	639	5117	25	HumanHTO6	batch1	T cell	CD4 Memory	0.407388979277357	9.0	3
+b_GTGCAGCAGAGAGCTC-1	bmcite	3085	738	9236	25	HumanHTO1	batch2	T cell	CD8 Naive	0.338112060631316	0.0	7
+a_CCTTCCCCAGGGCATA-1	bmcite	2207	663	5636	25	HumanHTO8	batch1	T cell	CD4 Naive	0.369531107641526	4.0	3
+b_CGATGTATCTCCAGGG-1	bmcite	1025	426	3178	25	HumanHTO7	batch2	T cell	CD4 Naive	0.382764931902276	8.0	9
+b_TCATTTGCAAGTTAAG-1	bmcite	3029	919	4998	25	HumanHTO1	batch2	T cell	CD4 Memory	0.107388083750573	9.0	3
+b_GGCCGATAGACTAGAT-1	bmcite	3659	1176	5880	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.503863953977436	6.0	5
+a_TATCTCATCCCACTTG-1	bmcite	2909	895	5885	25	HumanHTO9	batch1	T cell	CD4 Naive	0.216246221823468	4.0	3
+b_AACTCTTCAAGGCTCC-1	bmcite	1454	637	3508	25	HumanHTO1	batch2	T cell	CD8 Memory_2	0.343423293451112	1.0	9
+a_GAGCAGAAGGGAGTAA-1	bmcite	1836	516	2999	25	HumanHTO7	batch1	T cell	CD4 Naive	0.499560587128117	8.0	9
+a_AGGCCGTCAATAAGCA-1	bmcite	1725	694	4982	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.594227623548963	5.0	5
+a_TCTGAGAAGATGTAAC-1	bmcite	3448	1191	4049	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.454141351480629	6.0	5
+a_GAATGAACAGCCAGAA-1	bmcite	1724	554	3018	24	HumanHTO5	batch1	T cell	CD4 Naive	0.391611129632607	8.0	9
+a_ACTGCTCCATCACGAT-1	bmcite	2453	1077	1819	25	HumanHTO7	batch1	NK	NK	0.551668225390373	1.0	7
+b_AAACGGGTCTTCTGGC-1	bmcite	3686	895	5913	25	HumanHTO7	batch2	T cell	CD8 Naive	0.435828647039349	0.0	7
+b_AGTCTTTCATTTGCCC-1	bmcite	3704	985	7660	24	HumanHTO3	batch2	T cell	CD8 Naive	0.0942700435492683	0.0	7
+a_TCGCGAGGTTGGGACA-1	bmcite	2574	733	2862	25	HumanHTO10	batch1	B cell	Memory B	0.573309667387255	2.0	1
+a_AATCGGTAGACAGACC-1	bmcite	1023	592	1005	25	HumanHTO4	batch1	Progenitor cells	GMP	0.764973047363983	5.0	9
+b_CGGTTAATCCCACTTG-1	bmcite	1366	638	3850	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.485286195530318	5.0	5
+a_CTCTGGTTCCTAGTGA-1	bmcite	5627	1846	9736	25	HumanHTO6	batch1	NK	NK	0.225738134061308	5.0	0
+a_CAGCTAAAGACCACGA-1	bmcite	1192	442	3839	24	HumanHTO5	batch1	T cell	CD4 Naive	0.261968822251255	8.0	9
+a_GCAGCCAGTAGCACGA-1	bmcite	1384	555	4662	25	HumanHTO5	batch1	T cell	CD4 Memory	0.252481460436492	9.0	3
+a_CACACCTCAATAGCGG-1	bmcite	2223	761	5840	25	HumanHTO6	batch1	T cell	CD4 Memory	0.0604735126435676	9.0	3
+b_CCAATCCAGAATGTGT-1	bmcite	2150	628	3972	25	HumanHTO10	batch2	T cell	CD4 Naive	0.418598794582086	8.0	3
+b_CCTTCGAGTGCAGACA-1	bmcite	2050	801	6468	25	HumanHTO2	batch2	T cell	CD8 Memory_2	0.0408374299624068	1.0	7
+a_GGTGTTATCGTGGGAA-1	bmcite	2184	908	3850	25	HumanHTO10	batch1	NK	NK	0.415124013744485	7.0	8
+b_TCTCATAGTAGCGATG-1	bmcite	2579	689	6872	25	HumanHTO6	batch2	T cell	CD8 Naive	0.330079910169869	0.0	7
+b_TCGTAGAGTCTCATCC-1	bmcite	2438	641	4290	24	HumanHTO3	batch2	T cell	CD4 Naive	0.33889816952734	4.0	3
+b_CCGTGGAAGTAACCCT-1	bmcite	3187	853	7680	25	HumanHTO8	batch2	T cell	CD8 Naive	0.480237347453366	0.0	7
+b_GGCGTGTGTGTGAATA-1	bmcite	3486	851	8664	25	HumanHTO5	batch2	T cell	CD8 Naive	0.143884024026141	0.0	7
+b_ATTGGTGCAGTCCTTC-1	bmcite	3264	867	5291	25	HumanHTO1	batch2	T cell	CD4 Naive	0.398751910092238	4.0	3
+b_ACACCGGAGCGACGTA-1	bmcite	2400	978	5953	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.492730325445223	6.0	5
+a_TGGCCAGGTTAAGGGC-1	bmcite	3348	1100	2815	25	HumanHTO1	batch1	Mono/DC	pDC	0.484485451463852	3.0	9
+b_GATGAAAAGGTTCCTA-1	bmcite	1047	432	1128	25	HumanHTO5	batch2	B cell	Naive B	0.331602307999023	2.0	9
+b_CATTCGCGTCCGTTAA-1	bmcite	1290	511	5709	25	HumanHTO10	batch2	NK	NK	0.538968423257954	7.0	8
+b_GTGCTTCGTTCCTCCA-1	bmcite	1993	819	2367	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.3277905183534	5.0	5
+b_GTATCTTAGTCGTACT-1	bmcite	1021	526	5973	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.469023930669039	6.0	5
+a_CACCACTCAGGGATTG-1	bmcite	1325	597	3351	24	HumanHTO6	batch1	T cell	CD4 Memory	0.0924857422334067	9.0	3
+a_CTCGAGGGTTGTTTGG-1	bmcite	1401	441	3267	24	HumanHTO8	batch1	B cell	Plasmablast	0.714347978105368	8.0	9
+b_CATTCGCGTTTAGGAA-1	bmcite	3387	1178	9865	25	HumanHTO7	batch2	Mono/DC	CD16 Mono	0.401011301829384	7.0	10
+a_TCAATCTCAAACAACA-1	bmcite	1210	478	1598	24	HumanHTO4	batch1	B cell	Naive B	0.40565067136656	2.0	9
+a_CTAGCCTTCCTAGTGA-1	bmcite	1857	654	2450	25	HumanHTO8	batch1	B cell	Memory B	0.447657731674141	2.0	1
+b_GGTGTTAAGAGGGCTT-1	bmcite	1286	653	2001	23	HumanHTO4	batch2	Mono/DC	cDC2	0.774736863597259	1.0	9
+b_GCGACCAGTAAACACA-1	bmcite	2159	746	6360	25	HumanHTO9	batch2	T cell	CD4 Naive	0.388649731186126	4.0	3
+b_GTGGGTCGTAGCGCTC-1	bmcite	2360	670	6471	25	HumanHTO4	batch2	T cell	CD4 Naive	0.273490920871913	4.0	3
+b_AGGGTGACACCAGGCT-1	bmcite	2366	693	2141	25	HumanHTO5	batch2	B cell	Naive B	0.327550654256183	2.0	1
+a_CGTCTACAGCTCCTTC-1	bmcite	2514	1010	4592	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.42463458392426	6.0	5
+a_TCTCTAAAGTACGTAA-1	bmcite	1986	828	5266	25	HumanHTO5	batch1	NK	NK	0.404721606545541	7.0	8
+b_CAAGTTGGTTGTACAC-1	bmcite	3176	848	9046	25	HumanHTO8	batch2	T cell	CD8 Naive	0.203959719480915	0.0	7
+a_AAGTCTGCATAGGATA-1	bmcite	1389	481	2982	25	HumanHTO4	batch1	T cell	CD4 Naive	0.246252632259695	8.0	9
+b_GCATACACAAGGTTCT-1	bmcite	1095	505	5338	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.475285021952642	6.0	5
+b_CGGCTAGAGACCTAGG-1	bmcite	1149	433	1845	22	HumanHTO1	batch2	T cell	CD4 Naive	0.311913860448426	8.0	9
+b_TACCTTATCGCAAACT-1	bmcite	1664	538	3804	24	HumanHTO8	batch2	T cell	CD8 Naive	0.424732242958868	1.0	9
+b_TCAGCAAAGAAACCTA-1	bmcite	3459	958	8786	25	HumanHTO8	batch2	T cell	CD8 Naive	0.525332472585026	0.0	7
+b_TCTTTCCTCGCATGAT-1	bmcite	1131	484	3085	24	HumanHTO6	batch2	T cell	CD8 Effector_1	0.609131823337953	1.0	9
+b_GTAACTGAGGATATAC-1	bmcite	2903	824	4322	25	HumanHTO3	batch2	T cell	CD4 Naive	0.275309763000692	4.0	3
+b_GACGGCTAGTTCGATC-1	bmcite	3977	1261	5345	25	HumanHTO6	batch2	Mono/DC	CD16 Mono	0.497078413001911	7.0	10
+a_CGACCTTTCGCAGGCT-1	bmcite	2131	692	6662	25	HumanHTO4	batch1	T cell	CD8 Naive	0.0600746421786131	0.0	7
+a_CGTTCTGTCCGCAAGC-1	bmcite	1591	477	3407	25	HumanHTO5	batch1	T cell	CD4 Naive	0.284070372679544	8.0	9
+b_TATTACCCAGACACTT-1	bmcite	33100	5078	4707	25	HumanHTO3	batch2	Progenitor cells	Prog_B 2	0.55193439223633	3.0	2
+a_ATAACGCGTTAAGATG-1	bmcite	2874	730	8068	25	HumanHTO7	batch1	T cell	CD8 Naive	0.218006987891371	0.0	7
+a_AGCCTAATCCGTAGGC-1	bmcite	2315	720	2030	24	HumanHTO6	batch1	B cell	Naive B	0.383507502887171	2.0	1
+a_CAGGTGCTCCTCAATT-1	bmcite	2357	719	8182	25	HumanHTO9	batch1	T cell	CD8 Naive	0.0003625336588045	0.0	7
+a_GAAGCAGAGACAGACC-1	bmcite	17081	3465	701	25	HumanHTO8	batch1	Progenitor cells	Prog_RBC	0.439638647311932	3.0	2
+b_ACGGGCTTCTCAACTT-1	bmcite	3836	1316	5299	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.459629407248315	6.0	5
+a_GTTCTCGCACCATCCT-1	bmcite	1545	479	4070	25	HumanHTO9	batch1	T cell	CD4 Naive	0.452855036661501	8.0	3
+b_TTCTTAGCAGACGCCT-1	bmcite	3433	893	6368	25	HumanHTO8	batch2	T cell	CD8 Naive	0.0058880195838983	0.0	7
+b_GCACTCTTCAAACCAC-1	bmcite	1045	541	2618	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.714098851818838	5.0	9
+a_GATTCAGTCAGTGTTG-1	bmcite	2778	845	5421	24	HumanHTO9	batch1	T cell	CD4 Naive	0.569762102724343		3
+a_TAGTTGGAGCTGAAAT-1	bmcite	2537	958	4198	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.506183931999928	6.0	5
+b_GTGCGGTTCGCCTGTT-1	bmcite	1117	573	2168	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.2734641798567	5.0	5
+a_CCTTCGATCAGCATGT-1	bmcite	3131	1097	4851	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.494196720794296	6.0	5
+a_CTCGTACAGTGTCCCG-1	bmcite	2304	667	6651	25	HumanHTO2	batch1	T cell	CD4 Naive	0.241010480301188	4.0	3
+b_ACTTGTTCATAAGACA-1	bmcite	1142	434	3411	23	HumanHTO2	batch2	T cell	CD8 Naive	3.55860297608758e-05	1.0	9
+b_GTAACGTTCAAACCAC-1	bmcite	17836	3112	2542	25	HumanHTO3	batch2	Progenitor cells	GMP	0.607424131063091	3.0	2
+b_ACTGTCCCAGATCCAT-1	bmcite	1196	501	1095	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.556434840499668	5.0	9
+b_ACTTTCAAGTGCTGCC-1	bmcite	6588	1652	525	25	HumanHTO2	batch2	Progenitor cells	LMPP	0.648332879228654	3.0	0
+a_AAGGCAGAGGTGTTAA-1	bmcite	3056	802	5603	25	HumanHTO9	batch1	T cell	CD4 Naive	0.339737962156757	4.0	3
+b_CCCTCCTGTCGCTTTC-1	bmcite	1925	845	3178	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.523568823476992	5.0	5
+b_GTGTTAGGTGTGCGTC-1	bmcite	3078	1068	4579	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.448964989534073	6.0	5
+b_TACAGTGGTTGGTTTG-1	bmcite	1640	816	4606	25	HumanHTO3	batch2	NK	NK	0.457337169851144	7.0	8
+b_ACTTGTTAGAGAACAG-1	bmcite	7058	1592	1466	25	HumanHTO9	batch2	Progenitor cells	GMP	0.337563716839369	3.0	0
+b_ACCCACTCAAGTTGTC-1	bmcite	3126	816	5943	25	HumanHTO1	batch2	T cell	CD8 Naive	0.433166030065796	0.0	7
+b_CCTAAAGGTCACACGC-1	bmcite	25226	4230	520	25	HumanHTO7	batch2	Progenitor cells	Prog_RBC	0.975523052450567	3.0	2
+b_ATCATGGGTAAACGCG-1	bmcite	3156	1049	4523	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.441979518654828	6.0	5
+b_AACTCAGCAGACGTAG-1	bmcite	1009	477	4405	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.490770208966407	5.0	5
+a_GATGAGGGTTTGACAC-1	bmcite	1759	681	2513	25	HumanHTO6	batch1	B cell	Naive B	0.298071831377129	2.0	1
+a_TAGTTGGAGGTAGCTG-1	bmcite	1028	406	1972	25	HumanHTO9	batch1	T cell	Treg	0.0471418352002341	8.0	9
+a_GTCACGGGTTGGTTTG-1	bmcite	1765	730	4485	25	HumanHTO9	batch1	NK	NK	0.610326201358594	7.0	8
+a_CTAGAGTAGTCTCCTC-1	bmcite	2057	616	3537	25	HumanHTO7	batch1	T cell	CD4 Naive	0.297731138736042	8.0	9
+b_GATCGTATCCTAGTGA-1	bmcite	3380	950	7501	25	HumanHTO2	batch2	T cell	CD8 Memory_1	0.0377705796207053	0.0	7
+a_CTAACTTTCGGACAAG-1	bmcite	1284	527	4057	25	HumanHTO7	batch1	T cell	CD4 Memory	0.240573623733415	8.0	9
+b_CGGACACCACTGTTAG-1	bmcite	3677	1241	4698	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.551618120760437	6.0	5
+a_CTTACCGAGGTAGCTG-1	bmcite	5649	681	1669	25	HumanHTO2	batch1	B cell	Plasmablast	0.464081601611821	8.0	9
+b_GCTCCTATCTAACTTC-1	bmcite	2819	806	7555	25	HumanHTO2	batch2	T cell	CD8 Naive	0.465217376106285	0.0	7
+b_CCTTCCCGTCTAAACC-1	bmcite	1641	726	2126	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.245974875915899	5.0	9
+b_GGGATGAGTGCAGTAG-1	bmcite	2339	806	3870	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.400547340091944	5.0	5
+b_AGACGTTAGCCACGCT-1	bmcite	4454	1583	4601	25	HumanHTO10	batch2	Mono/DC	pDC	0.520620548455535	6.0	5
+b_CATCAAGTCTTGCCGT-1	bmcite	2325	734	6290	25	HumanHTO6	batch2	T cell	CD8 Memory_1	0.321145761784962	1.0	7
+a_TGCGCAGCAAGTAGTA-1	bmcite	2761	788	7956	25	HumanHTO3	batch1	T cell	CD8 Naive	0.586600019251679	0.0	7
+a_GGGCATCTCAGTCCCT-1	bmcite	3307	955	7723	25	HumanHTO4	batch1	T cell	CD4 Naive	0.402611903480451	4.0	3
+a_TAAGCGTTCGGTCCGA-1	bmcite	2182	731	5688	25	HumanHTO10	batch1	T cell	CD8 Effector_1	0.351661362498682	1.0	8
+b_AGGCCACCATCCTAGA-1	bmcite	3780	922	8197	25	HumanHTO3	batch2	T cell	CD8 Naive	0.49988615087667	0.0	7
+b_ATTCTACAGGCTCATT-1	bmcite	2172	740	1836	24	HumanHTO5	batch2	B cell	Naive B	0.312592013463845	2.0	1
+a_GCATGATTCGAACGGA-1	bmcite	1596	701	3524	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.424132618374705	5.0	9
+a_AGTCTTTGTCAAACTC-1	bmcite	3622	1213	3908	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.461275534565907	6.0	5
+b_CTGTTTACATCTACGA-1	bmcite	6954	1353	8322	25	HumanHTO9	batch2	B cell	Plasmablast	0.558736915106177	3.0	9
+b_TTAACTCTCCGTCATC-1	bmcite	2714	804	6154	25	HumanHTO9	batch2	T cell	CD4 Naive	0.301399190247849	4.0	3
+a_TGGACGCTCTTGTATC-1	bmcite	2029	638	2887	25	HumanHTO8	batch1	B cell	Memory B	0.474308533072453	2.0	1
+a_TCGCGTTGTCAGTGGA-1	bmcite	4355	1292	9346	25	HumanHTO8	batch1	Mono/DC	CD16 Mono	0.35046178679549	7.0	10
+b_CTTCTCTAGCTGAACG-1	bmcite	2769	960	6650	25	HumanHTO8	batch2	T cell	CD8 Memory_2	0.26982883911862	9.0	7
+b_ATCTACTAGGGTTCCC-1	bmcite	3809	1214	4110	25	HumanHTO6	batch2	Progenitor cells	GMP	0.765398428250193	5.0	0
+a_TGTATTCTCCGTTGTC-1	bmcite	1289	702	686	25	HumanHTO8	batch1	Progenitor cells	Prog_B 2	0.588567444967197	3.0	9
+b_AACACGTTCGTACGGC-1	bmcite	5761	1580	6697	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.53596600228362	6.0	5
+a_AAAGATGCACCAGTTA-1	bmcite	1081	598	4637	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.530527818438746	5.0	5
+a_CCGTGGACACAGCCCA-1	bmcite	3359	1136	3898	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.408979399779487	5.0	5
+a_TAAGAGAGTCGATTGT-1	bmcite	1678	498	2438	23	HumanHTO4	batch1	T cell	CD8 Naive	0.0203402459977274	1.0	9
+a_CTCAGAAGTCCGTCAG-1	bmcite	1924	764	6467	25	HumanHTO3	batch1	T cell	gdT	0.395954102443477	9.0	3
+a_GAACATCTCGATAGAA-1	bmcite	9085	2129	11297	25	HumanHTO4	batch1	Progenitor cells	HSC	0.99999999999974	3.0	0
+a_CCATGTCAGCCAACAG-1	bmcite	2130	809	6254	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.339985742517496	6.0	5
+b_ATCATCTGTACGAAAT-1	bmcite	6612	1830	3223	25	HumanHTO2	batch2	Progenitor cells	GMP	0.643547061720415	3.0	0
+b_AGTCTTTGTGAAAGAG-1	bmcite	2309	553	5982	25	HumanHTO3	batch2	T cell	CD4 Naive	0.286248499771323	4.0	3
+b_GAATGAAGTCCAGTTA-1	bmcite	2994	800	6255	25	HumanHTO3	batch2	T cell	CD4 Naive	0.504082801516361	4.0	3
+a_CACACAACATATACGC-1	bmcite	1780	684	1747	25	HumanHTO8	batch1	B cell	Naive B	0.350045960014828	2.0	9
+b_GAACGGAAGACTGTAA-1	bmcite	1864	691	6742	25	HumanHTO2	batch2	T cell	CD8 Effector_2	0.159858039192525	1.0	7
+b_GGCCGATAGCCACCTG-1	bmcite	2566	1044	5276	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.476411919381396	5.0	5
+b_CGTTGGGAGTTCGATC-1	bmcite	5134	1533	10515	25	HumanHTO5	batch2	Mono/DC	CD16 Mono	0.405007014431193	6.0	4
+b_CCACTACGTTCTGAAC-1	bmcite	3006	939	5548	25	HumanHTO6	batch2	T cell	CD8 Naive	0.464328422847778	0.0	7
+a_TACGGTACATCTCCCA-1	bmcite	3590	862	8721	25	HumanHTO9	batch1	T cell	CD8 Naive	0.418184990374053	0.0	7
+a_TGGTTCCGTTGGACCC-1	bmcite	1918	820	4921	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.444832317251976	6.0	5
+b_GGAGCAAAGAGCTGGT-1	bmcite	5577	1206	6719	25	HumanHTO10	batch2	B cell	Memory B	0.103198690395544	2.0	1
+b_TGCCCATGTTCAACCA-1	bmcite	1753	551	3711	25	HumanHTO2	batch2	T cell	CD8 Naive	0.193513667988404	1.0	9
+b_TACTTGTAGACAATAC-1	bmcite	1257	578	4678	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.412894120787519	5.0	5
+b_AGCATACGTACGAAAT-1	bmcite	2895	880	4101	25	HumanHTO10	batch2	T cell	CD4 Memory	0.165478440553938	4.0	3
+b_CGATGGCGTGCACCAC-1	bmcite	2395	679	5920	25	HumanHTO5	batch2	T cell	CD4 Naive	0.430695625993392	4.0	3
+b_AGCTCTCGTCCGAGTC-1	bmcite	1025	363	1696	24	HumanHTO10	batch2	T cell	CD4 Naive	0.159550772252188	8.0	9
+a_AAGCCGCAGCGTGAAC-1	bmcite	2070	783	2867	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.147744229489561	5.0	5
+a_TTGTAGGGTCGTTGTA-1	bmcite	1709	851	5859	25	HumanHTO9	batch1	NK	NK	0.356474541850234	7.0	8
+b_CGCGGTAAGATCCCGC-1	bmcite	39905	4577	11862	25	HumanHTO7	batch2	Progenitor cells	Prog_RBC	1.0	7.0	6
+a_ACGAGGAAGAGTACAT-1	bmcite	2579	1008	7506	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.498771014713923	6.0	5
+a_CTCTAATCACAGTCGC-1	bmcite	1178	478	1406	25	HumanHTO7	batch1	T cell	CD4 Naive	0.110248847766336	8.0	9
+a_ACCCACTCAATGAAAC-1	bmcite	2653	1033	3067	25	HumanHTO10	batch1	Progenitor cells	GMP	0.450914533850939	5.0	9
+b_CTCTAATGTGTCCTCT-1	bmcite	3728	1126	5987	25	HumanHTO8	batch2	T cell	MAIT	0.44192474019511	1.0	7
+a_ATTATCCGTGCCTGTG-1	bmcite	1736	679	3232	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.544869657919688	5.0	5
+a_ACGGGTCTCGCAAGCC-1	bmcite	1974	625	6630	25	HumanHTO10	batch1	T cell	CD4 Naive	0.448782401459937	4.0	3
+b_CTAACTTGTGAGGGTT-1	bmcite	1017	403	928	24	HumanHTO9	batch2	B cell	Naive B	0.558644270770338	2.0	9
+a_TATCAGGAGGGCTTGA-1	bmcite	2057	811	4475	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.433035879661834	5.0	5
