changeset 0:d3c493fcb943 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 8beed1a19fcd9dc59f7746e1dfa735a2d5f29784"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,35 @@
+<macros>
+    <token name="@VERSION_SUFFIX@">3</token>
+    <!-- The ESet inspector/constructor and MuSiC tool can have
+         independent Galaxy versions but should reference the same
+         package version always. -->
+    <token name="@TOOL_VERSION@">0.1.1</token>
+    <token name="@RDATATYPE@">rdata</token>
+    <!-- Below is disabled until Galaxy supports it. Still not present
+         in 21.09
+         <token name="@RDATATYPE@">rdata.eset</token>
+    -->
+    <xml name="requirements">
+        <requirements>
+            <requirement type="package" version="@TOOL_VERSION@" >music-deconvolution</requirement>
+            <requirement type="package" version="0.9.3" >r-cowplot</requirement>
+            <requirement type="package" version="1.4.4" >r-reshape2</requirement>
+        </requirements>
+    </xml>
+    <xml name="validator_index_identifiers" >
+        <validator type="regex" message="FORMAT terms separated by commas">^(([A-Za-z0-9+_ -]+)\s?,?)*$</validator>
+    </xml>
+    <xml name="validator_text" >
+        <validator type="regex" message="No commas allowed">^(([A-Za-z0-9+_ -]+)\s?)+$</validator>
+    </xml>
+    <xml name="celltypes_macro" >
+        <param name="celltypes" type="text" optional="true" value=""
+               label="Comma list of cell types to use from scRNA dataset" help="If blank, then use all available cell types." >
+            <expand macro="validator_index_identifiers" />
+        </param>
+    </xml>
+    <xml name="validator_text_and_urls" >
+        <validator type="regex" message="No commas or apostrophes allowed">^(([A-Za-z0-9+_ -@.:/]+)\s?)*$</validator>
+    </xml>
+</macros>
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/music_compare.xml	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,208 @@
+<tool id="music_compare" name="MuSiC Compare" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@"
+      profile="21.09" license="GPL-3.0-or-later" >
+    <description>estimate and compare cell type proportions in multiple sets of bulk RNA-seq data</description>
+    <macros>
+        <import>macros.xml</import>
+        <macro name="test_input">
+            <repeat name="scrna_groups">
+                <param name="name" value="One" />
+                <param name="scrna_eset" value="Mousesubeset.degenesonly2.half.rds" />
+                <repeat name="bulk_groups" >
+                    <param name="name" value="Two" />
+                    <param name="bulk_eset" value="Mousebulkeset.rds" />
+                    <param name="factor_group" value="Control" />
+                </repeat>
+                <repeat name="bulk_groups" >
+                    <param name="name" value="Three" />
+                    <param name="bulk_eset" value="Mousebulkeset.rds" />
+                    <param name="factor_group" value="Pheno1" />
+                </repeat>
+            </repeat>
+            <repeat name="scrna_groups">
+                <param name="name" value="A" />
+                <param name="scrna_eset" value="Mousesubeset.degenesonly2.half.rds" />
+                <repeat name="bulk_groups" >
+                    <param name="name" value="B" />
+                    <param name="bulk_eset" value="Mousebulkeset.rds" />
+                    <param name="factor_group" value="Control" />
+                </repeat>
+            </repeat>
+        </macro>
+    </macros>
+    <expand macro="requirements" />
+    <command detect_errors="exit_code" ><![CDATA[
+mkdir report_data &&
+Rscript --vanilla '$__tool_directory__/scripts/compare.R' '$conf'
+]]></command>
+    <configfiles>
+        <configfile name="conf" >
+null_str_vec = function(gstr){
+   tokens = unlist(as.vector(strsplit(gstr, split=",")))
+   if (length(tokens) == 0){
+      return(NULL)
+   }
+   if (length(tokens) == 1){
+      return(tokens[[1]])
+   }
+   return(tokens)
+}
+
+files = list(
+#for $s, $scgroup in enumerate($scrna_groups):
+    '$scgroup.name' = list(
+        dataset = '$scgroup.scrna_eset',
+        label_cell = null_str_vec('$scgroup.adv.celltypes_label'),
+        label_sample = null_str_vec('$scgroup.adv.samples_label'),
+        celltype = null_str_vec('$scgroup.adv.celltypes'),
+        bulk = list(
+    #for $b, $bulkgroup in enumerate($scgroup.bulk_groups):
+            '$bulkgroup.name' = list(
+                dataset = null_str_vec('$bulkgroup.bulk_eset'),
+                factor_group = null_str_vec('$bulkgroup.factor_group'),
+                pheno_facts = null_str_vec('$bulkgroup.adv.phenotype_factors'),
+                pheno_excl = null_str_vec('$bulkgroup.adv.phenotype_factors_always_exclude')
+        #if $b &lt; len($scgroup.bulk_groups) - 1:
+            ),
+        #else
+            )
+        #end if
+    #end for
+    #if $s &lt; len($scrna_groups) - 1:
+        )
+    ),
+    #else
+        )
+    )
+    #end if
+#end for
+)
+
+heat_grouped_p = as.logical('$heat_grouped_p')
+out_filt = list(cells = null_str_vec('$filter.out_list_cells'),
+                facts = null_str_vec('$filter.out_list_facts'))
+est_method = null_str_vec('$est_method')
+
+out_heatmulti_pdf = '$out_heatmulti_pdf'
+out_heatsumm_pdf = '$out_heatsumm_pdf'
+        </configfile>
+    </configfiles>
+    <inputs>
+        <!-- Define single cell groups for sets of bulk datasets -->
+        <repeat name="scrna_groups" title="New scRNA Group" min="1"
+                help="Cell type proportion comparisons are performed between bulk datasets
+                      in each scRNA group. A second summary is performed comparing all cell
+                      type proportions across all groups." >
+            <!-- Single Cell Options -->
+            <param name="name" label="Name of scRNA Dataset" type="text" value="" />
+            <param name="scrna_eset" label="scRNA Dataset" type="data" format="@RDATATYPE@" />
+            <section name="adv" title="Advanced scRNA Parameters" >
