Mercurial > repos > bgruening > flexynesis
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(-) [+] |
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--- 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 ${on_string}: results"> <discover_datasets pattern="(?P<name>.+)\.tabular$" 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 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--- 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 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