+a_TTTGTCAGTATTCTCT-1	bmcite	2506	964	6947	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.470848973828344	6.0	5
+a_CTAACTTAGGCATGTG-1	bmcite	2313	957	3840	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.275714152334912	5.0	5
+a_ATAAGAGCATTAACCG-1	bmcite	1122	452	2956	24	HumanHTO5	batch1	T cell	CD8 Naive	0.079469931491347	1.0	9
+a_TATTACCAGGCCCTCA-1	bmcite	1600	585	1719	25	HumanHTO5	batch1	B cell	Naive B	0.365528225493166	2.0	9
+a_AGCTTGAAGTCCGGTC-1	bmcite	1401	568	1848	25	HumanHTO10	batch1	B cell	Naive B	0.416839337777897	2.0	9
+b_ACGAGGAGTGGCCCTA-1	bmcite	1224	585	2075	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.424693904376845	5.0	5
+b_ACATCAGAGTCCAGGA-1	bmcite	1971	669	2905	25	HumanHTO9	batch2	T cell	gdT	0.240691840320825	8.0	7
+b_CACATTTTCTCGATGA-1	bmcite	1063	545	5047	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.573276477185399	6.0	5
+b_TCAGATGTCCTGTACC-1	bmcite	1874	733	8822	25	HumanHTO4	batch2	T cell	CD8 Effector_2	0.529114118422649	1.0	7
+a_GCACTCTTCCTCGCAT-1	bmcite	2579	762	3615	25	HumanHTO8	batch1	T cell	CD8 Naive	0.520043468327473	0.0	7
+b_TACCTATTCCTAAGTG-1	bmcite	2233	751	2299	25	HumanHTO1	batch2	B cell	Naive B	0.42520963517192	2.0	1
+a_AGTGAGGCAGGCTCAC-1	bmcite	7812	1626	19327	25	HumanHTO9	batch1	T cell	CD8 Naive	0.259108610043792	0.0	7
+b_GCATACAAGCTGTTCA-1	bmcite	2389	992	4553	25	HumanHTO2	batch2	NK	NK	0.542417526598425	7.0	8
+a_CTAGAGTGTAATCACC-1	bmcite	2051	683	2965	25	HumanHTO2	batch1	B cell	Memory B	0.157248346255207	2.0	1
+a_GGGAATGTCAGGATCT-1	bmcite	1745	693	2129	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.623838985838847	5.0	5
+b_CGAGCACGTCCGAATT-1	bmcite	6786	1705	1393	25	HumanHTO7	batch2	Progenitor cells	Prog_Mk	0.509144708173581	3.0	0
+b_CTGCCTAGTATATGGA-1	bmcite	1637	542	3055	24	HumanHTO8	batch2	T cell	CD8 Naive	0.0002093994990445	1.0	9
+b_AGCGGTCAGTGGGATC-1	bmcite	2623	714	5113	25	HumanHTO9	batch2	T cell	CD4 Naive	0.0354857369293474	4.0	3
+b_GTACGTACAGACGTAG-1	bmcite	11399	1902	13386	25	HumanHTO7	batch2	B cell	Plasmablast	0.761376204044237	3.0	3
+b_CAGAATCCACTGAAGG-1	bmcite	3500	1016	4663	25	HumanHTO4	batch2	B cell	Naive B	0.0167411507484131	2.0	1
+b_CTACATTTCCGCGGTA-1	bmcite	1534	677	4287	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.546418107888492	5.0	5
+b_GGATTACAGATATGCA-1	bmcite	2158	778	6848	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.352520092828047	6.0	5
+a_GACTGCGAGGCTCAGA-1	bmcite	1829	779	3603	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.298301292996738	5.0	5
+a_GCAAACTGTTCGAATC-1	bmcite	2668	895	3700	25	HumanHTO1	batch1	Progenitor cells	GMP	0.61959029745317	5.0	0
+b_GGGCACTTCCAATGGT-1	bmcite	2233	726	1791	25	HumanHTO1	batch2	B cell	Naive B	0.422022093496349	2.0	1
+b_CCACCTAGTCACAAGG-1	bmcite	1213	508	4177	25	HumanHTO3	batch2	T cell	CD8 Effector_1	0.248866602829178	1.0	9
+b_GCATACATCGGATGTT-1	bmcite	2689	798	3070	25	HumanHTO1	batch2	B cell	Memory B	0.54841292807352	2.0	1
+a_TAGAGCTTCGCATGAT-1	bmcite	1457	489	3635	25	HumanHTO7	batch1	T cell	CD4 Naive	0.125085475659813	8.0	9
+a_CTAGTGAGTAGATTAG-1	bmcite	2933	1103	4179	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.365402972761134	6.0	5
+a_ACGGCCATCGCGTAGC-1	bmcite	1951	591	2184	25	HumanHTO8	batch1	B cell	Naive B	0.282329668205829	2.0	1
+b_AGTGTCAGTAGGCTGA-1	bmcite	2962	915	4979	25	HumanHTO3	batch2	T cell	CD4 Memory	0.405636735369409	9.0	3
+b_ATGGGAGGTGTCTGAT-1	bmcite	3588	1241	5120	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.450467957434999	6.0	5
+b_TCCACACGTGAGTGAC-1	bmcite	7256	1900	3511	25	HumanHTO2	batch2	Mono/DC	cDC2	0.473200129735026	10.0	0
+b_TCTCTAACAACACCCG-1	bmcite	1937	595	2743	25	HumanHTO1	batch2	B cell	Naive B	0.020087316139877	2.0	1
+b_ATCCACCGTGAACCTT-1	bmcite	1953	836	5679	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.498009637495579	6.0	5
+b_GGGACCTTCTGTACGA-1	bmcite	1949	877	7828	25	HumanHTO6	batch2	NK	NK	0.408003147617057	2.0	8
+a_TAGGCATCAATGGAAT-1	bmcite	1486	760	2047	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.522440189657904	5.0	9
+a_TCTGGAAGTGATGATA-1	bmcite	2000	641	6289	25	HumanHTO7	batch1	T cell	CD4 Naive	0.114786365457889	4.0	3
+b_TACCTATGTTTGCATG-1	bmcite	1999	861	5811	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.344051091444484	6.0	5
+a_CGGACTGGTTTAGGAA-1	bmcite	1049	412	4402	25	HumanHTO2	batch1	T cell	CD4 Naive	0.408644925114933	8.0	9
+a_ACGTCAATCAGGCGAA-1	bmcite	2443	754	1539	25	HumanHTO2	batch1	B cell	Memory B	0.456092700297059	2.0	9
+a_TAGTTGGGTCTCCACT-1	bmcite	2106	611	4921	25	HumanHTO2	batch1	T cell	CD4 Naive	0.376480389592068	8.0	3
+b_AGATTGCGTTCGGCAC-1	bmcite	1365	468	2644	25	HumanHTO4	batch2	T cell	CD4 Naive	0.42210836572495	8.0	9
+b_GGGTCTGAGATATGGT-1	bmcite	4620	1902	1485	25	HumanHTO3	batch2	Progenitor cells	Prog_Mk	0.624433405137987	7.0	8
+a_CAAGGCCTCCGTAGTA-1	bmcite	2259	872	826	25	HumanHTO4	batch1	Progenitor cells	Prog_RBC	0.753868861733778	3.0	9
+a_CTAACTTGTAGAGTGC-1	bmcite	1603	671	1380	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.283046458928329	5.0	9
+a_TACGGATGTGTCTGAT-1	bmcite	2831	720	4287	25	HumanHTO8	batch1	T cell	CD4 Naive	0.419039182375897	4.0	3
+a_GGATGTTGTGATGTGG-1	bmcite	1096	449	4134	24	HumanHTO4	batch1	T cell	CD8 Naive	0.56542596192359	1.0	9
+b_CTCGTACGTTCAGCGC-1	bmcite	24400	4293	991	25	HumanHTO6	batch2	Progenitor cells	Prog_RBC	0.73430850050716	3.0	2
+b_TACTTACCACGGTGTC-1	bmcite	3707	915	5461	25	HumanHTO8	batch2	T cell	CD4 Naive	0.425644052771669	9.0	3
+a_CCGTTCATCCTGCAGG-1	bmcite	1368	630	6631	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.564747311087969	6.0	5
+b_TCAGGATGTCTCCCTA-1	bmcite	1932	584	5274	25	HumanHTO1	batch2	T cell	CD8 Naive	0.355199402570323	1.0	7
+b_ATCATGGAGTGGGATC-1	bmcite	1927	772	11639	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.498823274277348	6.0	5
+b_CGTTCTGAGCGATATA-1	bmcite	2602	680	8197	25	HumanHTO8	batch2	T cell	CD8 Naive	0.201136882036202	0.0	7
+b_TGTGGTAAGCTAACTC-1	bmcite	2977	747	4539	25	HumanHTO10	batch2	T cell	CD4 Naive	0.298890236547878	4.0	3
+b_CAGATCAAGAGGTTGC-1	bmcite	1599	738	3728	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.480625529797036	6.0	5
+b_TCCCGATTCCAGATCA-1	bmcite	3740	924	6630	25	HumanHTO5	batch2	T cell	CD8 Naive	0.519937087879323	0.0	7
+b_GGATTACCACATTTCT-1	bmcite	4081	1246	3499	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.565166216351082	6.0	5
+a_CTCAGAAGTAGAGTGC-1	bmcite	3348	1083	6832	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.419706909228366	6.0	5
+b_TGGTTCCTCACTGGGC-1	bmcite	4066	1006	4577	25	HumanHTO8	batch2	T cell	CD8 Naive	0.504583116877136	0.0	7
+b_AGGGAGTTCATCTGTT-1	bmcite	5927	1503	866	25	HumanHTO4	batch2	Progenitor cells	HSC	0.524112687389378	3.0	0
+a_AATCGGTTCATAGCAC-1	bmcite	3005	993	5153	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.33167826259181	6.0	5
+a_GGACGTCTCGTACGGC-1	bmcite	2500	766	6535	25	HumanHTO6	batch1	T cell	CD4 Memory	0.294298903115515	9.0	3
+a_AGCTCTCGTACAAGTA-1	bmcite	3526	927	10493	25	HumanHTO2	batch1	T cell	CD8 Naive	0.583332557505536	0.0	7
+a_CCGTTCAGTGAGCGAT-1	bmcite	1995	636	4718	25	HumanHTO3	batch1	T cell	CD4 Naive	0.31998138019106	4.0	3
+a_GCTCCTAGTAGCTAAA-1	bmcite	1733	577	3778	25	HumanHTO10	batch1	T cell	CD4 Memory	0.0629169186634195	9.0	3
+b_CAAGAAAGTGTAATGA-1	bmcite	2986	782	6380	25	HumanHTO3	batch2	T cell	CD8 Naive	0.308738355567949	0.0	7
+a_TGAGGGATCCTGTACC-1	bmcite	2630	701	5340	25	HumanHTO8	batch1	T cell	CD4 Naive	0.263935788308676	4.0	3
+b_CGGACTGAGCTTTGGT-1	bmcite	2633	731	5769	25	HumanHTO4	batch2	T cell	CD4 Naive	1.5146173805547402e-09	4.0	3
+a_AAGTCTGTCAGCCTAA-1	bmcite	2607	801	5829	25	HumanHTO10	batch1	T cell	CD4 Naive	0.556971598940624	4.0	3
+b_AACACGTAGGACAGCT-1	bmcite	6696	1924	5506	25	HumanHTO4	batch2	Mono/DC	cDC2	0.630720700476128	10.0	0
+a_GTCAAGTCAGGCGATA-1	bmcite	1942	832	5903	25	HumanHTO2	batch1	T cell	CD8 Memory_2	0.518028948791121	1.0	8
+b_TAGTGGTGTTCGTCTC-1	bmcite	2517	685	8505	25	HumanHTO6	batch2	T cell	CD8 Naive	0.464815213436919	0.0	7
+b_CTGCGGAAGTCAAGGC-1	bmcite	2939	870	6967	25	HumanHTO1	batch2	T cell	CD8 Naive	0.263323414817516	0.0	7
+a_CACAGTACACTTGGAT-1	bmcite	2777	756	9468	25	HumanHTO1	batch1	T cell	CD8 Naive	0.448104882535819	0.0	7
+b_GGGTTGCTCTCGAGTA-1	bmcite	1053	407	2071	25	HumanHTO6	batch2	T cell	CD8 Naive	0.0001213265908318	1.0	9
+b_TCAGGTACAAACTGTC-1	bmcite	2322	715	4740	25	HumanHTO9	batch2	B cell	Naive B	0.373441821751997	2.0	1
+a_GCACATAGTTGTCTTT-1	bmcite	3432	905	9234	25	HumanHTO10	batch1	T cell	CD8 Naive	0.203605417547358	0.0	7
+b_ATTGGACTCTCCAGGG-1	bmcite	2882	723	4847	25	HumanHTO9	batch2	T cell	CD4 Naive	0.450938088552669	4.0	3
+b_GTTCTCGAGATGCCTT-1	bmcite	1248	567	2874	24	HumanHTO5	batch2	T cell	CD8 Memory_2	0.129446597129376	1.0	9
+b_CTCTAATAGCAGCCTC-1	bmcite	2932	1023	6314	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.551007512147221	6.0	5
+b_GACTAACGTGCAGTAG-1	bmcite	2311	742	4562	24	HumanHTO2	batch2	T cell	CD8 Naive	0.554546984116582	1.0	7
+b_CTGTGCTGTTAGATGA-1	bmcite	2051	705	4714	25	HumanHTO4	batch2	T cell	MAIT	0.435896410915084	1.0	7
+a_GGCTGGTTCTACTTAC-1	bmcite	1485	653	2060	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.386615511555266	5.0	9
+b_CGTGAGCCACCATCCT-1	bmcite	1257	459	2808	24	HumanHTO1	batch2	T cell	CD4 Naive	0.357615397649392	8.0	9
+b_AGCTCCTGTCCTAGCG-1	bmcite	1561	499	2019	25	HumanHTO6	batch2	T cell	CD4 Naive	0.197293341555321	8.0	9
+b_TACCTTAGTCACCTAA-1	bmcite	3026	849	7375	25	HumanHTO1	batch2	T cell	CD8 Naive	0.761164285915691	0.0	7
+b_TGACTTTTCTCTAAGG-1	bmcite	1907	710	4711	25	HumanHTO4	batch2	T cell	CD8 Effector_1	0.190665056042087	1.0	7
+b_GAAGCAGAGCGTCTAT-1	bmcite	1659	635	2399	25	HumanHTO4	batch2	B cell	Naive B	3.04644579411881e-07	2.0	1
+b_TACCTTAGTTCGCTAA-1	bmcite	2587	721	5914	25	HumanHTO8	batch2	T cell	CD4 Naive	0.361080924736155	4.0	3
+b_TATCAGGGTTTGTGTG-1	bmcite	2740	807	4944	25	HumanHTO5	batch2	T cell	CD4 Memory	0.457353431298117	9.0	3
+a_TAGCCGGGTTCCGGCA-1	bmcite	1996	791	3172	25	HumanHTO6	batch1	Progenitor cells	Prog_RBC	1.0	5.0	5
+b_TCGTACCGTACAGTGG-1	bmcite	1590	638	2690	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.344231591899257	2.0	9
+a_TACGGGCCATTCTTAC-1	bmcite	3059	1101	4370	25	HumanHTO7	batch1	Progenitor cells	GMP	0.671183261751812	5.0	0
+a_GCAATCAGTTGCTCCT-1	bmcite	3219	894	8578	25	HumanHTO3	batch1	T cell	CD8 Naive	0.298684849643601	0.0	7
+b_CGTGTCTAGGCTAGAC-1	bmcite	1472	559	2270	25	HumanHTO2	batch2	T cell	MAIT	0.273008615701186	7.0	9
+b_AGACGTTCATTGGCGC-1	bmcite	3666	893	9266	25	HumanHTO2	batch2	T cell	CD8 Naive	0.344344378807004	0.0	7
+b_CCCAATCGTGCAGACA-1	bmcite	1959	634	3439	25	HumanHTO3	batch2	B cell	Naive B	0.0414911719171452	2.0	1
+a_ATTGGACAGTGGTAAT-1	bmcite	1032	391	845	24	HumanHTO4	batch1	B cell	Naive B	0.448027437213446	2.0	9
+b_TCATTACGTATGGTTC-1	bmcite	2723	1060	4447	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.520346767434494	6.0	5
+a_CGTCAGGTCGGATGTT-1	bmcite	2796	984	4544	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.266592100009134	6.0	5
+b_GTATCTTGTACCGCTG-1	bmcite	2642	796	8650	25	HumanHTO5	batch2	T cell	CD4 Memory	0.438184261788094	9.0	3
+b_GGCTCGAGTCATCCCT-1	bmcite	1729	660	1253	25	HumanHTO8	batch2	B cell	Naive B	0.353744498135475	2.0	9
+b_ATCACGACAATCGGTT-1	bmcite	1198	457	3020	24	HumanHTO1	batch2	T cell	CD4 Naive	0.321411639709373	8.0	9
+a_TCAATCTTCAACCAAC-1	bmcite	11741	2747	595	25	HumanHTO7	batch1	Progenitor cells	Prog_RBC	0.564506872256386	3.0	0
+a_TTGGAACGTAGTACCT-1	bmcite	2295	747	2930	25	HumanHTO1	batch1	B cell	Memory B	0.356512362589423	2.0	1
+b_CAGCTGGCAGTAACGG-1	bmcite	1004	433	3174	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.817430708110659	5.0	9
+a_AGCGTATGTTCGCGAC-1	bmcite	2278	638	6675	25	HumanHTO5	batch1	T cell	CD4 Naive	0.485250633576352	4.0	3
+a_CATGACACAGAAGCAC-1	bmcite	2991	837	8603	25	HumanHTO3	batch1	T cell	CD8 Naive	0.340478194680251	0.0	7
+b_CACCACTTCGCAAACT-1	bmcite	1115	447	924	24	HumanHTO5	batch2	B cell	Naive B	0.707353199836215	2.0	9
+b_CCAGCGACATCTCCCA-1	bmcite	13763	2245	528	25	HumanHTO6	batch2	Progenitor cells	Prog_RBC	0.994279240823036	3.0	2
+b_AAACCTGTCCTTTCTC-1	bmcite	3210	895	5107	25	HumanHTO7	batch2	T cell	CD4 Naive	0.304340471344954	4.0	3
+b_CCTAGCTGTCTAGCCG-1	bmcite	2817	978	5094	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.440173456497288	6.0	5
+b_GATGCTACAGATAATG-1	bmcite	3193	1082	6490	25	HumanHTO2	batch2	NK	NK	0.802219610164794	4.0	3
+b_ATCACGATCGTTTAGG-1	bmcite	1373	544	815	25	HumanHTO8	batch2	B cell	Naive B	0.415554642208688	2.0	9
+b_TGCGGGTAGGGTATCG-1	bmcite	1602	697	3545	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.480999179721731	5.0	5
+a_AAGGAGCGTTTCGCTC-1	bmcite	2543	709	6742	25	HumanHTO4	batch1	T cell	CD4 Naive	0.366638429493162	4.0	3
+b_CGTCTACGTAGTACCT-1	bmcite	2869	864	3785	25	HumanHTO2	batch2	T cell	CD4 Memory	0.342726233882139	9.0	3
+a_ATCGAGTCAGGATCGA-1	bmcite	2668	748	4676	25	HumanHTO4	batch1	T cell	CD4 Memory	0.332161845170949	9.0	3
+a_GATGAGGCAATGGAGC-1	bmcite	2792	803	6736	25	HumanHTO5	batch1	T cell	CD4 Memory	0.216063657959589	9.0	3
+a_GCTGCGATCTACTTAC-1	bmcite	1029	410	1710	25	HumanHTO5	batch1	B cell	Naive B	0.355309796403579	2.0	9
+a_TCGTAGACAAGTTAAG-1	bmcite	13182	2770	2352	25	HumanHTO8	batch1	Progenitor cells	LMPP	0.484466459632338	3.0	0
+a_CTCGGGAAGCCTTGAT-1	bmcite	3545	1133	5970	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.559234298187383	6.0	5
+b_AAAGATGAGAAACCGC-1	bmcite	3199	782	8165	25	HumanHTO3	batch2	T cell	CD8 Naive	0.278735458744272	0.0	7
+a_TCGGGACAGGACAGAA-1	bmcite	2217	1039	5826	25	HumanHTO3	batch1	NK	NK	0.490157144681031	7.0	8
+a_CCGGGATAGCTCCCAG-1	bmcite	1480	515	2705	25	HumanHTO9	batch1	B cell	Naive B	0.388495097345282	2.0	1
+a_TGGCGCAGTGACCAAG-1	bmcite	1209	603	2855	24	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.381409008420095	5.0	9
+b_GCGCAACCACATTAGC-1	bmcite	3420	1311	2457	25	HumanHTO3	batch2	NK	CD56 bright NK	0.44410765110881	7.0	8
+b_CATCAGAAGACCTTTG-1	bmcite	1951	644	3021	25	HumanHTO7	batch2	B cell	Naive B	0.0012305463537494	2.0	1
+b_CCGTTCATCGTCTGAA-1	bmcite	2256	734	6079	25	HumanHTO2	batch2	T cell	CD8 Naive	0.433153890771428	1.0	7
+a_GAACCTAAGCTTTGGT-1	bmcite	1967	835	6419	25	HumanHTO9	batch1	NK	NK	0.374359545634368	7.0	8
+a_ACGCAGCTCGGCGGTT-1	bmcite	1522	518	1443	25	HumanHTO5	batch1	B cell	Naive B	0.471338097426871	2.0	9
+a_GACCAATTCTTGACGA-1	bmcite	1776	563	995	23	HumanHTO5	batch1	T cell	CD4 Memory	0.243059538715103	8.0	9
+a_ACCGTAAAGAGATGAG-1	bmcite	2915	818	5714	25	HumanHTO4	batch1	T cell	CD4 Memory	0.17546793160058	9.0	3
+b_ATCTGCCTCAGGTAAA-1	bmcite	1482	643	4001	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.424996457794898	5.0	5
+b_CATATGGAGCGACGTA-1	bmcite	4182	1424	10092	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.758133012198712	7.0	10
+b_CCGGTAGTCACAACGT-1	bmcite	1415	620	4692	25	HumanHTO1	batch2	NK	NK	0.572361859232594	7.0	8
+b_GACTGCGCATACTACG-1	bmcite	2798	761	6544	25	HumanHTO5	batch2	T cell	CD8 Naive	0.0142969630489221	0.0	7
+b_CACAGGCGTCCTCCAT-1	bmcite	2665	717	5740	24	HumanHTO8	batch2	T cell	CD4 Memory	0.350930216648001	9.0	3
+a_CGGACGTAGTTATCGC-1	bmcite	1178	419	3582	25	HumanHTO8	batch1	B cell	Memory B	0.767222220750105	2.0	1
+b_CTCTACGAGATGCCAG-1	bmcite	1363	644	3078	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.434973333864501	5.0	5
+b_CTCGTACAGCTGGAAC-1	bmcite	2423	646	5911	25	HumanHTO3	batch2	T cell	CD4 Naive	0.442676569481343	4.0	3
+a_TACAGTGAGACGACGT-1	bmcite	1776	538	5055	25	HumanHTO5	batch1	T cell	CD4 Naive	0.243738104594036	8.0	3
+a_CTAGTGAGTTCGCGAC-1	bmcite	6639	1625	1425	24	HumanHTO4	batch1	Progenitor cells	HSC	0.438482873466056	3.0	0
+b_TCACAAGTCCAGAGGA-1	bmcite	6804	1725	5804	25	HumanHTO8	batch2	B cell	Memory B	0.375549038765307	2.0	1
+a_TTCGGTCAGAAACCTA-1	bmcite	3342	930	4922	25	HumanHTO10	batch1	B cell	Memory B	0.232586406887543	2.0	1
+a_GCAGTTATCTCCAACC-1	bmcite	1742	742	5373	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.441543494089741	6.0	5
+b_GTAACGTAGCACGCCT-1	bmcite	2445	819	5892	25	HumanHTO8	batch2	T cell	Treg	7.13420173800622e-07	9.0	3
+b_GCAATCAAGCTATGCT-1	bmcite	2389	811	3896	25	HumanHTO6	batch2	T cell	CD4 Memory	0.251346918375092	9.0	3
+b_GGACAAGAGTGGTCCC-1	bmcite	1573	535	5850	25	HumanHTO3	batch2	T cell	CD8 Naive	0.436882321891197	1.0	7
+a_GGCGTGTCAGATCGGA-1	bmcite	2021	612	3950	25	HumanHTO3	batch1	T cell	CD4 Naive	0.409900281123938	8.0	3
+b_AGCATACCAACTGCGC-1	bmcite	1084	437	1288	25	HumanHTO3	batch2	B cell	Memory B	0.518223655131131	2.0	9
+a_CTGATCCGTACATCCA-1	bmcite	1554	512	3898	25	HumanHTO9	batch1	T cell	CD4 Naive	0.0418052844267037	8.0	9
+a_TCTTCGGCAGGAATCG-1	bmcite	1677	734	2694	24	HumanHTO7	batch1	NK	NK	0.397837158674568	7.0	8
+a_CGTAGCGGTGCACCAC-1	bmcite	3067	883	4193	25	HumanHTO7	batch1	B cell	Memory B	0.185837788735525	2.0	1
+b_GGAGCAACACTGTGTA-1	bmcite	3286	800	9706	25	HumanHTO5	batch2	T cell	CD8 Naive	0.506898654842527	0.0	7
+b_CCTTCCCTCTTCATGT-1	bmcite	1510	557	2576	25	HumanHTO8	batch2	T cell	CD4 Naive	0.419165548323449	8.0	9
+b_CTGGTCTTCTTGGGTA-1	bmcite	1885	808	7709	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.513237464346831	6.0	5
+a_CGAACATTCTTCGAGA-1	bmcite	2022	674	3652	25	HumanHTO9	batch1	T cell	CD4 Naive	0.428049503369998	8.0	3
+a_GCGCGATGTACAGTTC-1	bmcite	1912	760	5988	25	HumanHTO8	batch1	NK	NK	0.160806032879927	7.0	8
+a_CGGAGCTCACACAGAG-1	bmcite	6834	1780	5662	25	HumanHTO3	batch1	Mono/DC	cDC2	0.576092142212931	10.0	0
+b_CGTTAGAGTAGCTTGT-1	bmcite	3552	1114	9475	25	HumanHTO6	batch2	Mono/DC	CD16 Mono	0.452512439413593	7.0	10
+a_GGGCATCGTATAATGG-1	bmcite	3844	1229	4532	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.539627971123804	6.0	5
+a_CTAGCCTCATAACCTG-1	bmcite	1952	729	8204	25	HumanHTO5	batch1	T cell	CD8 Memory_2	0.0124648158713414	1.0	7
+b_ACTGATGTCAGTTCGA-1	bmcite	3033	936	5219	25	HumanHTO1	batch2	T cell	CD4 Memory	0.363623269816583	9.0	3
+b_TTTCCTCCATCCGGGT-1	bmcite	2372	891	6932	25	HumanHTO2	batch2	T cell	CD8 Effector_1	0.353665485408251	1.0	7
+b_ATTCTACTCATCTGTT-1	bmcite	4086	1280	1052	25	HumanHTO3	batch2	Progenitor cells	Prog_DC	0.677270191549652	3.0	0
+a_CTTTGCGTCCACTCCA-1	bmcite	2304	820	5868	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.495123960420391	6.0	5
+a_GGGAGATTCGTATCAG-1	bmcite	2884	794	5170	25	HumanHTO2	batch1	T cell	CD8 Naive	0.469305722874908	0.0	7
+b_ATTGGACCACGGTAGA-1	bmcite	3247	1131	4856	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.278385474573256	6.0	5
+a_GTTAAGCCAGGTCCAC-1	bmcite	2134	737	4384	25	HumanHTO2	batch1	T cell	CD4 Memory	0.222153439325616	9.0	3
+b_TTCTTAGGTCCGAGTC-1	bmcite	1993	570	2243	25	HumanHTO1	batch2	T cell	CD8 Naive	0.390664316887691	1.0	9
+b_CATCAAGCATTTGCTT-1	bmcite	2570	813	3695	25	HumanHTO6	batch2	T cell	CD8 Naive	0.066808940492626	0.0	7
+b_CTCAGAATCCTAGAAC-1	bmcite	2636	754	5262	25	HumanHTO4	batch2	T cell	CD8 Naive	0.513581416347362	0.0	7
+a_TGCGTGGAGTGTCCAT-1	bmcite	2164	681	2583	25	HumanHTO4	batch1	B cell	Memory B	0.627150761964156	2.0	1
+a_TACTTGTGTAGCTCCG-1	bmcite	1455	648	4856	25	HumanHTO7	batch1	NK	NK	0.440968818391922	7.0	8
+b_GACACGCGTCGAAAGC-1	bmcite	2493	732	2523	25	HumanHTO3	batch2	B cell	Naive B	0.505826525398145	2.0	1
+b_TGGGCGTTCAATAAGG-1	bmcite	5931	1827	3743	25	HumanHTO3	batch2	B cell	Memory B	0.744345610291516	2.0	1
+a_GAAACTCAGACAGAGA-1	bmcite	1212	422	4256	23	HumanHTO6	batch1	T cell	CD4 Naive	0.133259467912997	8.0	3
+a_AAGCCGCCAGTAGAGC-1	bmcite	34223	5005	714	25	HumanHTO10	batch1	Progenitor cells	Prog_RBC	0.481632176889269	3.0	2
+b_CCCAGTTTCGTGGGAA-1	bmcite	2915	820	3733	25	HumanHTO8	batch2	T cell	CD4 Memory	0.332525721878976	9.0	3
+a_CATCAAGTCTCCGGTT-1	bmcite	1084	582	3880	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.5562775972048	5.0	5
+a_GGTGTTAGTGGCTCCA-1	bmcite	1922	635	2206	25	HumanHTO10	batch1	B cell	Naive B	0.372421558933739	2.0	1
+a_CGTTAGACAGTATCTG-1	bmcite	3129	761	4917	25	HumanHTO1	batch1	T cell	CD4 Naive	0.240171961131472	4.0	3
+a_AACTCAGTCACTCTTA-1	bmcite	2476	765	3234	25	HumanHTO3	batch1	T cell	CD4 Memory	0.174391298013622	8.0	3
+b_CCCATACAGCTGCCCA-1	bmcite	1547	560	3420	25	HumanHTO5	batch2	T cell	gdT	0.489146752573379	8.0	9
+a_CTTCTCTTCTCAAGTG-1	bmcite	1493	629	3195	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.383527916791579	5.0	5
+b_ATCATGGGTCTAGCGC-1	bmcite	2756	728	5458	25	HumanHTO1	batch2	T cell	CD4 Naive	0.380684052748166	4.0	3
+b_AACACGTAGGGATGGG-1	bmcite	1042	411	2954	25	HumanHTO7	batch2	T cell	CD4 Naive	0.234109983045286	8.0	9
+b_CGCTGGACATTTCACT-1	bmcite	2197	895	3991	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.546093869122599	6.0	5
+a_GAATAAGGTGCCTGTG-1	bmcite	2253	786	7002	25	HumanHTO7	batch1	T cell	CD8 Effector_2	0.44108724036553	1.0	8
+b_CAACCAACATCTACGA-1	bmcite	3686	969	8626	24	HumanHTO6	batch2	T cell	CD8 Naive	0.0004598395654142	0.0	7
+a_CGGCTAGCATATACCG-1	bmcite	2930	913	5384	25	HumanHTO2	batch1	T cell	CD4 Memory	0.303317995819665	8.0	7
+b_CTGATCCAGCTTCGCG-1	bmcite	4768	1454	8692	25	HumanHTO7	batch2	B cell	Memory B	0.354480313218822	2.0	1
+a_AGTGAGGAGCACCGTC-1	bmcite	1721	660	5330	25	HumanHTO7	batch1	T cell	CD8 Effector_1	0.227481218571611	1.0	7
+b_CCTTTCTGTTCGCGAC-1	bmcite	1140	560	1650	25	HumanHTO8	batch2	NK	NK	0.279293086358829	1.0	9
+a_ATCATGGAGGAGTTGC-1	bmcite	1956	764	6810	25	HumanHTO8	batch1	T cell	CD8 Effector_1	0.415048387098661	1.0	8
+a_GAATAAGAGATACACA-1	bmcite	2299	817	1918	25	HumanHTO8	batch1	B cell	Naive B	0.0520438933534337	2.0	1
+b_CTCGGAGGTCTAACGT-1	bmcite	4231	1093	1752	25	HumanHTO9	batch2	Progenitor cells	GMP	0.696278619608229	3.0	0
+a_GTGCAGCCACAGTCGC-1	bmcite	12659	2707	1040	25	HumanHTO2	batch1	Progenitor cells	Prog_RBC	0.688811706701988	3.0	2
+b_AGAGCTTAGCCGGTAA-1	bmcite	3530	956	7398	25	HumanHTO1	batch2	T cell	CD8 Naive	0.355473901476214	0.0	7
+a_GGTGAAGTCAACCATG-1	bmcite	2036	642	3316	25	HumanHTO6	batch1	NK	NK	0.492053893707182	7.0	8
+a_CCTCTGAAGTAGGTGC-1	bmcite	1909	699	5198	25	HumanHTO10	batch1	T cell	gdT	0.452410780688848	1.0	7
+a_TACAGTGCACTACAGT-1	bmcite	2214	705	3113	25	HumanHTO8	batch1	B cell	Naive B	0.460413894743583	2.0	1
+a_AGTGTCATCTAACTTC-1	bmcite	1802	574	7529	25	HumanHTO8	batch1	T cell	CD8 Naive	5.70383197378133e-06	1.0	7
+b_TGCCCATAGTCGCCGT-1	bmcite	2250	694	3911	25	HumanHTO5	batch2	T cell	CD4 Memory	0.348638861003149	4.0	3
+a_ACGGGCTGTTCTGAAC-1	bmcite	1436	606	1771	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.415748335842579	5.0	9
+b_GCAATCAGTAAAGGAG-1	bmcite	1384	587	3758	25	HumanHTO8	batch2	T cell	CD4 Memory	0.350195575011001	9.0	3
+b_AGAATAGAGTACGTAA-1	bmcite	3014	819	5321	25	HumanHTO3	batch2	T cell	CD8 Naive	0.272116581611789	0.0	7
+b_AGTGTCATCCTAGTGA-1	bmcite	1971	685	5123	25	HumanHTO5	batch2	T cell	CD4 Memory	0.568934361031908	9.0	3
+b_CGGGTCAAGTGTTAGA-1	bmcite	2176	779	3449	25	HumanHTO7	batch2	T cell	MAIT	0.173768961427712	1.0	7
+b_GCTGCTTGTATCACCA-1	bmcite	4350	1041	4610	25	HumanHTO7	batch2	T cell	CD4 Memory	0.30623282679041	9.0	3
+a_CGATCGGAGCTTTGGT-1	bmcite	1860	779	4516	25	HumanHTO2	batch1	T cell	Treg	0.122455313160014	9.0	3
+a_CACAGGCTCACGCATA-1	bmcite	2989	835	2887	25	HumanHTO2	batch1	B cell	Memory B	0.415691036697793	2.0	1
+a_TGAAAGATCATTGCGA-1	bmcite	1801	754	4193	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.563526837696203	5.0	5
+a_ACTGTCCTCTGTCTAT-1	bmcite	23729	4189	783	25	HumanHTO2	batch1	Progenitor cells	Prog_RBC	0.57008986970122	3.0	2
+b_CAGAGAGAGGGCATGT-1	bmcite	3421	821	10366	25	HumanHTO1	batch2	T cell	CD8 Naive	0.525608394153812	0.0	7
+a_GGACAGATCCTCATTA-1	bmcite	1490	689	1240	25	HumanHTO7	batch1	Progenitor cells	GMP	0.491567757867046	3.0	9
+b_CTCACACAGGGTGTGT-1	bmcite	2196	766	5311	25	HumanHTO2	batch2	T cell	CD8 Memory_1	0.231086807771179	0.0	7
+b_TTTGCGCCAGCGATCC-1	bmcite	1493	533	2933	24	HumanHTO8	batch2	T cell	CD4 Memory	0.279870773528737	8.0	9
+b_CAAGATCGTCGGCTCA-1	bmcite	2506	751	5859	25	HumanHTO6	batch2	T cell	CD8 Naive	0.372880674817441	0.0	7
+a_AAACGGGCAACTGGCC-1	bmcite	2989	843	7076	25	HumanHTO7	batch1	T cell	CD4 Memory	0.125563765818779	9.0	3
+b_ATGTGTGCATTCTTAC-1	bmcite	1403	516	1421	25	HumanHTO1	batch2	B cell	Naive B	0.350224086123447	2.0	9
+a_TGCCCATGTAGCGCAA-1	bmcite	5077	1381	4980	25	HumanHTO2	batch1	Mono/DC	cDC2	0.601247072934693	10.0	5
+a_CAAGAAAGTTAAGTAG-1	bmcite	2833	961	9059	25	HumanHTO8	batch1	T cell	CD8 Memory_1	0.16499944425692	0.0	7
+b_GTGGGTCTCCCTAACC-1	bmcite	2206	822	1748	23	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.576205839790291	5.0	9
+a_TCGCGTTCACCTTGTC-1	bmcite	1411	494	1088	25	HumanHTO10	batch1	B cell	Memory B	0.679075958379119	2.0	9
+b_AATCGGTGTAGCTTGT-1	bmcite	2367	877	5099	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.467651789511706	2.0	1
+b_CTCGGAGCAGTCCTTC-1	bmcite	3125	1071	6859	25	HumanHTO6	batch2	T cell	CD4 Memory	0.195022022777266	9.0	3
+a_TTAGGCAGTCGCGGTT-1	bmcite	1876	663	4496	25	HumanHTO7	batch1	T cell	CD4 Memory	0.406022933686626	9.0	3
+a_CGCTATCCAGATCTGT-1	bmcite	2694	1036	6121	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.386855566214689	6.0	5
+b_CTACCCAGTAACGACG-1	bmcite	2512	803	2572	25	HumanHTO8	batch2	B cell	Memory B	0.458088573846503	2.0	1
+a_GTGCAGCTCATGTCCC-1	bmcite	3708	947	11006	25	HumanHTO1	batch1	T cell	CD8 Naive	0.457490870144292	0.0	7
+a_CCCTCCTCAAGTCTAC-1	bmcite	1383	683	5307	25	HumanHTO9	batch1	NK	NK	0.322022239743331	7.0	8
+b_TCATTACTCAGTCCCT-1	bmcite	2532	988	4058	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.32527195875061	5.0	5
+a_CCTTACGCAGAGCCAA-1	bmcite	1922	690	6463	25	HumanHTO10	batch1	T cell	CD4 Naive	0.313074550862813	4.0	3
+a_TGTCCCACAGAGTGTG-1	bmcite	1248	437	3549	25	HumanHTO3	batch1	T cell	CD8 Naive	0.11589704941958	1.0	9
+a_GCTGCGAAGAATCTCC-1	bmcite	1037	506	4565	25	HumanHTO9	batch1	NK	NK	0.206137004789484	7.0	8
+b_GTTAAGCCATACGCCG-1	bmcite	3305	830	7658	25	HumanHTO3	batch2	T cell	CD8 Naive	0.424947980863034	0.0	7
+a_ATTATCCCAGCTCGCA-1	bmcite	1370	629	4531	25	HumanHTO7	batch1	NK	NK	0.442715365607051	7.0	8
+a_GTGTTAGGTCAACATC-1	bmcite	1831	740	3262	25	HumanHTO10	batch1	T cell	CD4 Memory	0.290462029401032	4.0	3
+b_GCATGCGCACGTAAGG-1	bmcite	2358	836	2231	23	HumanHTO5	batch2	Mono/DC	cDC2	0.599315170923862	5.0	9
+b_CACCACTCACCTATCC-1	bmcite	2051	719	2208	25	HumanHTO9	batch2	B cell	Naive B	0.353275814047843	2.0	1
+b_GTGTTAGAGATCCGAG-1	bmcite	1024	480	2862	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.375008128969464	5.0	5
+a_AGTGAGGAGCAATATG-1	bmcite	3178	1132	3662	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.46406915141258	6.0	5
+b_GGGAGATTCCTTTACA-1	bmcite	13022	2615	2592	25	HumanHTO2	batch2	Progenitor cells	GMP	0.497882845310795	3.0	0
+b_TGTGGTAAGCACGCCT-1	bmcite	2660	939	4724	25	HumanHTO6	batch2	T cell	CD4 Memory	0.423552918908314	9.0	3
+b_TACTTACCACGTCAGC-1	bmcite	2210	785	5154	25	HumanHTO4	batch2	T cell	CD8 Memory_1	0.450750316490007	1.0	7
+a_ACAGCCGGTTTAGGAA-1	bmcite	1001	476	2037	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.638030933109628	5.0	9
+a_ATCGAGTGTTCCACTC-1	bmcite	1536	736	2207	25	HumanHTO1	batch1	NK	NK	0.205016501685532	1.0	9
+b_GTGTGCGAGACTGGGT-1	bmcite	2586	949	5567	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.465151018352479	6.0	5
+a_CGGAGCTGTCCTGCTT-1	bmcite	2640	856	4468	25	HumanHTO6	batch1	B cell	Naive B	0.514009433591327	2.0	1
+b_CTAGTGAGTCTAAACC-1	bmcite	4332	1440	3519	25	HumanHTO9	batch2	Progenitor cells	Prog_DC	0.589628534529098	5.0	0
+a_ATCGAGTGTAGCGCTC-1	bmcite	2081	746	4429	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.443244655867257	5.0	5
+b_TAGACCACACGTCAGC-1	bmcite	2025	857	3744	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.465873426050868	5.0	5
+a_GTTAAGCGTCGACTGC-1	bmcite	1478	683	3786	25	HumanHTO2	batch1	T cell	CD8 Memory_2	0.324883353010196	8.0	9
+a_TGAGAGGTCGTATCAG-1	bmcite	2122	842	7419	25	HumanHTO7	batch1	T cell	MAIT	0.140436464159066	1.0	7
+a_CCCATACTCATAAAGG-1	bmcite	35229	5065	1145	25	HumanHTO10	batch1	Progenitor cells	Prog_RBC	0.351956208637175	3.0	2
+a_AGTCTTTAGATGGCGT-1	bmcite	1445	517	4298	25	HumanHTO3	batch1	T cell	CD4 Memory	0.204909446196552	8.0	3
+a_GCGCCAAAGATGTTAG-1	bmcite	2850	728	6031	25	HumanHTO10	batch1	T cell	CD4 Naive	0.298429552721358	9.0	3
+b_ATCCGAACATGCAATC-1	bmcite	3235	892	6548	25	HumanHTO8	batch2	T cell	CD8 Naive	0.438105938795104	0.0	7
+b_TTAGTTCTCGACGGAA-1	bmcite	2469	1000	2473	25	HumanHTO9	batch2	NK	CD56 bright NK	0.0438166894540161	7.0	8
+a_TAGAGCTTCATCTGTT-1	bmcite	2558	719	6124	25	HumanHTO1	batch1	T cell	CD4 Naive	0.396898532527539	4.0	3
+a_CTAGCCTAGATCCTGT-1	bmcite	2977	1116	3107	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.462395817805254	5.0	5
+b_ATCCGAAGTAGCTGCC-1	bmcite	1789	511	3185	25	HumanHTO3	batch2	T cell	CD4 Naive	0.196728849396064	8.0	9
+a_GTAGGCCCACCGCTAG-1	bmcite	2352	754	3520	25	HumanHTO3	batch1	T cell	CD4 Memory	0.121715807697294	9.0	3
+b_ATCTGCCCATGTAAGA-1	bmcite	1447	705	7642	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.582965442662954	6.0	5
+a_TACGGATTCCTCAATT-1	bmcite	2489	931	4804	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.260510557611821	6.0	5
+b_CTCCTAGAGAAGGTGA-1	bmcite	2702	836	5492	25	HumanHTO7	batch2	T cell	CD4 Memory	0.256808666436623	9.0	3
+b_AACGTTGTCTTCTGGC-1	bmcite	2460	958	4739	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.39677414349653	5.0	5
+a_GAGTCCGTCCTAGGGC-1	bmcite	1305	577	5634	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.335461667979363	6.0	5
+b_CACCAGGTCTGTCTCG-1	bmcite	1918	646	3441	24	HumanHTO7	batch2	T cell	CD4 Naive	0.464470660479074	8.0	9
+a_GAGGTGACAGAGTGTG-1	bmcite	2491	729	4561	25	HumanHTO8	batch1	T cell	CD8 Naive	0.113779880515875	0.0	7
+a_GACTACATCAACGGGA-1	bmcite	1266	535	4031	25	HumanHTO6	batch1	T cell	CD4 Memory	0.507187793600257	9.0	3
+b_CACCTTGAGCTAGCCC-1	bmcite	1371	669	2815	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.476692621011432	5.0	5
+b_AGTAGTCCATTGTGCA-1	bmcite	2206	898	4197	25	HumanHTO4	batch2	Mono/DC	CD16 Mono	0.371306641854372	7.0	9
+b_GCGGGTTCAGTATCTG-1	bmcite	2669	750	6232	25	HumanHTO7	batch2	T cell	CD8 Naive	0.0014128358555995	0.0	7
+a_CTCCTAGTCACTTCAT-1	bmcite	2390	870	5147	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.445656725667899	6.0	5
+b_TACTTACGTCATCCCT-1	bmcite	2161	802	7226	25	HumanHTO1	batch2	T cell	CD8 Effector_2	0.456110955354438	1.0	7
+a_GATGAGGAGTGGAGTC-1	bmcite	3450	1276	1639	25	HumanHTO5	batch1	Progenitor cells	HSC	0.476359677271709	3.0	0
+a_CTGCCTATCTTCGGTC-1	bmcite	1945	799	5156	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.583080122881749	6.0	5
+b_CACACCTGTACAGTTC-1	bmcite	1083	598	1298	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.702477184613886	5.0	9
+b_CCACCTACACAGTCGC-1	bmcite	1774	640	2371	25	HumanHTO7	batch2	T cell	CD4 Memory	0.472278842791242	8.0	9
+b_CCGTGGATCTTTAGTC-1	bmcite	2292	683	6704	25	HumanHTO7	batch2	T cell	CD4 Naive	0.415850839589441	4.0	3
+b_AAGTCTGCATGTCCTC-1	bmcite	3296	876	4292	25	HumanHTO3	batch2	T cell	CD4 Memory	0.378120612933682	9.0	3
+a_TAGACCAAGGAGTAGA-1	bmcite	2484	868	6012	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.463406877524731	6.0	5
+b_GTGCAGCTCCTCCTAG-1	bmcite	2669	822	4484	25	HumanHTO6	batch2	T cell	CD4 Memory	0.30104354438647	9.0	3
+b_ATTGGTGAGGAATTAC-1	bmcite	2743	745	8375	25	HumanHTO7	batch2	T cell	CD8 Naive	0.483218702674081	0.0	7
+b_GGACGTCTCGATCCCT-1	bmcite	2168	932	1688	25	HumanHTO5	batch2	Mono/DC	pDC	0.402982717490067	8.0	9
+b_ATTCTACTCTACTTAC-1	bmcite	3493	989	2841	25	HumanHTO6	batch2	B cell	Memory B	0.500276349008913	2.0	1
+a_TGAGGGAGTGAAATCA-1	bmcite	1009	511	6078	25	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.833206386942246	6.0	5
+b_ACGGGTCGTCATATGC-1	bmcite	5102	1472	1087	25	HumanHTO7	batch2	Progenitor cells	Prog_Mk	0.473906149062871	3.0	0
+a_ACGAGCCCAAGGCTCC-1	bmcite	2271	876	4583	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.354962824022485	6.0	5
+a_CAGATCACAGTATGCT-1	bmcite	1840	869	1119	24	HumanHTO7	batch1	Mono/DC	pDC	0.466189424876149	3.0	9
+b_CATCAAGTCGCCTGTT-1	bmcite	1960	556	3624	25	HumanHTO2	batch2	T cell	CD4 Naive	0.273968145097026	8.0	3
+b_CTTAACTAGGTCATCT-1	bmcite	2855	840	5369	25	HumanHTO10	batch2	T cell	CD8 Naive	0.205572367067764	0.0	7
+a_GAACATCCATCTCGCT-1	bmcite	2701	821	2982	24	HumanHTO10	batch1	T cell	CD4 Naive	0.0096095295711046	8.0	7
+b_TCAGGATTCGGCGCTA-1	bmcite	1851	657	1915	25	HumanHTO7	batch2	B cell	Naive B	0.441122756422048	2.0	1
+a_CCGGGATTCATGCAAC-1	bmcite	3263	1072	1049	25	HumanHTO6	batch1	Progenitor cells	Prog_B 1	0.34978502251247	3.0	9
+b_TCACGAAAGTACACCT-1	bmcite	1864	761	4438	25	HumanHTO10	batch2	T cell	CD8 Memory_2	1.77707748985282e-06	1.0	7
+b_TGGTTCCCATCCGTGG-1	bmcite	2585	984	5181	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.31605899935034	6.0	5
+b_ACCGTAATCCAATGGT-1	bmcite	2261	877	4763	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.33095015092402	6.0	5
+a_CGTAGGCAGAGACGAA-1	bmcite	3449	1180	9558	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.52273718366786	6.0	5
+b_TAGTGGTAGTCCTCCT-1	bmcite	3725	958	6139	25	HumanHTO8	batch2	T cell	CD4 Memory	0.512527458701307	9.0	3
+b_CATGACATCATGCAAC-1	bmcite	2796	1083	4709	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.44482864397339	6.0	5
+a_TACTCATAGCCACGCT-1	bmcite	2099	715	4467	25	HumanHTO8	batch1	T cell	CD4 Naive	0.129096471971868	8.0	9
+b_TTCCCAGAGTACCGGA-1	bmcite	1513	561	2363	25	HumanHTO6	batch2	B cell	Naive B	0.334174209103927	2.0	1
+b_ACTGAGTCACCAGATT-1	bmcite	3539	983	5030	25	HumanHTO6	batch2	T cell	CD4 Memory	0.402949833456146	9.0	3
+b_CTCGTCAAGCCCTAAT-1	bmcite	2575	984	3197	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.39614971711189	5.0	5
+b_CCGGGATTCCTCAACC-1	bmcite	2636	794	3730	25	HumanHTO10	batch2	T cell	CD8 Naive	0.288392313358849	0.0	7
+a_TTTGCGCTCACATGCA-1	bmcite	2582	897	3990	25	HumanHTO2	batch1	T cell	CD4 Memory	0.385615450530316	9.0	3
+b_TCGCGTTAGCTGGAAC-1	bmcite	1574	478	1651	25	HumanHTO9	batch2	T cell	CD8 Naive	0.140678083356166	1.0	9
+a_CGGGTCACAATAAGCA-1	bmcite	1107	512	2103	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.641278944832709	5.0	9
+b_ACTGAACTCTTACCGC-1	bmcite	3506	1169	1720	24	HumanHTO7	batch2	Mono/DC	cDC2	0.523236053495163	5.0	9
+b_GTACGTAAGGCTAGCA-1	bmcite	1500	514	4052	25	HumanHTO9	batch2	T cell	CD8 Naive	0.471271688810408	1.0	9
+a_CGTAGCGTCGAATGGG-1	bmcite	1539	546	1022	25	HumanHTO6	batch1	B cell	Naive B	0.360251194850211	2.0	9
+a_TACTTGTGTCACTGGC-1	bmcite	1853	650	4241	25	HumanHTO8	batch1	T cell	CD8 Naive	0.539739981700425	1.0	9
+b_ACACTGAGTCATGCCG-1	bmcite	2416	1016	2352	25	HumanHTO2	batch2	Progenitor cells	GMP	0.999907639199208	5.0	9
+b_CGTCTACGTGAGTGAC-1	bmcite	3334	1003	6330	25	HumanHTO6	batch2	T cell	CD4 Memory	0.234919721500884	9.0	3
+b_TCTATTGCACGGTAGA-1	bmcite	1490	510	6299	25	HumanHTO8	batch2	B cell	Memory B	0.551876477536975	2.0	1
+b_GGACGTCGTGACTACT-1	bmcite	3627	1155	4123	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.533683751705963	6.0	5
+b_ACTGATGAGCTAGTCT-1	bmcite	2155	886	14120	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.453793919795321	2.0	4
+b_AACGTTGCATTGAGCT-1	bmcite	8950	2418	4191	25	HumanHTO4	batch2	Progenitor cells	Prog_B 2	0.587539349606256	3.0	0
+b_CTCGGGATCCGCATCT-1	bmcite	2158	786	8242	25	HumanHTO7	batch2	T cell	CD8 Effector_1	0.186664547942352	0.0	7
+b_CCTAAAGTCGCCTGTT-1	bmcite	2511	899	4766	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.479117104704558	6.0	5
+a_AAACGGGGTGTCTGAT-1	bmcite	3060	825	5304	25	HumanHTO2	batch1	T cell	CD4 Memory	0.435773705972694	9.0	3
+a_TATCTCATCACGCATA-1	bmcite	2077	840	3156	24	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.424765708021197	5.0	5
+a_GTTCGGGAGTTGTAGA-1	bmcite	2797	802	8894	25	HumanHTO4	batch1	T cell	CD8 Naive	0.527813074441139	0.0	7
+b_CTAGCCTCATCCTTGC-1	bmcite	3108	805	4818	25	HumanHTO5	batch2	T cell	CD4 Naive	0.410636523271989	4.0	3
+a_TTGAACGCAGCTCCGA-1	bmcite	2193	678	2457	25	HumanHTO6	batch1	B cell	Naive B	0.423474245125703	2.0	1
+b_TATCTCATCTTTAGTC-1	bmcite	2995	860	3300	25	HumanHTO7	batch2	B cell	Memory B	0.345847119456072	2.0	1
+a_CTAGCCTGTCTCCATC-1	bmcite	2862	811	5506	25	HumanHTO1	batch1	T cell	CD4 Memory	0.38384176800703	9.0	3
+b_ATCTACTCACCAACCG-1	bmcite	1321	515	1255	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.432202911306595	5.0	9
+b_TTCGAAGCATGATCCA-1	bmcite	2207	830	2933	24	HumanHTO9	batch2	Mono/DC	CD16 Mono	0.518388163537103	7.0	8
+a_ATGCGATTCGCTAGCG-1	bmcite	1826	794	4029	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.439617359149692	6.0	5
+a_TAAACCGTCACGATGT-1	bmcite	1174	546	2955	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.565496437317411	5.0	9
+a_GACTGCGGTACTCTCC-1	bmcite	2832	842	7338	25	HumanHTO8	batch1	T cell	CD8 Naive	0.560558264766481	0.0	7
+a_CGCTATCGTGAAGGCT-1	bmcite	1067	498	3307	24	HumanHTO6	batch1	Mono/DC	CD16 Mono	0.757092875502988	7.0	9
+b_ACGCAGCGTCATCCCT-1	bmcite	1111	521	6014	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.62657643138914	6.0	5
+b_TGCCCATAGGCCCGTT-1	bmcite	2919	1076	2868	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.449066251060188	5.0	9
+a_GGATTACTCCGCATCT-1	bmcite	2911	817	7123	25	HumanHTO4	batch1	T cell	CD4 Naive	0.383582304329711	4.0	3
+a_GCAATCACATTGAGCT-1	bmcite	1065	519	5053	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.47608047125262	5.0	5
+a_CCGGGATTCGTATCAG-1	bmcite	1484	525	4279	25	HumanHTO10	batch1	T cell	CD4 Naive	0.295830396092362	8.0	9
+a_CATCGAATCATAGCAC-1	bmcite	1120	516	4072	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.732001373472257	5.0	5
+b_CTCTAATCAGTATGCT-1	bmcite	2484	748	5741	25	HumanHTO4	batch2	T cell	CD4 Naive	0.395907287521287		3
+a_TCTTTCCGTTGTCTTT-1	bmcite	1683	678	2787	25	HumanHTO6	batch1	B cell	Naive B	0.490397029969701	2.0	1
+a_CGCCAAGTCTAACTGG-1	bmcite	3487	885	6296	25	HumanHTO4	batch1	T cell	CD4 Naive	0.35584259044401	9.0	3
+b_GACGTGCCACGAAGCA-1	bmcite	1801	594	2681	25	HumanHTO8	batch2	B cell	Naive B	0.365598158389459	2.0	1
+b_CTCTGGTTCTAACGGT-1	bmcite	1428	651	2850	25	HumanHTO2	batch2	Mono/DC	CD16 Mono	0.465208948403662	7.0	9
+b_GCTTCCAGTAAATACG-1	bmcite	4512	1099	5173	25	HumanHTO2	batch2	T cell	CD4 Memory	0.354757549145207	9.0	3
+b_CAAGGCCTCTTGTACT-1	bmcite	1371	548	2135	25	HumanHTO6	batch2	T cell	CD4 Memory	0.325114371440011	8.0	9
+b_GTCGGGTTCCTACAGA-1	bmcite	1671	604	5252	25	HumanHTO1	batch2	T cell	CD8 Memory_1	0.475168808974463	1.0	9
+b_TGCCAAAAGAGTGACC-1	bmcite	1185	459	1869	25	HumanHTO8	batch2	T cell	CD4 Memory	0.246008477426068	8.0	9
+a_GACACGCAGTACGCGA-1	bmcite	3948	1374	485	25	HumanHTO10	batch1	Progenitor cells	Prog_RBC	0.817021314946881	3.0	0
+a_AAGTCTGAGTGTACCT-1	bmcite	2963	1124	7464	25	HumanHTO3	batch1	Mono/DC	CD16 Mono	0.459779114373213	6.0	10
+a_CAGCGACAGTGAAGTT-1	bmcite	1125	415	4733	25	HumanHTO9	batch1	T cell	CD4 Naive	0.561146067527104	8.0	9
+a_GGGCATCTCTGCTGCT-1	bmcite	3502	1067	5789	25	HumanHTO2	batch1	T cell	CD4 Memory	0.488104775049078	9.0	3
+a_ACGCCGAGTGTATGGG-1	bmcite	1417	644	3113	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.554284558209247	5.0	9
+a_TGGCTGGGTAGTACCT-1	bmcite	1288	421	2472	25	HumanHTO10	batch1	T cell	CD8 Naive	0.005061813489516	1.0	9
+a_TTCGAAGCAGCTTCGG-1	bmcite	2437	714	6293	25	HumanHTO10	batch1	T cell	CD4 Naive	0.479709552805102	4.0	3
+b_AGGGATGAGCGTCAAG-1	bmcite	1597	724	2946	23	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.55204294267861	5.0	5
+b_GTTACAGGTAAGGATT-1	bmcite	4233	1064	4993	25	HumanHTO8	batch2	B cell	Memory B	0.14489598641529	2.0	1
+b_CATCGAACAGACGCTC-1	bmcite	17076	3115	3184	25	HumanHTO2	batch2	Progenitor cells	LMPP	0.498430346695962	3.0	2
+a_GGTGCGTGTCGCCATG-1	bmcite	2671	745	8730	25	HumanHTO5	batch1	T cell	CD8 Naive	0.401795021703382	0.0	7
+b_CCATTCGGTAGTGAAT-1	bmcite	1849	776	3171	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.328947114905341	5.0	9
+b_CAACCTCGTCAGAGGT-1	bmcite	2822	818	4457	25	HumanHTO6	batch2	T cell	Treg	0.0293532594488855	4.0	3
+b_GTCTCGTAGAAACGCC-1	bmcite	1119	500	3643	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.505962380592872	5.0	5
+b_TGCTGCTCATGAACCT-1	bmcite	1035	446	4710	25	HumanHTO5	batch2	T cell	CD8 Effector_2	0.29523752756053	9.0	9
+a_AACTTTCAGTGTGGCA-1	bmcite	1523	663	6488	25	HumanHTO6	batch1	NK	NK	0.310484712266623	7.0	8
+b_GTCACAATCCACGAAT-1	bmcite	2280	990	5106	25	HumanHTO5	batch2	NK	NK	0.437990634330168	7.0	8
+a_TGGGAAGTCACCGGGT-1	bmcite	1576	627	6417	25	HumanHTO10	batch1	T cell	CD8 Memory_2	0.224619416701919	1.0	7
+a_GCACATACAGGGCATA-1	bmcite	1046	413	1394	25	HumanHTO1	batch1	B cell	Memory B	0.138516777645177	2.0	9
+a_GATTCAGCATCGTCGG-1	bmcite	4241	1045	3305	25	HumanHTO4	batch1	B cell	Memory B	0.107894562364186	2.0	1
+a_CGCGTTTCAAGCGTAG-1	bmcite	19189	3620	891	25	HumanHTO8	batch1	Progenitor cells	Prog_RBC	0.762831950511361	3.0	2
+a_ATAACGCAGCTAGCCC-1	bmcite	2737	985	4745	25	HumanHTO6	batch1	T cell	Treg	0.174379180071445	9.0	3
+a_TGTCCCAGTAAAGTCA-1	bmcite	1869	730	4981	25	HumanHTO9	batch1	T cell	MAIT	0.412789691479433	1.0	7
+b_CTCACACTCAGTTTGG-1	bmcite	2520	987	1656	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.282531919593164	5.0	9
+a_ATCATCTCATTCCTCG-1	bmcite	5136	1598	11444	25	HumanHTO2	batch1	NK	NK	0.37750783093723	1.0	8
+b_CCGTTCACAAGTACCT-1	bmcite	3882	1218	761	25	HumanHTO2	batch2	Progenitor cells	HSC	0.783842966399983	3.0	0
+a_CACACCTGTGCGAAAC-1	bmcite	3121	810	9451	25	HumanHTO2	batch1	T cell	CD8 Naive	0.548058482512223	0.0	7
+b_AACCATGTCGGCGCTA-1	bmcite	1441	643	3297	24	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.41586648637573	5.0	5
+b_ATAAGAGGTCTAGCGC-1	bmcite	2464	923	1476	25	HumanHTO9	batch2	Progenitor cells	Prog_DC	0.532012380496487	3.0	9
+a_TGGACGCTCCAGTAGT-1	bmcite	2577	678	4495	25	HumanHTO2	batch1	T cell	CD4 Naive	0.425151564240738	4.0	3
+a_GGATTACCAGCCTTGG-1	bmcite	1001	510	3311	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.587728079873063	5.0	5
+a_CGATTGAGTAGAAGGA-1	bmcite	12701	2920	4343	25	HumanHTO1	batch1	Progenitor cells	Prog_DC	0.881667085991992	3.0	0
+b_ATTCTACGTCAGAAGC-1	bmcite	2849	918	3139	25	HumanHTO2	batch2	B cell	Memory B	0.453876652256826	2.0	1
+b_GACGGCTCAAAGCAAT-1	bmcite	12231	2796	2937	25	HumanHTO4	batch2	Progenitor cells	LMPP	0.621661646855231	3.0	0
+a_CACACCTAGCTGCAAG-1	bmcite	1740	581	3508	24	HumanHTO3	batch1	T cell	CD4 Memory	0.0044540868178841	8.0	9
+a_AGCAGCCTCTGCAAGT-1	bmcite	2352	729	7252	25	HumanHTO10	batch1	T cell	CD4 Memory	0.267849152188257	9.0	3
+b_ATCTACTAGGCTCTTA-1	bmcite	3805	981	6671	25	HumanHTO5	batch2	T cell	CD4 Memory	0.268337286694907	9.0	3
+a_ATGCGATGTTGGGACA-1	bmcite	1163	443	1942	25	HumanHTO8	batch1	B cell	Memory B	0.833278909095848	2.0	9
+b_GGATGTTTCTCGTATT-1	bmcite	2944	840	5597	25	HumanHTO10	batch2	T cell	CD4 Naive	0.506305148924552	4.0	3
+b_TCGCGTTGTTGACGTT-1	bmcite	2207	693	3176	25	HumanHTO7	batch2	T cell	CD4 Memory	0.333188209055845	9.0	3
+b_CGCCAAGCACAAGACG-1	bmcite	1207	574	4014	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.388158622361239	5.0	5
+a_CCCTCCTCAATCTACG-1	bmcite	1713	613	5507	25	HumanHTO2	batch1	T cell	CD4 Memory	0.132600808070392	9.0	3
+b_GACACGCTCATGCATG-1	bmcite	2978	823	7264	25	HumanHTO2	batch2	T cell	CD8 Naive	0.40513988072956	0.0	7
+b_AGGCCACAGGGATACC-1	bmcite	17497	3335	2042	25	HumanHTO6	batch2	Progenitor cells	LMPP	0.607697233927515	3.0	2
+b_TCGCGAGTCGTCGTTC-1	bmcite	2189	629	6188	25	HumanHTO7	batch2	T cell	CD4 Naive	0.322763020196158	4.0	3
+a_TGTGTTTAGAGTCTGG-1	bmcite	2376	808	4866	25	HumanHTO8	batch1	T cell	CD4 Naive	0.176054912132332	4.0	3
+a_ACTGATGTCACATAGC-1	bmcite	1019	413	1621	25	HumanHTO8	batch1	B cell	Memory B	0.666491374482205	2.0	9
+b_AACCGCGGTAGAGCTG-1	bmcite	3671	951	3421	25	HumanHTO3	batch2	B cell	Memory B	0.348853699345495	2.0	1
+b_AACTCAGTCTCCAGGG-1	bmcite	2169	644	2351	25	HumanHTO4	batch2	B cell	Naive B	0.345702698201229	2.0	1
+a_GATCGATGTTCACGGC-1	bmcite	3006	790	8135	25	HumanHTO1	batch1	T cell	CD8 Naive	0.428251285629902	0.0	7
+b_CGGAGTCCAGCTATTG-1	bmcite	1706	673	5075	25	HumanHTO9	batch2	NK	NK	0.535578483114347	7.0	8
+b_CGGGTCAAGTCATCCA-1	bmcite	3406	1040	4819	25	HumanHTO6	batch2	T cell	CD4 Memory	0.333805414047228	9.0	3
+a_GAACCTACAGCGAACA-1	bmcite	1788	643	3351	25	HumanHTO10	batch1	T cell	CD8 Memory_1	0.495084735222572	1.0	9
+b_TAAGCGTTCTTTAGTC-1	bmcite	2744	780	6100	25	HumanHTO2	batch2	T cell	CD4 Naive	0.431637012870655	4.0	3
+b_ACGGAGATCGTTTGCC-1	bmcite	2388	980	5711	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.244644392673689	6.0	5
+b_TATCAGGAGATGCCAG-1	bmcite	1561	689	3836	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.361522926720347	5.0	5
+b_ACGGGCTCATTCCTGC-1	bmcite	2185	802	8957	25	HumanHTO2	batch2	T cell	CD8 Effector_1	0.245543828049995	1.0	7
+b_TGAGGGAAGATGTCGG-1	bmcite	5936	1719	4310	25	HumanHTO5	batch2	Mono/DC	cDC2	0.547180580581374	10.0	0
+a_CCTAGCTAGACAAGCC-1	bmcite	1014	429	2097	25	HumanHTO8	batch1	T cell	CD8 Naive	0.0126734383507651	2.0	9
+b_TGCGCAGTCCTTGGTC-1	bmcite	5668	1636	8869	25	HumanHTO10	batch2	Mono/DC	CD16 Mono	0.389932674315674	7.0	10
+a_CATCGAAAGAGCCCAA-1	bmcite	1498	668	2483	25	HumanHTO4	batch1	NK	NK	0.383991647571521	7.0	8
+b_CTGCTGTGTAAAGGAG-1	bmcite	1968	783	6212	25	HumanHTO5	batch2	T cell	CD8 Effector_1	0.248044160594081	1.0	7
+b_TGAGCATCAATAGAGT-1	bmcite	2314	677	5013	25	HumanHTO8	batch2	T cell	CD4 Naive	0.500442873130448	4.0	3
+a_AGATCTGGTTCAGTAC-1	bmcite	1950	607	5423	25	HumanHTO7	batch1	T cell	CD4 Naive	0.236092339814312	8.0	3
+b_TAGCCGGGTGGTGTAG-1	bmcite	1260	631	3972	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.709212617488244	5.0	5
+b_TCTTCGGGTGTGCCTG-1	bmcite	2613	714	4073	25	HumanHTO3	batch2	B cell	Naive B	0.321815936982893	2.0	1
+b_CTACCCATCAGTCCCT-1	bmcite	3691	1083	4722	25	HumanHTO7	batch2	T cell	Treg	0.0188050416221932	4.0	3
+a_ACGGCCAAGCAGGCTA-1	bmcite	1426	612	6215	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.409793170586238	6.0	5
+a_CTGCTGTAGCTAGTTC-1	bmcite	2140	864	3185	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.480212346571947	5.0	5
+b_GACTACATCTTAGAGC-1	bmcite	1618	676	4921	25	HumanHTO7	batch2	T cell	CD8 Effector_1	0.53476983258425	1.0	9
+b_CAGTCCTAGTGGAGAA-1	bmcite	1284	633	4655	25	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.537195049543417	5.0	5
+a_CATATGGTCTTAGCCC-1	bmcite	1790	764	5101	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.435317477673133	6.0	5
+b_TTCCCAGGTAGAGGAA-1	bmcite	2186	844	6897	25	HumanHTO5	batch2	T cell	CD8 Effector_1	0.192603686366335	1.0	7
+a_GGGTTGCTCGTAGGAG-1	bmcite	1050	467	4629	25	HumanHTO7	batch1	NK	NK	0.47429972874112	7.0	8
+b_ATTTCTGTCTAACTTC-1	bmcite	1590	570	1175	25	HumanHTO7	batch2	T cell	CD4 Memory	0.377400191598133	8.0	9
+a_GTAACTGCATTATCTC-1	bmcite	1409	618	4679	24	HumanHTO10	batch1	Mono/DC	CD14 Mono	0.450874656908642	5.0	5
+a_TGGCCAGGTGCCTGTG-1	bmcite	2807	1001	5253	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.405814205891062	6.0	5
+b_AAACGGGCACTACAGT-1	bmcite	2644	760	4769	25	HumanHTO2	batch2	T cell	CD4 Naive	0.466439556817882	4.0	3
+b_TCGAGGCCAAGCTGGA-1	bmcite	3544	1160	5877	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.44177378196394	6.0	5
+a_TTTACTGTCGACGGAA-1	bmcite	1247	458	3609	25	HumanHTO6	batch1	T cell	CD8 Naive	0.102900925094771	1.0	9
+b_AACCATGTCAAGGCTT-1	bmcite	1496	569	1393	24	HumanHTO7	batch2	B cell	Naive B	0.524804429941665	2.0	9
+a_TCGGTAACATGTTGAC-1	bmcite	1215	494	2017	25	HumanHTO2	batch1	B cell	Naive B	0.389171216248116	2.0	9
+a_CAAGATCTCTTACCTA-1	bmcite	1269	480	1786	25	HumanHTO8	batch1	T cell	CD8 Memory_1	0.248635892168355	1.0	9
+a_ATCATGGCACGCGAAA-1	bmcite	1944	649	1853	24	HumanHTO4	batch1	T cell	CD4 Memory	0.0504514242998822	8.0	9
+b_TGCCAAATCGTACGGC-1	bmcite	2996	917	2954	25	HumanHTO5	batch2	B cell	Memory B	0.183498029251464	2.0	1
+a_CACATTTAGCCCTAAT-1	bmcite	1164	591	3890	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.870534658491606	5.0	5
+a_CGATCGGCAGCCTGTG-1	bmcite	2016	680	4295	25	HumanHTO4	batch1	B cell	Memory B	0.585451461755103	2.0	1
+b_TGAGCCGAGGGATCTG-1	bmcite	2532	945	4558	25	HumanHTO8	batch2	Progenitor cells	GMP	0.570650020026448	6.0	5
+a_CTACACCCACCGATAT-1	bmcite	2205	704	4731	25	HumanHTO9	batch1	T cell	CD4 Naive	0.62453682590405	4.0	3
+a_TGACTAGCACCACGTG-1	bmcite	2166	698	6817	25	HumanHTO3	batch1	T cell	CD8 Naive	0.50455872697569	0.0	7
+a_TGGTTAGAGGCCCTTG-1	bmcite	2788	773	9240	25	HumanHTO8	batch1	T cell	CD8 Naive	0.373862883280787	0.0	7
+b_TCGCGAGTCGCAAACT-1	bmcite	2886	748	7018	25	HumanHTO9	batch2	T cell	CD8 Naive	0.321734462989631	0.0	7
+a_AAGTCTGGTTCCGGCA-1	bmcite	1398	597	3541	25	HumanHTO1	batch1	T cell	CD8 Effector_1	0.0880131414785655	1.0	9
+b_CAGGTGCTCAAACGGG-1	bmcite	1797	735	7058	25	HumanHTO4	batch2	Mono/DC	CD14 Mono	0.473441463356508	6.0	5
+a_GCGACCACAGGTGCCT-1	bmcite	1344	479	2842	25	HumanHTO8	batch1	T cell	Treg	0.095633358476163	8.0	9
+a_CGAGCCACATGTTCCC-1	bmcite	2302	653	7563	25	HumanHTO8	batch1	T cell	CD8 Naive	0.35100487438574	0.0	7
+b_CGTAGGCTCGCATGAT-1	bmcite	3552	854	8779	25	HumanHTO2	batch2	T cell	CD8 Naive	0.246678285760912	0.0	7
+a_CTGCTGTTCTGTACGA-1	bmcite	1926	867	6220	25	HumanHTO10	batch1	NK	NK	0.5799146060561	7.0	8
+a_AACTCCCTCCGTAGGC-1	bmcite	1921	871	4881	25	HumanHTO8	batch1	NK	NK	0.448385834896016	7.0	8
+a_CCACCTATCAGTTTGG-1	bmcite	2400	777	5452	25	HumanHTO7	batch1	T cell	CD4 Naive	0.221383456550816	4.0	3
+b_CGGACTGCATGGTCTA-1	bmcite	1497	573	4484	25	HumanHTO10	batch2	T cell	CD4 Naive	0.532673291501719	8.0	3
+b_GACTGCGCATCGATGT-1	bmcite	1067	397	2310	25	HumanHTO7	batch2	T cell	CD8 Naive	0.0164582142687304	1.0	9
+b_ACGAGCCAGGCCATAG-1	bmcite	2997	874	6701	25	HumanHTO2	batch2	T cell	CD4 Memory	0.0497436631703491	9.0	3
+b_CAGCTGGCAATGCCAT-1	bmcite	2161	659	2027	25	HumanHTO9	batch2	B cell	Naive B	0.383276896668798	2.0	1
+a_GGAACTTCACATAACC-1	bmcite	1567	674	2891	25	HumanHTO1	batch1	Progenitor cells	GMP	0.994001839008315	5.0	9
+a_GACGTGCAGACTAGAT-1	bmcite	4512	1203	6574	25	HumanHTO3	batch1	B cell	Memory B	0.595113853886896	2.0	1
+a_GACTACACAAGCCTAT-1	bmcite	2416	893	2830	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.401146196307693	5.0	5
+b_GGGTCTGCATCGATGT-1	bmcite	2753	838	2141	25	HumanHTO7	batch2	T cell	Treg	0.279266684426873	8.0	7
+a_CCTCAGTCATTTGCTT-1	bmcite	3138	933	8131	25	HumanHTO1	batch1	T cell	CD8 Naive	0.248566153936056	0.0	7
+b_GGGTCTGTCGCCTGAG-1	bmcite	1610	658	8583	25	HumanHTO1	batch2	T cell	CD8 Effector_1	0.099141381259757	1.0	4
+b_CCTTACGTCAGCATGT-1	bmcite	4089	1287	6593	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.593129086631912	7.0	10
+a_GAATAAGCACTCGACG-1	bmcite	2273	657	8005	25	HumanHTO3	batch1	T cell	CD8 Naive	0.546295559379211	0.0	7
+a_CCATGTCTCTCTTGAT-1	bmcite	2347	726	3175	25	HumanHTO8	batch1	T cell	CD4 Naive	0.452747961137133	4.0	3
+b_CACCACTAGTCACGCC-1	bmcite	2677	787	4225	25	HumanHTO7	batch2	T cell	CD8 Naive	0.0137338704725505	0.0	7
+a_CTGAAACAGAGTAATC-1	bmcite	2220	839	2986	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.420590116467598	5.0	9
+a_ATCTACTTCCACGTGG-1	bmcite	1262	630	4021	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.496867880735455	5.0	5
+a_GACCTGGGTAGTGAAT-1	bmcite	2588	943	4470	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.375554388283018	6.0	5
+b_GGGTTGCTCGCTTAGA-1	bmcite	8269	1888	1589	25	HumanHTO5	batch2	Progenitor cells	HSC	0.431879212428896	3.0	0
+a_AGCGTCGCAAACGTGG-1	bmcite	1583	523	3937	25	HumanHTO4	batch1	T cell	CD4 Naive	0.252806988673489	8.0	9
+a_AGAGCTTCAGGACCCT-1	bmcite	3267	836	6144	25	HumanHTO6	batch1	T cell	CD4 Naive	0.119452382644377	9.0	3
+b_TACGGTACAGACAAAT-1	bmcite	2233	733	2355	25	HumanHTO6	batch2	B cell	Naive B	0.441831053753958	2.0	1
+a_ATTTCTGCAGGCAGTA-1	bmcite	2217	677	8635	25	HumanHTO2	batch1	T cell	CD8 Naive	0.0058872246548162	0.0	7
+a_TACGGGCTCTTGTACT-1	bmcite	1422	513	2791	25	HumanHTO8	batch1	Progenitor cells	Prog_RBC	0.999967901844301	9.0	9
+a_CCCAGTTAGTCAAGGC-1	bmcite	2139	851	6249	25	HumanHTO9	batch1	Mono/DC	CD14 Mono	0.34815974037321	6.0	5
+b_GTTCTCGTCTGCCCTA-1	bmcite	4385	1169	8447	25	HumanHTO10	batch2	T cell	CD8 Naive	0.512875309827777	0.0	7
+a_GATTCAGCAACGATCT-1	bmcite	2720	949	5461	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.315552523818103	6.0	5
+b_TCAATCTCATCAGTAC-1	bmcite	3851	978	6570	25	HumanHTO4	batch2	T cell	CD8 Naive	0.303666694534924	0.0	7
+a_CAGTAACTCTGCAAGT-1	bmcite	1431	588	1560	25	HumanHTO1	batch1	B cell	Naive B	0.425149361464014	2.0	9
+a_AAGGTTCCATGGAATA-1	bmcite	6082	1149	7214	25	HumanHTO2	batch1	B cell	Plasmablast	0.55567714783666	3.0	7
+b_GTCGGGTTCGGTGTCG-1	bmcite	2900	754	5919	25	HumanHTO1	batch2	T cell	CD4 Memory	0.201715506637813	4.0	3
+b_GACTACATCAACGCTA-1	bmcite	2619	944	6580	25	HumanHTO9	batch2	T cell	CD8 Memory_2	0.126071988846914	1.0	7
+b_TGTTCCGAGAAGCCCA-1	bmcite	3570	856	3512	25	HumanHTO10	batch2	T cell	CD4 Naive	0.0573669069080926	4.0	3
+a_CAGAATCTCATGTCCC-1	bmcite	2985	1186	4440	25	HumanHTO6	batch1	Progenitor cells	Prog_B 1	0.708259757317674	4.0	3
+b_ATTCTACGTAGCCTAT-1	bmcite	1312	499	3044	24	HumanHTO3	batch2	T cell	CD4 Memory	0.372784602279055	9.0	9
+b_ATCCGAACACAAGTAA-1	bmcite	1710	527	1015	25	HumanHTO5	batch2	B cell	Memory B	0.292915873309366	2.0	9
+b_TGGCTGGGTCGGGTCT-1	bmcite	1849	764	6443	24	HumanHTO9	batch2	T cell	CD8 Effector_2	0.425986421865104	1.0	7
+a_CATGCCTCACCCTATC-1	bmcite	2062	691	4841	25	HumanHTO8	batch1	T cell	CD4 Memory	0.0613256016273702	4.0	3
+a_GTACTCCGTTCCTCCA-1	bmcite	8920	2761	4009	25	HumanHTO7	batch1	Progenitor cells	Prog_DC	0.736271731028716	3.0	0
+a_CCATTCGAGGCTACGA-1	bmcite	8009	2243	1001	25	HumanHTO3	batch1	Progenitor cells	Prog_RBC	0.572037511287642	3.0	0
+b_AGGCCGTTCAACTCTT-1	bmcite	3525	1002	6867	25	HumanHTO5	batch2	T cell	CD8 Naive	0.255621087508357	0.0	7
+a_GATGCTAGTCAATGTC-1	bmcite	10859	2550	1506	25	HumanHTO3	batch1	Progenitor cells	Prog_RBC	0.62079966521724	3.0	0
+b_CACCAGGAGCCCGAAA-1	bmcite	1344	613	3704	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.540412507194833	5.0	9
+b_TTCCCAGTCCCATTAT-1	bmcite	2055	660	3526	25	HumanHTO7	batch2	T cell	CD8 Naive	0.317837946068224	1.0	9
+b_ATGCGATCACCCATTC-1	bmcite	1805	601	5023	25	HumanHTO6	batch2	T cell	CD8 Naive	0.520975189543353	1.0	9
+a_GGGAGATGTCGCTTCT-1	bmcite	2522	695	3476	24	HumanHTO2	batch1	B cell	Memory B	0.398836172796994	2.0	1
+a_TCATTACAGCCACCTG-1	bmcite	1731	777	5666	25	HumanHTO2	batch1	NK	NK	0.388840350859156	7.0	8
+b_CTACGTCTCACAATGC-1	bmcite	4177	1243	6324	25	HumanHTO10	batch2	T cell	CD4 Memory	0.267318730696094	9.0	3
+b_ACGATGTCAGCTGGCT-1	bmcite	1542	476	2617	25	HumanHTO4	batch2	T cell	CD4 Naive	0.480728689480677	8.0	9
+b_ATTATCCAGTACGCCC-1	bmcite	2058	860	5331	25	HumanHTO5	batch2	NK	NK	0.503602891071006	7.0	8
+a_AGTGTCACACATCCGG-1	bmcite	13682	3186	3959	25	HumanHTO2	batch1	Progenitor cells	GMP	0.319502841872842	3.0	0
+b_GATGAAACATGTAAGA-1	bmcite	6919	1781	3888	25	HumanHTO7	batch2	Mono/DC	cDC2	0.528060769714729	10.0	0
+b_CTCGGAGTCCTCAACC-1	bmcite	2054	639	3838	23	HumanHTO7	batch2	T cell	CD8 Naive	0.523073748412518	0.0	7
+b_AAGGAGCAGAAACCAT-1	bmcite	1243	709	4738	25	HumanHTO4	batch2	Progenitor cells	GMP	0.884077625063475	5.0	5
+b_GCTGCTTAGACTACAA-1	bmcite	1805	599	4804	25	HumanHTO3	batch2	T cell	CD4 Memory	0.397950776765865	9.0	3
+a_CTCTACGTCCCAAGAT-1	bmcite	2363	754	5762	25	HumanHTO5	batch1	T cell	CD4 Memory	0.168029011644563	4.0	3
+a_AACTTTCGTTGCGTTA-1	bmcite	3138	1081	4814	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.435463759723987	6.0	5
+b_AAATGCCCAAACAACA-1	bmcite	2189	792	2083	25	HumanHTO1	batch2	B cell	Memory B	0.585867024550432	2.0	9
+b_TATCAGGCAAGTAGTA-1	bmcite	3315	979	4327	25	HumanHTO5	batch2	T cell	CD8 Naive	0.0065458946691567	1.0	7
+b_AACTCTTGTGCTCTTC-1	bmcite	2431	703	2311	25	HumanHTO7	batch2	T cell	CD4 Memory	0.0002278702824659	8.0	7
+b_CTCGGGAGTGTGTGCC-1	bmcite	1124	443	6116	24	HumanHTO4	batch2	T cell	CD4 Naive	0.375330993376607	9.0	3
+b_ATCCACCGTTCAGTAC-1	bmcite	2610	849	2862	25	HumanHTO7	batch2	B cell	Memory B	0.151387416469016	2.0	1
+b_TGCCCTACACAACGCC-1	bmcite	3373	834	8158	25	HumanHTO3	batch2	T cell	CD8 Naive	0.265306253829324	0.0	7
+b_TCTTTCCAGATGTCGG-1	bmcite	2071	650	2446	25	HumanHTO9	batch2	T cell	CD4 Naive	0.315655442080474	8.0	9
+a_TCAGGTAAGTGGGCTA-1	bmcite	19417	3237	660	25	HumanHTO4	batch1	Progenitor cells	Prog_RBC	0.892839710107371	3.0	2
+a_ACACTGACAACGATCT-1	bmcite	1710	647	3206	25	HumanHTO10	batch1	B cell	Naive B	0.0622535667393608	2.0	1
+b_ATCATCTGTCTGCAAT-1	bmcite	2932	822	5001	24	HumanHTO8	batch2	T cell	CD4 Naive	0.497697432117422	4.0	3
+a_TGAGCCGCAGAAGCAC-1	bmcite	2093	650	6024	25	HumanHTO1	batch1	T cell	CD4 Memory	0.0386796813515914	4.0	3
+b_AAGGCAGTCTCGATGA-1	bmcite	4734	1486	7949	25	HumanHTO8	batch2	Mono/DC	CD16 Mono	0.376488621271306	7.0	10
+a_TAGACCAGTTGAACTC-1	bmcite	1403	531	1294	25	HumanHTO1	batch1	B cell	Naive B	0.512846451636923	2.0	9
+b_CAAGTTGCAGAGTGTG-1	bmcite	3555	930	6615	25	HumanHTO7	batch2	T cell	CD8 Naive	0.393522818985611	0.0	7
+b_AGTTGGTAGGGAACGG-1	bmcite	2865	822	6630	25	HumanHTO4	batch2	T cell	CD4 Memory	0.164929301904211	9.0	3
+a_TGCGTGGCACATCTTT-1	bmcite	1296	581	4262	25	HumanHTO6	batch1	Mono/DC	CD14 Mono	0.548061294533654	5.0	5
+b_GAATAAGCATCAGTCA-1	bmcite	3397	1106	4473	25	HumanHTO5	batch2	T cell	CD4 Memory	0.575374975215796	4.0	3
+b_TACCTTAGTCCGAAGA-1	bmcite	20520	3714	6724	25	HumanHTO6	batch2	Progenitor cells	LMPP	0.999853792265563	3.0	2
+b_GATCGCGGTCAGATAA-1	bmcite	2901	685	6765	25	HumanHTO6	batch2	T cell	CD8 Naive	0.0213881504994911	0.0	7
+b_GATGAGGTCGGGAGTA-1	bmcite	2163	629	4355	24	HumanHTO8	batch2	T cell	CD4 Naive	0.246835818695251	4.0	3
+b_GAACATCCAAAGGTGC-1	bmcite	20328	3529	4913	25	HumanHTO7	batch2	Progenitor cells	Prog_B 2	0.219976591731397	3.0	2
+a_TAGACCAAGAGTCTGG-1	bmcite	1142	556	1263	25	HumanHTO5	batch1	Progenitor cells	HSC	0.486365554331379	3.0	9
+a_GTATCTTGTTCACCTC-1	bmcite	1452	465	1164	25	HumanHTO1	batch1	B cell	Naive B	0.485536152931307	2.0	9
+b_CAAGGCCGTGTGCCTG-1	bmcite	2073	825	10729	25	HumanHTO4	batch2	T cell	CD8 Effector_1	0.100004711841923	1.0	7
+a_CACATTTGTGAACCTT-1	bmcite	1549	531	7178	23	HumanHTO2	batch1	T cell	CD8 Naive	0.0088943765077216	1.0	7
+b_CGTAGGCCATGTCTCC-1	bmcite	3926	922	5299	25	HumanHTO7	batch2	T cell	CD8 Naive	0.417734333481598	0.0	7
+a_GGCTCGATCACTCTTA-1	bmcite	2868	837	7485	25	HumanHTO3	batch1	T cell	CD4 Naive	0.334093866060587	4.0	3
+b_GGTGTTAAGATGTGTA-1	bmcite	1485	663	6269	25	HumanHTO2	batch2	T cell	CD8 Effector_2	0.340346379516229	9.0	9
+a_TGCGGGTCAAACGCGA-1	bmcite	1378	579	3442	25	HumanHTO5	batch1	T cell	CD4 Memory	0.0375429801269124	9.0	3
+b_GTCATTTCAGCCACCA-1	bmcite	3486	898	6286	25	HumanHTO3	batch2	T cell	CD4 Naive	0.504545963023418	4.0	3
+b_GGGACCTCATGGTCTA-1	bmcite	3222	921	3169	25	HumanHTO7	batch2	T cell	CD4 Memory	0.184058983668862	9.0	3
+a_AACTCAGCACGGTGTC-1	bmcite	1371	509	4201	25	HumanHTO8	batch1	T cell	CD4 Naive	0.521407250109977	8.0	9
+b_GCATGATGTAAACACA-1	bmcite	1624	689	5325	25	HumanHTO6	batch2	NK	NK	0.397162581903758	7.0	8
+b_AGGGTGAAGCCATCGC-1	bmcite	1086	520	2328	25	HumanHTO1	batch2	NK	CD56 bright NK	0.176747909854778	7.0	8
+b_CCCAATCGTTTGACAC-1	bmcite	4188	944	9446	25	HumanHTO1	batch2	T cell	CD8 Naive	0.459767576562336	0.0	7
+b_TGAGAGGTCTGTGCAA-1	bmcite	1586	726	2336	25	HumanHTO4	batch2	Progenitor cells	GMP	0.505218037585857	5.0	9
+a_CTTCTCTGTCCTGCTT-1	bmcite	2268	630	5795	25	HumanHTO2	batch1	T cell	CD4 Naive	0.128925618519704	4.0	3
+b_TTATGCTTCGTACCGG-1	bmcite	5023	1551	4950	25	HumanHTO5	batch2	Mono/DC	cDC2	0.727242606628588	6.0	5
+b_ACTGATGGTATGAAAC-1	bmcite	2416	860	3381	25	HumanHTO10	batch2	Mono/DC	CD14 Mono	0.319399527517047	5.0	5
+a_CGGGTCACAGACAGGT-1	bmcite	1001	387	1960	25	HumanHTO3	batch1	B cell	Naive B	0.621408938877849	2.0	1
+a_CAGCTAAAGTCAATAG-1	bmcite	1362	414	2250	25	HumanHTO1	batch1	T cell	CD4 Naive	0.60029879586809	8.0	9
+b_CTCTAATGTTTAGGAA-1	bmcite	2921	1064	4981	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.414585381629355	6.0	5
+b_ATGGGAGAGAATCTCC-1	bmcite	1019	485	2889	25	HumanHTO1	batch2	Mono/DC	CD14 Mono	0.346957536842031	5.0	5
+a_CCGTTCAGTTGATTCG-1	bmcite	1027	472	3439	25	HumanHTO5	batch1	Mono/DC	CD14 Mono	0.409951773658274	5.0	9
+a_CTTCTCTAGTAGGCCA-1	bmcite	1852	820	5253	25	HumanHTO7	batch1	NK	NK	0.147518344745058	7.0	8
+b_CCTTCCCAGTCCGGTC-1	bmcite	5615	1766	3983	25	HumanHTO8	batch2	Progenitor cells	GMP	0.964068394066567	5.0	0
+a_ATGTGTGGTCTCGTTC-1	bmcite	1535	635	7232	25	HumanHTO2	batch1	T cell	CD8 Memory_2	0.092256281708403	1.0	8
+b_TGGACGCCATGACATC-1	bmcite	2147	739	3235	25	HumanHTO8	batch2	NK	NK	0.251173829348268	7.0	8
+b_ACATCAGTCTATCCTA-1	bmcite	4203	1058	9884	25	HumanHTO8	batch2	T cell	CD8 Naive	0.366301544894753	0.0	7
+b_TCGAGGCCATGCTGGC-1	bmcite	13593	1916	674	25	HumanHTO1	batch2	Progenitor cells	Prog_RBC	0.758341839098001	3.0	0
+b_TACACGACACACCGAC-1	bmcite	2822	793	4312	25	HumanHTO3	batch2	T cell	CD4 Naive	0.325958614632475	4.0	3
+a_GGGAATGAGAGCTTCT-1	bmcite	3361	941	8160	25	HumanHTO4	batch1	T cell	CD8 Naive	0.154268052811174	0.0	7
+a_TCAGATGTCAACGGGA-1	bmcite	2332	741	5304	25	HumanHTO7	batch1	T cell	CD4 Naive	0.500242707369756	4.0	3
+b_CTGATAGAGAAGGGTA-1	bmcite	2361	868	923	23	HumanHTO6	batch2	Progenitor cells	Prog_B 2	0.480352450520281	3.0	9
+b_GCGCCAAGTACTTAGC-1	bmcite	1587	646	3475	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.407332226887322	5.0	5
+a_CTGTGCTCAATTCCTT-1	bmcite	2856	785	4941	25	HumanHTO5	batch1	T cell	CD8 Naive	0.414549263778022	0.0	7
+b_ATCATCTCAGTACACT-1	bmcite	1601	613	1881	25	HumanHTO10	batch2	T cell	CD4 Memory	0.159094928889955	8.0	9
+b_GTTCATTCATTGGCGC-1	bmcite	2993	871	3970	25	HumanHTO10	batch2	T cell	CD4 Memory	0.305888145793749	9.0	3
+a_CGCGGTAGTAAATACG-1	bmcite	2662	708	9480	25	HumanHTO1	batch1	T cell	CD8 Naive	0.369251095717106	0.0	7
+b_ACTTTCAAGATCACGG-1	bmcite	1976	608	4165	24	HumanHTO8	batch2	T cell	CD8 Naive	0.619184320790701	1.0	7
+a_GGTGAAGCACGGCTAC-1	bmcite	3563	950	6660	25	HumanHTO10	batch1	T cell	CD8 Naive	0.237870590598066	0.0	7
+b_TTAGGACTCTGTTTGT-1	bmcite	2546	855	2275	25	HumanHTO8	batch2	B cell	Memory B	0.21143460023468	2.0	9
+a_TTGACTTAGATAGGAG-1	bmcite	1859	690	3427	25	HumanHTO7	batch1	T cell	gdT	0.283752309807318	1.0	7
+a_TGGACGCTCGTTTATC-1	bmcite	2633	868	3223	25	HumanHTO5	batch1	B cell	Memory B	0.323588663395689	2.0	1
+b_ACCTTTACACAGATTC-1	bmcite	2490	895	7441	25	HumanHTO1	batch2	T cell	CD8 Effector_1	0.151749521536144	1.0	7
+b_TAAGTGCGTAAGTTCC-1	bmcite	1469	502	1550	24	HumanHTO2	batch2	T cell	CD8 Naive	0.14926600322704	1.0	9
+b_CTACATTGTTATCGGT-1	bmcite	3722	854	6525	25	HumanHTO3	batch2	T cell	CD4 Memory	0.2323778569231	9.0	3
+b_CTGATAGCACAGACAG-1	bmcite	2010	715	2109	25	HumanHTO2	batch2	B cell	Naive B	0.146275391125804	2.0	1
+a_GCGAGAAAGGCATTGG-1	bmcite	1794	693	2096	24	HumanHTO10	batch1	B cell	Naive B	0.232944162414691	2.0	1
+a_CTAGAGTCAGATGGCA-1	bmcite	2314	912	5572	25	HumanHTO7	batch1	Mono/DC	CD14 Mono	0.406366754805568	6.0	5
+b_CACCAGGGTGTCAATC-1	bmcite	1913	806	4173	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.353468622526774	5.0	5
+a_CATATGGAGTCACGCC-1	bmcite	2790	835	4862	25	HumanHTO1	batch1	T cell	CD4 Memory	0.321113643593435	4.0	3
+b_GACTGCGCATACGCCG-1	bmcite	2257	911	5787	25	HumanHTO5	batch2	Mono/DC	CD14 Mono	0.642170751790099	6.0	5
+a_CACACAAGTAATCGTC-1	bmcite	1067	532	736	25	HumanHTO7	batch1	NK	NK	0.167090161852281	1.0	9
+a_ATTGGACTCCTATGTT-1	bmcite	1044	479	1200	25	HumanHTO6	batch1	B cell	Naive B	0.491227954631153	2.0	9
+b_TCAACGAGTTCCACAA-1	bmcite	2399	690	3645	25	HumanHTO3	batch2	T cell	CD8 Naive	0.402402238890991	0.0	7
+a_GTGGGTCCACGTAAGG-1	bmcite	2711	988	4962	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.656096040401163	6.0	5
+a_GTAGGCCCAGACGCAA-1	bmcite	1167	544	3518	24	HumanHTO8	batch1	NK	NK	0.466258412807513	7.0	8
+a_CAGCTGGGTTTACTCT-1	bmcite	1490	763	3061	25	HumanHTO6	batch1	Progenitor cells	GMP	0.564736444776335	5.0	9
+b_ACCTTTATCTCGGACG-1	bmcite	1759	654	2038	25	HumanHTO3	batch2	B cell	Naive B	0.429034859699112	2.0	1
+a_AACTCTTCAGGAATGC-1	bmcite	1020	492	6269	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.550245579380469	6.0	5
+b_AACACGTAGTATGACA-1	bmcite	1783	896	1812	25	HumanHTO1	batch2	Progenitor cells	Prog_B 1	0.73919047610304	2.0	9
+b_AGCCTAATCTGATACG-1	bmcite	3497	897	7211	25	HumanHTO6	batch2	T cell	CD4 Naive	0.540611115850009	4.0	3
+b_TGCCCATGTGACCAAG-1	bmcite	3894	1109	953	25	HumanHTO6	batch2	Progenitor cells	Prog_B 1	0.748208601920255	3.0	0
+b_AGCTCCTGTCTAGAGG-1	bmcite	1565	689	2737	25	HumanHTO9	batch2	Mono/DC	CD14 Mono	0.443601297384166	5.0	5
+a_CGTCTACGTACATCCA-1	bmcite	9875	2641	8449	25	HumanHTO9	batch1	Progenitor cells	Prog_DC	0.974701188356907	5.0	4
+b_AGCAGCCTCCTAGTGA-1	bmcite	4030	1519	4860	25	HumanHTO7	batch2	Mono/DC	pDC	0.482117067111276	3.0	0
+b_TTAGTTCAGACAGAGA-1	bmcite	3341	1005	7501	25	HumanHTO7	batch2	T cell	CD4 Memory	0.582204080273625	9.0	3
+a_TGGGAAGGTGGTTTCA-1	bmcite	2567	774	4431	25	HumanHTO2	batch1	T cell	CD4 Naive	0.0059399204703649	4.0	3
+a_ACGGGCTTCGCCTGAG-1	bmcite	1146	404	1895	24	HumanHTO9	batch1	T cell	CD4 Naive	0.328186677624256	8.0	9
+a_ATAACGCTCAACACCA-1	bmcite	1828	712	11458	24	HumanHTO7	batch1	T cell	CD8 Memory_2	0.456151686209458	1.0	7
+b_GCAAACTGTTAGAACA-1	bmcite	3143	890	5255	25	HumanHTO9	batch2	T cell	CD4 Naive	0.292314378871619	4.0	3
+b_TGTGGTAGTAACGCGA-1	bmcite	2067	643	5064	25	HumanHTO10	batch2	T cell	CD4 Naive	0.395269579146116	4.0	3
+a_TAAGTGCCACAAGACG-1	bmcite	1988	456	3341	25	HumanHTO8	batch1	B cell	Plasmablast	0.339012040791524	8.0	9
+b_ATCCACCCAATAGCGG-1	bmcite	2501	698	4626	25	HumanHTO6	batch2	T cell	CD4 Naive	0.391093157804535	4.0	3
+a_TGAGCCGGTTACCGAT-1	bmcite	10693	2360	1016	24	HumanHTO4	batch1	Progenitor cells	Prog_Mk	0.600349099891368	3.0	0
+b_CGACTTCTCGCTGATA-1	bmcite	2877	855	4737	25	HumanHTO4	batch2	T cell	CD4 Memory	0.443096300629453	9.0	3
+b_ACGTCAAGTAATCACC-1	bmcite	5949	1724	10880	25	HumanHTO2	batch2	Mono/DC	CD16 Mono	0.556315376375263	7.0	10
+b_GGGCATCGTCGCCATG-1	bmcite	1069	384	2028	25	HumanHTO2	batch2	T cell	Treg	0.118025810752741	8.0	9
+a_CTCGAAATCGCTTGTC-1	bmcite	1685	764	5325	25	HumanHTO1	batch1	Mono/DC	CD14 Mono	0.338007751271417	6.0	5
+a_CGTCTACAGCGATCCC-1	bmcite	2000	799	2240	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.469283698389256	8.0	9
+b_GCTCCTACAGCGTAAG-1	bmcite	2569	740	10292	25	HumanHTO2	batch2	T cell	CD8 Naive	0.379867274403661	0.0	7
+b_CTCGGGACAGCATACT-1	bmcite	2386	700	2662	25	HumanHTO8	batch2	B cell	Naive B	0.484307848123602	2.0	1
+a_CTGATCCAGGGTCGAT-1	bmcite	2118	790	4613	25	HumanHTO2	batch1	Mono/DC	CD14 Mono	0.489729309243999	6.0	5
+b_GATGAAATCCCATTTA-1	bmcite	1254	416	1153	25	HumanHTO9	batch2	B cell	Naive B	0.0002631100937884	2.0	1
+a_TCGAGGCAGCGAGAAA-1	bmcite	2272	754	4670	25	HumanHTO7	batch1	T cell	CD4 Memory	0.407141626685639	9.0	3
+a_TCTTCGGCAATGCCAT-1	bmcite	1454	491	3152	25	HumanHTO8	batch1	T cell	CD8 Naive	0.0068109211843756	1.0	9
+a_CTACACCCATTCCTCG-1	bmcite	22672	4076	603	25	HumanHTO2	batch1	Progenitor cells	Prog_RBC	0.980168850311466	3.0	2
+a_AACTCTTTCTCGTATT-1	bmcite	1676	754	2464	24	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.543123257338337	5.0	5
+b_GTAACGTTCGCATGAT-1	bmcite	2872	1070	2682	24	HumanHTO3	batch2	Mono/DC	cDC2	0.51751293236904	5.0	9
+a_TGATTTCCATTGGCGC-1	bmcite	2353	651	4586	25	HumanHTO3	batch1	T cell	CD4 Memory	0.0033870891946006	4.0	3
+a_TCCCGATAGTCTTGCA-1	bmcite	2744	934	511	25	HumanHTO8	batch1	Progenitor cells	HSC	0.544836994818596	3.0	9
+a_ACAGCCGAGGCTAGCA-1	bmcite	7929	1329	600	25	HumanHTO5	batch1	Progenitor cells	Prog_RBC	0.861273221252694	3.0	9
+a_CGTCCATTCTCAAACG-1	bmcite	1744	520	3389	25	HumanHTO9	batch1	T cell	CD4 Naive	0.39113981935778	8.0	9
+b_CGGACACTCTAACTTC-1	bmcite	1099	609	1527	23	HumanHTO6	batch2	Mono/DC	CD14 Mono	0.456580971366491	5.0	9
+a_AGCAGCCAGTGCAAGC-1	bmcite	1144	447	3307	25	HumanHTO3	batch1	T cell	CD8 Naive	0.150273524242043	1.0	9
+a_ATTCTACGTGCCTTGG-1	bmcite	1518	661	3693	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.353395448262051	5.0	5
+b_TGTTCCGTCAGTGTTG-1	bmcite	2075	624	3214	25	HumanHTO7	batch2	B cell	Memory B	0.494911305107911	2.0	1
+a_CATCAAGCACCGGAAA-1	bmcite	1319	561	2680	25	HumanHTO7	batch1	NK	NK	0.0090472599520001	7.0	8
+b_CACCACTTCAGGTAAA-1	bmcite	5042	1596	4434	25	HumanHTO8	batch2	Mono/DC	CD14 Mono	0.485171392555435	6.0	5
+b_GCATGCGAGTGCGTGA-1	bmcite	1923	767	5468	25	HumanHTO4	batch2	T cell	CD8 Effector_1	0.298532544987206	1.0	8
+a_GCTCCTAGTCCAGTGC-1	bmcite	2462	704	6137	25	HumanHTO7	batch1	T cell	CD4 Naive	0.346647326231899	4.0	3
+b_CCTTTCTCATTGGGCC-1	bmcite	1568	612	3619	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.545020570992619	2.0	1
+b_GATCAGTCAGTGGAGT-1	bmcite	1398	674	4018	25	HumanHTO2	batch2	NK	NK	0.49917360658189	7.0	8
+a_GAACCTATCAAAGACA-1	bmcite	2609	756	5007	25	HumanHTO1	batch1	T cell	CD4 Memory	0.4245621004084	9.0	3
+a_ACGAGCCTCAACACGT-1	bmcite	2337	1112	5765	24	HumanHTO7	batch1	T cell	Treg	0.395231409893062	9.0	3
+b_TGGCTGGCACCATGTA-1	bmcite	1457	646	3317	25	HumanHTO7	batch2	T cell	CD8 Effector_2	0.313645260106316	1.0	9
+a_CTACCCAAGCGTTCCG-1	bmcite	2342	889	5529	25	HumanHTO3	batch1	Mono/DC	CD14 Mono	0.498770089733997	6.0	5
+b_AATCGGTGTAGTGAAT-1	bmcite	3005	1125	11019	25	HumanHTO6	batch2	T cell	CD8 Memory_2	0.225589982852786	1.0	7
+b_GTCGTAAGTAAGTGGC-1	bmcite	2963	716	5750	25	HumanHTO4	batch2	T cell	CD4 Naive	0.319824535135006	4.0	3
+a_CATCCACCAAACTGCT-1	bmcite	2722	954	4160	25	HumanHTO4	batch1	Mono/DC	CD14 Mono	0.452725734932264	6.0	5
+a_TTGGAACCATTGGTAC-1	bmcite	2228	867	4652	25	HumanHTO8	batch1	Mono/DC	CD14 Mono	0.486692857736483	5.0	5
+b_CTCGAGGAGAACAACT-1	bmcite	3072	1131	4715	25	HumanHTO3	batch2	Mono/DC	CD14 Mono	0.521764148635087	6.0	5
+a_TCACAAGCACTGTGTA-1	bmcite	1070	460	1173	24	HumanHTO8	batch1	T cell	gdT	0.438781573737194	1.0	9
+b_CTTTGCGGTCAACATC-1	bmcite	3482	916	6371	25	HumanHTO4	batch2	T cell	CD4 Naive	0.31152015952036	9.0	3
+b_CATATTCGTTGGGACA-1	bmcite	3099	977	7053	25	HumanHTO1	batch2	T cell	CD8 Naive	0.506019344721973	0.0	7
+a_TCTATTGAGTTGCAGG-1	bmcite	1658	620	1673	25	HumanHTO1	batch1	B cell	Naive B	0.3673324327947	2.0	9
+b_CACAGTATCGCTGATA-1	bmcite	2442	936	5084	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.367243053487102	6.0	5
+b_CAGTAACCAAGTTGTC-1	bmcite	2920	1038	6157	25	HumanHTO2	batch2	Mono/DC	CD14 Mono	0.456998548340856	6.0	5
+a_CGAGCACAGCGAGAAA-1	bmcite	2241	717	5962	25	HumanHTO6	batch1	T cell	CD4 Naive	0.328468464676478	4.0	3
+b_AGATCTGAGGCAGTCA-1	bmcite	3320	835	8305	25	HumanHTO3	batch2	T cell	CD8 Naive	0.039025725231153	0.0	7
+a_CACCACTCAGCTGTAT-1	bmcite	7043	1963	4868	24	HumanHTO7	batch1	Progenitor cells	Prog_DC	0.584230750755133	3.0	0
+b_GTGCAGCCACCTCGTT-1	bmcite	2887	1099	4211	25	HumanHTO7	batch2	Mono/DC	CD14 Mono	0.484651257553884	6.0	5
--- a/test-data/test/clin	Fri Jul 04 14:57:40 2025 +0000
+++ b/test-data/test/clin	Wed Jul 23 07:49:41 2025 +0000
@@ -1,931 +1,931 @@
-sample	Crizotinib	Erlotinib	Irinotecan	Lapatinib	Nilotinib	Paclitaxel	Palbociclib	PD-0325901	PLX4720	Selumetinib	Sorafenib	Topotecan	OS_MONTHS	OS_STATUS
-target_dataset:SNU-503													222.2	1:DECEASED
-target_dataset:MOLP-2													61.7	0:LIVING
-target_dataset:Hs 695T	0.0137798414144127	0.0027959688318722	0.321048617899494	0.0123784672647789	0.0072786504189836	0.425845588447356	0.0648429496552876	0.344708384078938	0.0802339062256618	0.16073821868368	0.0	0.258897062513722	191.9333333	1:DECEASED
-target_dataset:SNU-C1													221.6	1:DECEASED
-target_dataset:COV434													102.3	1:DECEASED
-target_dataset:Becker													54.26666667	1:DECEASED
-target_dataset:TO 175.T													32.93333333	1:DECEASED
-target_dataset:IST-Mes1	0.0552132243328737	0.0301124999999999	0.297394724843068	0.0211427925113085	0.0844624998723459	0.441486600527355	0.0605887433961187	0.0275086952422516	0.0	0.0336000000000011	0.104544637172834	0.412407352923342	12.93333333	1:DECEASED
-target_dataset:SK-MM-2	0.0503744786698895	0.174156172063407	0.185320604859398	0.130386044991775	0.04805924800038	0.823749589146546	0.0505855369866957	0.286601633605995	0.0167646084212706	0.214235762418748	0.0485726240534126	0.272473327983325	185.0	1:DECEASED
-target_dataset:IM95	0.174670919343538			0.0		0.791126516285247		0.369769696319062	0.0403548642931684	0.43719340894271	0.0	0.206487085114108	45.16666667	1:DECEASED
-target_dataset:TE-14													98.7	0:LIVING
-target_dataset:Panc 03.27	0.0796665922057676	0.0627539316471438	0.429714192199248	0.0719940465872414	0.0021332421720732	0.743670895135035	0.0	0.313473247283132	0.0105086310980879	0.187731110927696	0.0627586330467863	0.365102164549562	149.8666667	0:LIVING
-target_dataset:HPAF-II	0.0557030069576363	0.0623307309772142		0.107629278498636		0.597273034723835		0.344478511177828	0.0	0.190852101975817	0.0		110.8666667	1:DECEASED
-target_dataset:SU-DHL-6	0.0	0.0	0.496410544649477	0.0	0.0	0.937500000000002	0.144741110147388	0.0	0.0114321875739164	0.0	0.0	0.513979728997994	60.13333333	0:LIVING
-target_dataset:SU.86.86	0.0297317601658816	0.0552695916589043		0.0396905477569368		0.527358736247149	0.0071655826439729	0.119234826114633	0.0180886272510808	0.059267656908599	0.0036003485168101	0.346456304092055	145.5	0:LIVING
-target_dataset:SNU-449	0.0128301444675588	0.0585179792211455	0.206179825852516	0.0524914190275245	0.0384795109699205	0.215095888097641	0.0	0.110221536165828	0.0371722879918132	0.0373175378680638	0.0405636986709944	0.145561716096203	197.7333333	1:DECEASED
-target_dataset:TE-4													102.5666667	0:LIVING
-target_dataset:ONS-76	0.0268650048204659	0.0214716122271844	0.348225215742851	0.0043104416659977	0.0065135486793762		0.0468475129024269	0.542920988746459	0.0306991998077307	0.350447808903791	0.0792615595753895		57.66666667	0:LIVING
-target_dataset:CGTH-W-1													155.7333333	1:DECEASED
-target_dataset:59M													28.56666667	1:DECEASED
-target_dataset:KMRC-20													101.4	0:LIVING
-target_dataset:B-CPAP	0.0249270910935637	0.0266103192388467	0.449276325757027	0.0182480115854805	0.0343968519234228	0.826543125499563	0.0251337707176827	0.3720568260738	0.166209595942362	0.205164760804459	0.124376215592606	0.386317335498267	211.9333333	0:LIVING
-target_dataset:A101D													274.2	0:LIVING
-target_dataset:AMO1	0.0941530094982855	0.0405618113899816	0.483946297362751	0.0785074679050283	0.158647488942422	0.854449262343508	0.0415696056530704	0.376007494209651	0.0155689893337798	0.19601950913879	0.0559722266443097	0.578895564373285	178.1666667	1:DECEASED
-target_dataset:NUGC-3	0.0188236287087096	0.236166481231863	0.607142244930022	0.216177680910763	0.0883670856177096	0.900506484123504	0.0781259856851584	0.320008507748779	0.0571610241841914	0.16218353306577	0.0377339731602628	0.511847551942751	58.93333333	1:DECEASED
-target_dataset:TE-9	0.025890030501428	0.0809057812802422	0.450871390330327	0.109944836602749	0.0296334712782784	0.863355537136094	0.0	0.114881746126447	0.0223946707326747	0.0897124999999999	0.0310959301979209	0.455172260341561	195.9333333	0:LIVING
-target_dataset:NCI-H929													30.3	1:DECEASED
-target_dataset:Hs 746.T	0.445435574914554	0.0282985266311347		0.0114718758777435	0.0694253390232157	0.641646591638054	0.070194026722269	0.317541105628196	0.0124249999999997	0.199511794347974	0.021568622240491	0.343084658408675	131.1	0:LIVING
-target_dataset:VCaP													57.3	1:DECEASED
-target_dataset:RVH-421	0.0046653014612015	0.0070321023866841	0.156119994817844	0.0014619594225774	0.0035873202892606	0.515119894518282	0.09653659611486	0.591801079952944	0.248206096140526	0.368595637925021	0.0	0.131360115762058	145.4333333	1:DECEASED
-target_dataset:UACC-812	7.77764034401597e-14	0.0508647075977552	0.231078343923212	0.215754177409555	0.108273933531381	0.62717486167209	0.0535688009920196	0.185881344597065	0.0	0.195892871951449	0.0150196122351588	0.191768070423079	150.4666667	0:LIVING
-target_dataset:TE-15	0.0	0.237841669361768		0.201942228882737		0.872753797118087		0.460122626177974	0.135541740738307	0.351150751602073	0.0	0.366677158460097	27.06666667	1:DECEASED
-target_dataset:A-204	0.0793819165254227	0.0132449010203385	0.450792215512211	0.0320300715478958	0.236943389697007	0.686246971142559		0.0786379836158717	0.0286255961853843	0.0343107815846088	0.26127278860821	0.351817043361683	247.8333333	1:DECEASED
-target_dataset:JIMT-1													24.86666667	1:DECEASED
-target_dataset:Hep 3B2.1-7	0.0523762701694029	0.0	0.122211196827965	0.0053474366142828	0.0583226870938649	0.452560094068671	0.0	0.294122430689068	0.0	0.0905536148791019	0.0243771998687227	0.0	80.66666667	0:LIVING
-target_dataset:SNU-C2A	0.0432982232073854	0.0947523608802268		0.0393501657752935	0.0	0.499947500700396	0.0530127428444635	0.270125379159745	0.0	0.074578373308403	0.0122252728998805	0.224115509872517	71.5	0:LIVING
-target_dataset:KLE	0.0	0.0871332926716541	0.221611086794081	0.0204216792983633	0.0	0.318642090321315	0.0300918903865755	0.0730953481969017	0.0213661995845222	0.0182945858789499	0.0	0.124003571358037	86.83333333	0:LIVING
-target_dataset:CAL-29													194.5666667	0:LIVING
-target_dataset:Reh	0.0587408679781686	0.0072748198316787		0.0238082845228587	0.0685182402212822	0.892719665483598	0.101838538529868	0.0	0.0760316990365508	0.0	0.0167084011841347	0.548315507968591	119.3666667	1:DECEASED
-target_dataset:TM-31													157.8	1:DECEASED
-target_dataset:Hs 571.T													22.23333333	0:LIVING
-target_dataset:TC-71	0.0352298195806185	0.0	0.450902215255161	0.0	0.0	0.898771555096695	0.0	0.0	0.051987640590833	0.0	0.0017652472241589	0.470930714963612	68.7	0:LIVING
-target_dataset:NCI-H2171													164.6	1:DECEASED
-target_dataset:U-251MG													46.06666667	1:DECEASED
-target_dataset:Huh-7													195.3	0:LIVING
-target_dataset:OVKATE													85.73333333	1:DECEASED
-target_dataset:Panc 08.13													272.1	0:LIVING
-target_dataset:CL-11													16.3	1:DECEASED
-target_dataset:SCC-9	0.0216342901314922	0.209352556276592		0.0975596712530789		0.673021788053213		0.35836331956886	0.0079461925878136	0.254885197493485	0.0	0.267992200402892	187.3	0:LIVING
-target_dataset:CMK-86	0.163255319440947	0.0731526974712473	0.520556599667272	0.0264857828435508	0.0522488975072093	0.747057647942774	0.239868060846815	0.102713467563602	0.0152017643369352	0.126543374146748	0.0425669814201898	0.598857385656374	172.8	1:DECEASED
-target_dataset:SNU-1272													121.6666667	1:DECEASED
-target_dataset:DMS 79													211.9	1:DECEASED
-target_dataset:HEL 92.1.7	0.0	0.0177813221317003		0.0033491594589878	0.018735464800044	0.633499797060302	0.0	0.0495097744571229	0.0114121971711227	0.0226248375526365	0.0281875000000003	0.343565318062566	56.5	1:DECEASED
-target_dataset:BT-20	0.0053854075571219	0.135831213554948	0.329299675326338	0.0577058433906533	0.0528750000000003	0.62499968838294	0.0		0.0072821463799632	0.017985394405743	0.0823599999999969	0.252513220832068	236.1333333	0:LIVING
-target_dataset:RPMI-7951	0.0663138489949516	0.0109625104648995		0.0101707509831928		0.890655493157463		0.197593848525163	0.0418312338126707	0.122791705494357	0.0	0.412802134288002	74.46666667	1:DECEASED
-target_dataset:HEK TE													104.5333333	0:LIVING
-target_dataset:GI-1	0.0554844949909191	0.0218896481720671		0.0263227432457074		0.715916289235424		0.101254762209423	0.0358484185754373	0.0446274842637695	0.0494678240079706	0.268284182547972	164.7	0:LIVING
-target_dataset:TE 159.T													88.93333333	1:DECEASED
-target_dataset:NCI-H1355	0.0397847556892167	0.170404319767082		0.162261593072831	0.0095582011009516	0.604401253355353	0.114961394506362	0.412695825443437	0.050931889329994	0.299147129365416		0.0916456708468782	237.2666667	1:DECEASED
-target_dataset:OC 316	0.0132017395355424	0.0341578512748455	0.393795090700988	0.0467524125114623	0.0208361362367381	0.826627284142302	0.0093081110375972	0.069047441593875	0.0240980175560026	0.0239579507029604	0.0	0.392025882240121	72.46666667	1:DECEASED
-target_dataset:HuH-28													224.8666667	0:LIVING
-target_dataset:SNU-878													335.7333333	1:DECEASED
-target_dataset:H4	0.111348578072488	0.0303917922741951		0.0	0.025775362541623	0.654294925658615	0.096600786325714	0.0010813732910778	0.0357219845034107	0.0	0.0070875000000002	0.306452218614489	102.7666667	1:DECEASED
-target_dataset:Daudi													112.9666667	1:DECEASED
-target_dataset:ES-2	0.0178105693626796	0.0207823472235324		0.0156688051497569		0.666676481292321	0.0294290924294295	0.420176129789581	0.0872679397891918	0.261322938588207	0.0173691763074328	0.548431744981789	26.76666667	1:DECEASED
-target_dataset:T3M-4													152.2	0:LIVING
-target_dataset:NCI-H854													153.8333333	1:DECEASED
-target_dataset:CAL-78	0.0362952914520503	0.0301368569813702	0.371622800418339	0.0242507168472441	0.0288657174416576	0.698944309339976	0.001814650657197	0.0036029107330894	0.0468709113299677	0.0	0.0105011335706235	0.356716784153508	39.16666667	1:DECEASED
-target_dataset:PK-45H	0.0483935013577612	0.0409515063862812	0.246848310348925	0.0187623562501652	0.0227344628918851	0.682609754074604	0.0511033808764478	0.5961552202652	0.0381569623714353	0.461874503383251	0.0	0.0514738791017594	177.6	0:LIVING
-target_dataset:A3/Kawakami													173.6333333	0:LIVING
-target_dataset:DMS 114	0.269570407476876	0.0		0.0139513219719525		0.761388443678442	0.105729761832857	0.0253325072909534	0.0	0.0691113872548532	0.0288161223308484	0.441184646316643	196.8666667	0:LIVING
-target_dataset:SK-N-AS	0.104381950519362	0.0127704422972367		0.0035777427890918		0.694384529040137	0.120436106495766	0.502073318472702	0.0	0.393668119038524	0.0105774737837995	0.40653186992673	20.2	1:DECEASED
-target_dataset:KNS-42	0.0142078933413851	0.0	0.236113390126947	0.0061208822875985	0.017347406573859	0.655403955926972	0.0588011278219564	0.0865769763328396	0.024825	0.0444625000000001		0.196555677116887	230.5	0:LIVING
-target_dataset:RS4;11													2.533333333	1:DECEASED
-target_dataset:22Rv1	0.0738452249511108	0.0050320973958801	0.506325359188213	0.0380548001574303	3.91511356066416e-14	0.74824327284111	0.0769388317051619	0.393935546322014	0.0987198302023474	0.372499279365378	0.0383372826425124	0.467487328285478	105.0	0:LIVING
-target_dataset:NCI-H2196													138.1	0:LIVING
-target_dataset:HCC15	0.0368600056859078	0.027726216792311		0.0463668826183946	0.012306987949475	0.68342354060299	0.0038861922599438	0.203720007147131	0.027968306476472	0.125454594400689	0.0119254853424875	0.270926264846983	41.46666667	1:DECEASED
-target_dataset:Detroit 562	0.0039442879484753	0.115415781570674		0.0454820639748503	0.0279140921388389	0.748042314370733	0.100027509578524	0.197995244075214	0.0373374999999998	0.128523820258397	0.0	0.149501933186434	77.5	1:DECEASED
-target_dataset:NCI-H1693	0.0161691401857627	0.196446584599643	0.392163495069849	0.116568744189364	0.015571978588497	0.515423595396102	0.0157198500272257	0.230436044755481	0.0185375000000006	0.113403285906594	0.0	0.394500943564118	174.5	1:DECEASED
-target_dataset:23132/87													256.8666667	1:DECEASED
-target_dataset:Karpas-299	0.135426391901824	0.0265723504029587		0.0046247070748952	0.0178091176692254	0.782499608022833	0.0756616108431302	0.247769883675943	0.0597624999999999	0.124382816740925	0.0217499999999998	0.3097516989195	153.9666667	0:LIVING
-target_dataset:P12-Ichikawa	0.0667397947087021	0.0423395601743679	0.699805450455911	0.0290539707188473	0.0387389814766192	0.920160222193926	0.178526286248371	0.306338405838376	0.0481741881186354	0.257745973642425	0.0622502917687655	0.682425464781365	2.0	0:LIVING
-target_dataset:Hs 683	0.0714767473403402	0.0118898423501595	0.34344997047633	0.0	0.0289656288192123	0.756532219097569	0.0375824776840394	0.02647120086727	0.0202259684385692	0.0501036073772769	0.0	0.333160455363049	186.3666667	0:LIVING
-target_dataset:ME-1 [Human leukemia]													202.1	0:LIVING
-target_dataset:JK-1													197.4333333	0:LIVING
-target_dataset:Hs 870.T													185.7666667	0:LIVING
-target_dataset:SW1417	0.0340022192018826	0.0119077730879152		0.0668004069306818		0.646467029130834	0.0101675841180453	0.517144722047801	0.243599402151787	0.347046957571766	0.0150891252709347	0.385340761896912	29.3	1:DECEASED
-target_dataset:Hs 944.T	0.0594628470002751	0.0221635619877788	0.520180246077975	0.0114181445251114	0.0092673610379475	0.82305327027765	0.0575916061117761	0.433838689336883	0.0639682623330179	0.29811349717258	0.0152969697932169	0.461260291753571	80.23333333	0:LIVING
-target_dataset:SALE													81.03333333	0:LIVING
-target_dataset:DAN-G													104.4666667	1:DECEASED
-target_dataset:HT-29	0.028050432022223	0.0008752056595092	0.392135562122309	0.0708895906300962	0.0427885500529516	0.836335594966698	0.06318305165994	0.668832118684568	0.242983014668772	0.536672618935481	0.0396337299205433	0.306424380992122	131.3	1:DECEASED
-target_dataset:JHOM-1													114.4666667	0:LIVING
-target_dataset:DV-90	0.0	0.145650783489382		0.0221992125290487		0.811663295247692		0.538584413186012	0.0	0.37419310770602	0.0	0.39447157274801	78.16666667	0:LIVING
-target_dataset:ZR-75-30	0.0	0.0	0.251553327774995	0.494316603465404	0.0516846243601643	0.445059034455755	0.0	0.0	0.0	0.0	0.0592587093471536	0.052483968809906	14.8	1:DECEASED
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+target_dataset:COR-L23	0.0218432103055836	0.0096508432450326	0.401043970426146	0.0119452991978726	0.0245015362460737	0.667296659167872	0.0026034311952182	0.0795197670436245	0.002369911232307	0.0016180066209453	0.0532266709729124	0.386865781509373	35.2	1	buz
+target_dataset:NCI-H1435													234.4	0	qux
+target_dataset:SNU-886													85.86666667	1	buz
+target_dataset:NCI-H2009	0.0297128797621746	0.0812374464357929	0.400206588047819	0.0278787472349518	0.0068683799458119	0.624539149604038	0.0029081404215365	0.339420217586036	0.0312719175242975	0.114938583759352	0.0048904253293696	0.244651648173706	199.1333333	0	qux
+target_dataset:Panc 10.05		0.0565475629518381	0.444781917855853	0.0646589116413945	0.0105304362890635	0.742107044106543	0	0.434277419605191	0	0.26212043293038	0.0248813832755119	0.412679547408518	50.66666667	1	buz
--- a/test-data/train/clin	Fri Jul 04 14:57:40 2025 +0000
+++ b/test-data/train/clin	Wed Jul 23 07:49:41 2025 +0000
@@ -1,921 +1,921 @@
-sample	Crizotinib	Erlotinib	Irinotecan	Lapatinib	Nilotinib	Paclitaxel	Palbociclib	PD-0325901	PLX4720	Selumetinib	Sorafenib	Topotecan	OS_MONTHS	OS_STATUS
-source_dataset:NCI-H1770													43.1	1:DECEASED
-source_dataset:KM12	0.253542275169827	0.113994160484336	0.0742822815015893	0.0429820009390036	0.107209858162004	0.0704101784264294	0.267770471939332	0.159832031329315	0.0089754680069798	0.0	0.127609568622519	0.142865276466136	240.7	0:LIVING
-source_dataset:Kelly	0.0798387095208873	0.0926893742438769	0.173013039987695	0.101237703906914	0.0	0.0104624513720856	0.0898392517986342	0.060911679498632	0.0050168414893049	0.073373861381777	0.130170998278001	0.46903463855175	144.3333333	0:LIVING
-source_dataset:WSU-DLCL2		0.0712472440111968	0.320791588631048	0.075583344695118	0.0566156129514686	0.1251308081892	0.280535976167446	0.0022971179572881	0.222030383410802		0.0692838823668002		157.5333333	0:LIVING
-source_dataset:VMRC-RCZ	0.0043994430625854	0.0788264548067618	0.0047243492140753	0.0374860112109303	0.0	0.0	0.0427801906206174	0.0383337227413034	0.0716181557165881	0.0355712355433095	0.0070204186294004	0.0496543874808994	78.7	0:LIVING
-source_dataset:NCI-H2810	0.100349825443384		0.142990095481615					0.0403945820817186	0.0373131064748271	0.125512734099215		0.302095879126078	126.6666667	1:DECEASED
-source_dataset:Caki-1													111.0	0:LIVING
-source_dataset:BB65-RCC													85.73333333	1:DECEASED
-source_dataset:CA46	0.0014836585749416	0.060209392888273	0.0982361203431573	0.153541787409306	0.0364880167353971	0.017353135135241	0.0987147315665915	0.0328606125015914	0.0613456077986809	0.0	0.148371612163237	0.136444408332251	170.8666667	0:LIVING
-source_dataset:NB(TU)1	0.0183000105738514	0.112469509484137	0.0934125004649796	0.100393268497024	0.051794993610324	2.96987950035085e-16	0.178847597660038	0.190320453138889	0.000802305363319	0.392653469603823	0.0880151929965022	0.31628423085018	125.6	1:DECEASED
-source_dataset:NCI-H28	0.110541730531776	0.146562151339564	0.0321330174534193	0.0997984073393081	0.0279453662581887	0.0	0.255436122432369	0.0923759567359449	0.02858073731996	0.0409939242963945	0.0199917979001204	0.111327681732459	138.3333333	0:LIVING
-source_dataset:A-704	0.118008145616614	0.0470571542875395	0.142427763365562	0.0635334577085759	0.112364715745615	0.0037607652262141	0.0930048340888921	0.0395032603987703	0.0816282016406401	0.0337644599588087	0.108355692202339	0.0952555225256304	256.0	0:LIVING
-source_dataset:C32 [Human melanoma]	0.0345773709623989	0.065620750366615	0.132619433213006	0.0077983791389459	0.0465478218296898	0.0	0.0651582070145086	0.382092565090217	0.31887745203201	0.4406039046368	0.0250336462655307	0.323631553277508	58.66666667	1:DECEASED
-source_dataset:SW1463	0.0239455360650954	0.148997158031401	0.0994807758806709	0.163907424238169	0.00524820235804	0.0223518540166035	0.176702936008273	0.15204669548728	0.0423852040911128	0.172151370740176	0.0914584929232963	0.1664640804271	195.5333333	0:LIVING
-source_dataset:NCI-H211	0.0258940850956053	0.0427341905043995	0.18843664333191	0.120845509791877	0.010433896017643	0.123756430652602	0.271016748896732	0.138539034251607	0.0079301460652363	0.0168112607667866	0.148764558287982	0.210533512753385	60.26666667	1:DECEASED
-source_dataset:CTB-1													32.03333333	1:DECEASED
-source_dataset:SNG-M	0.0921650350324685	0.0731774351510269	0.0186446363452908	0.0394328147427676	0.0377503453449762	0.0519377010044006	0.0617394670899124	0.0293855840199477	0.0385765468020728	0.0	0.0343428092250767	0.08341110973931	144.4666667	0:LIVING
-source_dataset:CHP-126													83.53333333	1:DECEASED
-source_dataset:KTCTL-1M													35.36666667	1:DECEASED
-source_dataset:TE-5	0.0366813622057351	0.0574097299569007	0.0900076465298708	0.130231207554743	0.0135790823280882	0.0055021063797262	0.0740220544327983	0.0516114057215029	0.0257467919008146	0.0215053471124296	0.0480948022894985	0.269357832183848	143.6	1:DECEASED
-source_dataset:EB3 [Human Burkitt lymphoma]	0.117845871001186	0.0069792245838784	0.106795119795463	0.116074591596646	0.0631158564519489	0.0	0.0954416861004747	0.0029869168163324	0.0012704563740367	0.0	0.111466187376426	0.258718428876902	23.9	0:LIVING
-source_dataset:HEC-1	0.0	0.0870761859380532	0.0097331388354801	0.003537699598482	0.0291182181456424	0.0	0.0375277040537696	0.0120410665574334	0.019604333425516	0.0046102197673638	0.080341395148694	0.162506497099668	102.0666667	0:LIVING
-source_dataset:NUGC-3	0.0671027331033016	0.23977326447954	0.231934630416341	0.201198031262588	0.0057428012905087	0.0397768658365836	0.088921483803944	0.0708094061036489	0.0948252256624489	0.0729485505667814	0.0442422278762252	0.408972977458665	49.56666667	1:DECEASED
-source_dataset:EW-24	0.0264271192785393	0.0039770424692694	0.290460443750475	0.0489135583575455	0.0	0.0093472597045784	0.0982519797002821	0.0531100724461812	0.0042710135483333	0.0	0.0453749370086662	0.398962442015848	17.83333333	0:LIVING
-source_dataset:EFM-19	0.03450227797394	0.0027004650868092	0.0123275685333175	0.111273484314508	0.0328251131974957	0.152556928876367	0.101701854703237	0.0096698973809006	0.0824729248083269	0.0576780082720597	0.0889303149203382	0.142147567259493	204.2	1:DECEASED
-source_dataset:MHH-PREB-1	0.0056016686335372	0.0845859251173788	0.124041599162577	0.0870321210679636	0.0140002686122394	0.0410443160009252	0.164759240313728	0.0057302001258648	0.0485477027348424	0.0	0.114781878363679	0.175871661818716	21.06666667	1:DECEASED
-source_dataset:PA-1	0.0	0.126645238104914	0.153479384552727	0.0827683146362719	0.0	0.0368138583217264	0.149891297763914	0.0385156889926094	0.0955414333587079	0.040471716915113	0.139281692769646	0.352964942836964	155.4	0:LIVING
-source_dataset:ME-1 [Human leukemia]	0.0	0.006385161152713	0.0581620478735128	0.0072473899169195	0.006289434158014	0.0042634941128583	0.0555661065270014	0.200509072033167	0.0073220445917031	0.185049655765616	0.0874726675508334	0.151564360094103	205.7333333	0:LIVING
-source_dataset:NCI-H196	0.03082105580162	0.10600839913528	0.0181594541870985	0.0	0.0	0.0	0.0908357643658376	0.0006937475530779	0.0558783983888229	0.0195623464522077	0.0368678686825519	0.0149860784990341	38.03333333	1:DECEASED
-source_dataset:A-673	0.0010805411017356	0.055179000489131	0.354270675906642	0.0217881855073444	0.0	0.12984374629934	0.143967992292495	0.0947711912329061	0.0404369917098152	0.0786268292761396	0.0762308207200617	0.52071544733925	173.6	1:DECEASED
-source_dataset:JVM-2	0.0479265062405519	0.0638842930736179	0.28865626571462	0.0657449857889388	0.088887509206207	0.136040681478755	0.345728922498992	0.114501359976157	0.117380375044886	0.126188427451124	0.114314218504868	0.479027131463465	16.73333333	1:DECEASED
-source_dataset:AM-38	0.0536457452386115	0.0096849709655602	0.180237741723468	0.0228716412381702	0.0	0.009759297726037	0.0883876210933476	0.272347278886785	0.180221756810487	0.124625323515681	0.0913658770095074	0.329343604944104	63.2	1:DECEASED
-source_dataset:NCI-H345	0.0687254248657827						0.0312769150331269	0.103404234862909		0.0		0.0087151337095621	225.5	0:LIVING
-source_dataset:NCI-H841	0.053574457063515	0.112861159454981	0.0547369690270067	0.106811550614197	0.035399468561401	0.0393198788940025	0.0633116587135247	0.0	0.025105441761682	0.0932090911099185	0.084240823865105	0.158999749405044	50.76666667	1:DECEASED
-source_dataset:SK-N-SH		0.0525748988375155	0.254991347186096	0.089171084873008	0.0295310821329998	0.0	0.103937217097206	0.0249456825337022	0.0257794001005369		0.108236511789003		68.2	1:DECEASED
-source_dataset:Ishikawa (Heraklio) 02 ER-													24.9	1:DECEASED
-source_dataset:BICR 31													115.3	0:LIVING
-source_dataset:MOLM-13	0.130247821325521	0.214342993596823	0.4476094588001	0.0749067058335512	0.0467027081886607	0.181179803898745	0.464050807900859	0.149864945863373	0.0222222347149344	0.158692555035458	0.958958815223193	0.569303732144603	90.8	1:DECEASED
-source_dataset:NCI-H2342		0.0755230221011622	0.0030073958194482	0.0073011887829907	0.0266562039962546	0.0	0.0485081072332211	0.0294454587248274	0.05042168957277		0.204788840542511		76.13333333	1:DECEASED
-source_dataset:HuCC-T1	0.0507443202763358	0.15750441624725	0.104568766890444	0.178734875500363	0.103323224637988	0.134962038663971	0.0546791594987109	0.229633002923161	0.0886952355157449	0.181967958774021	0.146542819955029	0.123582108033019	108.1666667	0:LIVING
-source_dataset:C-33 A	0.0630337622238665	0.0934427645635586	0.188426937606088	0.143746999187892	0.0319701705352335	0.0603329467756193	0.0464606617116402	0.0721911781726206	0.106576454337409	0.122187012693371	0.153421704207624	0.312937278127398	176.6	0:LIVING
-source_dataset:LOX-IMVI	0.0835735781666339	0.120280559181888	0.184587182809544	0.0898363602129966	0.0	0.109813395354437	0.117291104030206	0.180008087277961	0.170242789207695	0.26651010625017	0.0452648304368062	0.482072801866741	44.76666667	1:DECEASED
-source_dataset:SaOS-2	0.0341563942465052	0.0	0.108860813297106	0.0380027008924374	0.0021251850363549	0.0167745387026029	0.0589525935446958	0.0267087354951326	0.030177782586067	0.0360774242461995	0.0812958848315628	0.324839635358458	150.5666667	0:LIVING
-source_dataset:FU-OV-1		0.0949714158265014	0.004521101628147	0.128700863299832	0.1081587353784	0.0137397635368294	0.0564058496058183	0.127099586083634	0.0		0.0177399162719194		149.4	0:LIVING
-source_dataset:NCI-H748													171.6333333	1:DECEASED
-source_dataset:L-540	0.0522315889642514	0.0	0.263368130050943	0.083303555872629	0.0374609371080811	0.0151783663232376	0.0374475129374788	0.0329792290354737	0.0599647335597436	0.0	0.135782902323365	0.506889472991247	42.96666667	1:DECEASED
-source_dataset:MOLT-4	0.0705602483320121	0.0657527879266316	0.282995810747186	0.0398030108399176	0.0402250175888835	0.0448015720017699	0.054718433850882	0.15613228046436	0.0046588718497031	0.0488642887511412	0.0458788733021524	0.208018109863527	81.13333333	1:DECEASED
-source_dataset:GaMG	0.0276219842673586	0.0	0.0635235234913458	0.0361411339466697	0.0077433612324768	0.0284914230317315	0.0780149772205657	0.0108472930100279	0.0100751830748496	0.0429010291390658	0.0262690716904805	0.108511026511649	142.4333333	0:LIVING
-source_dataset:HCC1806	0.123570205041852	0.173135587844158	0.18925613922272	0.147618255196053	0.0208788726528446	0.220257521804702	0.0028060725508403	0.118309777511604	0.0181695065397814	0.208356631521625	0.0430214770643706	0.283086132464175	19.16666667	1:DECEASED
-source_dataset:NCI-H2087	0.0598623789138836	0.052555593518512	0.134016864775793	0.0248542699047053	0.0366153477421967	0.0116179358012912	0.0778176127570184	0.205315722883988	0.0102019835491889	0.222082251146459	0.0371149171150047	0.24027461138618	176.3666667	0:LIVING
-source_dataset:NCI-H630													89.96666667	0:LIVING
-source_dataset:HeLa	0.0173636616675455	0.0024432496160568	0.0378144991546205	0.0267015391984871	0.0855025014242918	0.0759989736999507	0.0718262653316272	0.0117641308889242	0.021366983905402	0.0111842368396232	0.0840343149624429	0.0741861001093092	128.5333333	0:LIVING
-source_dataset:TT		0.0546793713530746	0.0	0.0848903782730019	0.055160615100805	0.0	0.152226683857963	0.0509237393274914	0.0		0.0716232037510434		130.7	1:DECEASED
-source_dataset:VM-CUB-1	0.118305573095123	0.0391537232636123	0.261642277019221	0.0382369582410457	0.0310983299668832	0.0414757415315526	0.109321427601309	0.0639193845748015	0.0701857643650663	0.149709658287893	0.049943474657317	0.42374069988903	75.4	0:LIVING
-source_dataset:SW837	0.0510559595950419	0.0682815836939045	0.0360651519879889	0.061087968025751	0.0	0.0	0.0261274554170076	0.0995275517003429	0.0798752357155751	0.156822300123016	0.0168327635088781	0.0645463334018284	89.9	1:DECEASED
-source_dataset:SCaBER													35.23333333	1:DECEASED
-source_dataset:RKO	0.0622786552532289	0.0	0.0720049092788532	0.0127790708145867	0.07808210136027	0.0387963057661364	0.119129244786476	0.136798921068002	0.108075827503802	0.0303610257720739	0.0894273249215849	0.305398339340396	164.3333333	0:LIVING
-source_dataset:KLE	0.0	0.0713855462126308	0.0385098601311131	0.0409206914468001	0.0095650705465144	0.0	0.0358492527781556	0.0092178391604894	0.0687198790940871	0.0520191550276694	0.0633442476916273	0.0332481121899216	112.0	0:LIVING
-source_dataset:CML-T1	0.194709574048942	0.104949347378575	0.444927103122807	0.123862320126177	0.729896783143692	0.143769493513966	0.334616986203252	0.109011501085864	0.0661720330078647	0.0293856738380277	0.135237500019012	0.763588772529923	179.1	1:DECEASED
-source_dataset:D-566MG													188.3666667	0:LIVING
-source_dataset:Lu-134-A													174.5666667	1:DECEASED
-source_dataset:PEO1	0.0	0.0514665954698071	0.0345269579176818	0.0473678061612004	0.0	0.0032591630788621	0.0975007334684734	0.102424017927717	0.0271677044508502	0.0917437793067788	0.0080774091903653	0.347839282900094	74.1	1:DECEASED
-source_dataset:MDA-MB-436	0.0021713351370693	0.0160139240432495	0.0600581375616421	0.0437710019237604	0.0211668464665695	0.0943793825427865	0.0614282998241718	0.0055328549757945	0.0097005269587001	0.0329202351408974	0.0703896229519607	0.124004612699797	93.0	1:DECEASED
-source_dataset:NCI-H660													57.3	1:DECEASED
-source_dataset:NCI-H2126													150.6	1:DECEASED
-source_dataset:BFTC-909		0.109937893184019	0.116626947056251	0.130482050579485	0.118463647955596	0.0930377529404642	0.200606733172326	0.0889137265618575	0.0179073346267343	0.0778345340749247	0.0686722168008312	0.31622392477893	143.1666667	0:LIVING
-source_dataset:MDA-MB-468	0.117846367409064	0.108740338558122	0.101909960993592	0.181128023191625	0.0607198230173447	0.168909748801876	0.0466754316098848	0.0365570151715148	0.100719071686044	0.0424110892102114	0.131345835222727	0.433616889588349	87.23333333	1:DECEASED
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-source_dataset:HuO-3N1	0.0141074225149683	0.057874772605626	0.0092904036092176	0.0104218871342111	0.0748977263144086	0.0	0.0139481179848288	0.0	0.0311982222600123	0.0027341357280332	0.0096128040409571	0.100898376518842	55.4	1:DECEASED
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-source_dataset:NCI-H1304													14.13333333	1:DECEASED
-source_dataset:Lu-165	0.0	0.0213726597462009	0.0768308216929416	0.0198028788494999	0.0091796409301847	0.0	0.0115064427740289	0.0119684835241778	0.0285109683907118	0.0	0.0572252293905008	0.0795010609562632	198.3	0:LIVING
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-source_dataset:KOSC-2													70.6	1:DECEASED
-source_dataset:LB771-HNC	0.0	0.093266772649973	0.0038083385631902	0.0087723507298257	0.0686704132737175	0.0	0.0541764117525125	0.0228514491083768	0.0728556112031455	0.0016721128096751	0.098507023929859	0.0424892191640752	11.06666667	1:DECEASED
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-source_dataset:KNS-42													86.53333333	1:DECEASED
-source_dataset:COLO 680N	0.0270586368984846	0.0566319333500966	0.0393853565666137	0.0641309651304849	0.051060298076574	0.0	0.0068035186846049	0.0481658232773336	0.0370970692870579	0.0964236944468518	0.0056727220744791	0.116740108466174	16.7	1:DECEASED
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-source_dataset:MHH-NB-11													16.7	1:DECEASED
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-source_dataset:LB647-SCLC													199.0333333	1:DECEASED
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-source_dataset:TE-10													207.4666667	1:DECEASED
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-source_dataset:OV7													55.2	1:DECEASED
-source_dataset:SK-MEL-31													35.53333333	1:DECEASED
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-source_dataset:Hep 3B2.1-7	0.0209391983186268		0.108036610142722					0.102722010719415	0.11138877110569	0.136619725541138		0.0718912412080872	27.4	1:DECEASED
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-source_dataset:SJNB-6													95.86666667	0:LIVING
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-source_dataset:TK [Human B-cell lymphoma]	0.0037500533284072	0.007591205787772	0.10606634737132	0.0	0.0472165427548219	0.0	0.0007554505208101	0.0092325653416665	0.0	0.0	0.0776303900034359	0.443492555050379	226.0666667	0:LIVING
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-source_dataset:CMK													30.13333333	1:DECEASED
-source_dataset:NCI-H2052	0.0439235354251206	0.0973333751484498	0.0740561959975351	0.0110730343929227	0.018918379999847	0.0075379007469329	0.0410512173987626	0.0199616844057743	0.0329565402125384	0.0	0.0159796302152254	0.130442946136211	81.5	1:DECEASED
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-source_dataset:NCI-H510A													194.1666667	1:DECEASED
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-source_dataset:EMC-BAC-1													77.66666667	1:DECEASED
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-source_dataset:GT3TKB													107.2666667	1:DECEASED
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-source_dataset:NCI-H292													196.8666667	0:LIVING
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-source_dataset:OCI-Ly19													140.2	0:LIVING
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-source_dataset:D-542MG													11.86666667	1:DECEASED
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-source_dataset:JEG-3													85.4	1:DECEASED
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-source_dataset:Onda 10													15.06666667	1:DECEASED
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-source_dataset:NMC-G1													49.3	1:DECEASED
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-source_dataset:Hs 940.T													213.3666667	0:LIVING
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-source_dataset:COLO 800													81.1	1:DECEASED
-source_dataset:U-698-M	0.0368565419295806	0.0	0.249940821305058	0.127764592260805	0.0117094198500046	0.143023815917566	0.0859910746055594	0.0489677225778549	0.0326936824403626	0.0	0.24624444709326	0.319175862263143	16.16666667	1:DECEASED
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-source_dataset:COLO 783													229.9	0:LIVING
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-source_dataset:NCI-H146													185.1666667	0:LIVING
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-source_dataset:PE/CA-PJ15													73.46666667	1:DECEASED
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-source_dataset:NU-DUL-1													53.63333333	1:DECEASED
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-source_dataset:LB1047-RCC													111.1	1:DECEASED
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-source_dataset:JAR													20.26666667	0:LIVING
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-source_dataset:BB30-HNC													210.9666667	1:DECEASED
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-source_dataset:RPMI-2650													254.9333333	0:LIVING
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-source_dataset:KTCTL-26A													88.33333333	1:DECEASED
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-source_dataset:SUP-B8													144.4	0:LIVING
-source_dataset:HARA [Human squamous cell lung carcinoma]	0.0196607846009293	0.0488014352844363	0.0985425505326834	0.227032652527014	0.0409185473611114	0.116924038976644	0.19028485528748	0.0757227348952206	0.0808962988560895	0.0362057756020376	0.0415864896721363	0.295227903044844	37.86666667	1:DECEASED
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-source_dataset:NCI-H810	0.0221677675469343	0.0369882402103835	0.0666921612274232	0.0295756243799065	0.0	0.0095475809600023	0.0672370325337931	0.0	0.0661827808123533	0.0	0.170071946307782	0.23793536549141	113.2	1:DECEASED
-source_dataset:NCI-H1963	0.0	0.009679338851408	0.0816279536914229	0.0909915051773104	0.0520168557331762	0.0214087444861686	0.0366307701781396	0.0319906293612506	0.0	0.0	0.19251917782936	0.112898623452358	46.16666667	1:DECEASED
-source_dataset:EW-11	0.0310533138419536	0.0166032356760586	0.320655354452014	0.0523099883492327	0.0420194492626544	0.0867930575953994	0.184158482872382	0.0	0.0482696141368548	0.0	0.115189315038385	0.440799073938595	58.43333333	0:LIVING
-source_dataset:SK-OV-3	0.0272231291113518	0.139086315500833	0.129560746556663	0.214347669994645	0.0089933458126264	0.0033836882588469	0.0172950176553656	0.0340962885356019	0.0839628070968583	0.122716461914492	1.8313362616591202e-15	0.158336428026026	65.86666667	0:LIVING
-source_dataset:COLO 205	0.136365000084138	0.0344152288339466	0.107988908936341	0.025044651056274	0.0701984081907037	0.0466105393351194	0.191510728104659	0.322189280160171	0.317531126238421	0.475293248176166	0.146059382003036	0.0816244272131876	221.9	0:LIVING
+sample	Crizotinib	Erlotinib	Irinotecan	Lapatinib	Nilotinib	Paclitaxel	Palbociclib	PD-0325901	PLX4720	Selumetinib	Sorafenib	Topotecan	OS_MONTHS	OS_STATUS	class
+source_dataset:NCI-H1770													43.1	1	buz
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+source_dataset:Kelly	0.0798387095208873	0.0926893742438769	0.173013039987695	0.101237703906914	0	0.0104624513720856	0.0898392517986342	0.060911679498632	0.0050168414893049	0.073373861381777	0.130170998278001	0.46903463855175	144.3333333	0	qux
+source_dataset:WSU-DLCL2		0.0712472440111968	0.320791588631048	0.075583344695118	0.0566156129514686	0.1251308081892	0.280535976167446	0.0022971179572881	0.222030383410802		0.0692838823668002		157.5333333	0	qux
+source_dataset:VMRC-RCZ	0.0043994430625854	0.0788264548067618	0.0047243492140753	0.0374860112109303	0	0	0.0427801906206174	0.0383337227413034	0.0716181557165881	0.0355712355433095	0.0070204186294004	0.0496543874808994	78.7	0	qux
+source_dataset:NCI-H2810	0.100349825443384		0.142990095481615					0.0403945820817186	0.0373131064748271	0.125512734099215		0.302095879126078	126.6666667	1	buz
+source_dataset:Caki-1													111	0	qux
+source_dataset:BB65-RCC													85.73333333	1	buz
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+source_dataset:NB(TU)1	0.0183000105738514	0.112469509484137	0.0934125004649796	0.100393268497024	0.051794993610324	2.96987950035085E-16	0.178847597660038	0.190320453138889	0.000802305363319	0.392653469603823	0.0880151929965022	0.31628423085018	125.6	1	buz
+source_dataset:NCI-H28	0.110541730531776	0.146562151339564	0.0321330174534193	0.0997984073393081	0.0279453662581887	0	0.255436122432369	0.0923759567359449	0.02858073731996	0.0409939242963945	0.0199917979001204	0.111327681732459	138.3333333	0	qux
+source_dataset:A-704	0.118008145616614	0.0470571542875395	0.142427763365562	0.0635334577085759	0.112364715745615	0.0037607652262141	0.0930048340888921	0.0395032603987703	0.0816282016406401	0.0337644599588087	0.108355692202339	0.0952555225256304	256	0	qux
+source_dataset:C32 [Human melanoma]	0.0345773709623989	0.065620750366615	0.132619433213006	0.0077983791389459	0.0465478218296898	0	0.0651582070145086	0.382092565090217	0.31887745203201	0.4406039046368	0.0250336462655307	0.323631553277508	58.66666667	1	buz
+source_dataset:SW1463	0.0239455360650954	0.148997158031401	0.0994807758806709	0.163907424238169	0.00524820235804	0.0223518540166035	0.176702936008273	0.15204669548728	0.0423852040911128	0.172151370740176	0.0914584929232963	0.1664640804271	195.5333333	0	qux
+source_dataset:NCI-H211	0.0258940850956053	0.0427341905043995	0.18843664333191	0.120845509791877	0.010433896017643	0.123756430652602	0.271016748896732	0.138539034251607	0.0079301460652363	0.0168112607667866	0.148764558287982	0.210533512753385	60.26666667	1	buz
+source_dataset:CTB-1													32.03333333	1	buz
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+source_dataset:CHP-126													83.53333333	1	buz
+source_dataset:KTCTL-1M													35.36666667	1	buz
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+source_dataset:EB3 [Human Burkitt lymphoma]	0.117845871001186	0.0069792245838784	0.106795119795463	0.116074591596646	0.0631158564519489	0	0.0954416861004747	0.0029869168163324	0.0012704563740367	0	0.111466187376426	0.258718428876902	23.9	0	qux
+source_dataset:HEC-1	0	0.0870761859380532	0.0097331388354801	0.003537699598482	0.0291182181456424	0	0.0375277040537696	0.0120410665574334	0.019604333425516	0.0046102197673638	0.080341395148694	0.162506497099668	102.0666667	0	qux
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+source_dataset:ME-1 [Human leukemia]	0	0.006385161152713	0.0581620478735128	0.0072473899169195	0.006289434158014	0.0042634941128583	0.0555661065270014	0.200509072033167	0.0073220445917031	0.185049655765616	0.0874726675508334	0.151564360094103	205.7333333	0	qux
+source_dataset:NCI-H196	0.03082105580162	0.10600839913528	0.0181594541870985	0	0	0	0.0908357643658376	0.0006937475530779	0.0558783983888229	0.0195623464522077	0.0368678686825519	0.0149860784990341	38.03333333	1	buz
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