+                <param name="celltypes_label" type="text" value="cellType"
+                       label="Cell Types Label from scRNA dataset" >
+                    <expand macro="validator_text" />
+                </param>
+                <param name="samples_label" type="text" value="sampleID"
+                       label="Samples Identifier from scRNA dataset" >
+                    <expand macro="validator_text" />
+                </param>
+                <expand macro="celltypes_macro" />
+            </section>
+            <repeat name="bulk_groups" title="Bulk Datasets in scRNA Group" min="1"
+                    help="Choose bulk RNA datasets" >
+                <!-- Bulk Options -->
+                <param name="name" label="Name of Bulk Dataset" type="text" value="" />
+                <param name="bulk_eset" label="Bulk RNA Dataset" type="data" format="@RDATATYPE@" />
+                <param name="factor_group" type="text" label="Factor Name" optional="false"
+                       help="Name of column in phenotype data containing factor values. If column does not exist, it is a new factor that is applied to all samples in the dataset. Plots will be coloured by these factors." />
+                <section name="adv" title="Advanced Bulk Parameters" >
+                    <param name="phenotype_factors" type="text"
+                           label="Phenotype factors"
+                           help="List of phenotypes factors to be used in the linear regression. Please make sure that each factor has more than one unique value. Names correspond to column names in the bulk RNA dataset phenotype table. If blank, then treat all bulk phenotype columns as factors." >
+                        <expand macro="validator_index_identifiers" />
+                    </param>
+                    <param name="phenotype_factors_always_exclude" type="text"
+                           label="Excluded phenotype factors"
+                           help="List of phenotype factors to always exclude in the analysis"
+                           value="sampleID,SubjectName" >
+                        <expand macro="validator_index_identifiers" />
+                    </param>
+                </section>
+            </repeat>
+        </repeat>
+        <param name="est_method" type="select" label="Method to use" help="One to compare across all" >
+            <option value="MuSiC" selected="true" >MuSiC</option>
+            <option value="NNLS" selected="true" >NNLS</option>
+        </param>
+        <param name="heat_grouped_p" type="boolean" label="Individual heatmaps grouped by scRNA dataset?" checked="true" />
+        <section name="filter" title="Filter Summary Plots" >
+            <param name="out_list_cells" type="text" label="Show only these cell types (blank for all)"
+                   help="Comma-delimited list. Cell types given in the above scRNA datasets are still used for deconvolution (bulk reads are still assigned to discrete cell types), but we merely select the ones we want to show." />
+            <param name="out_list_facts" type="text" label="Show only these factors (blank for all)"
+                   help="Comma-delimited list. Factors must exist in those inferred from the above bulk datasets." />
+        </section>
+    </inputs>
+    <outputs>
+        <data name="out_heatmulti_pdf" format="pdf" label="${tool.name} on ${on_string}: Individual Heatmaps (${est_method})" />
+        <data name="out_heatsumm_pdf" format="pdf" label="${tool.name} on ${on_string}: Summarized Plots (${est_method})" />
+        <collection name="dtables" type="list" label="${tool.name} on ${on_string}: Tables (${est_method})" >
+            <discover_datasets pattern="values_(?P&lt;designation&gt;.+)\.tabular" format="tabular" directory="report_data" />
+        </collection>
+        <collection name="stats" type="list" label="${tool.name} on ${on_string}: Stats (${est_method})" >
+            <discover_datasets pattern="stats_(?P&lt;designation&gt;.+)\.tabular" format="tabular" directory="report_data" />
+        </collection>
+    </outputs>
+    <tests>
+        <test expect_num_outputs="4" >
+            <!-- NNLS Test with severe output filtering -->
+            <expand macro="test_input" />
+            <param name="est_method" value="NNLS" />
+            <param name="heat_grouped_p" value="true" />
+            <section name="filter" >
+                <param name="out_list_cells" value="PT,Podo,Fib,Endo" />
+                <param name="out_list_facts" value="APOL1,Pheno1" />
+            </section>
+            <output name="out_heatsumm_pdf" value="out_filt1.pdf" compare="sim_size" />
+        </test>
+        <test expect_num_outputs="4" >
+            <!-- NNLS Test with only factor filtering -->
+            <expand macro="test_input" />
+            <param name="est_method" value="NNLS" />
+            <param name="heat_grouped_p" value="true" />
+            <section name="filter" >
+                <param name="out_list_facts" value="APOL1,Pheno1" />
+            </section>
+            <output name="out_heatmulti_pdf" value="out_heat2.pdf" compare="sim_size" />
+        </test>
+        <test expect_num_outputs="4" >
+            <!-- MuSiC Test with no filtering -->
+            <expand macro="test_input" />
+            <param name="est_method" value="MuSiC" />
+            <param name="heat_grouped_p" value="true" />
+            <output_collection name="dtables" count="3">
+                <element name="Data Table" ftype="tabular" >
+                    <assert_contents>
+                        <has_text_matching expression="B\:\:APOL1\.G1NF42\s+PT\s+B\s+APOL1\s+0.56\d+\s+19035\d+" />
+                    </assert_contents>
+                </element>
+            </output_collection>
+            <output_collection name="stats" count="6">
+                <element name="Three: Read Props" ftype="tabular" >
+                    <assert_contents>
+                        <has_text_matching expression="T lymph(\s+0)+" />
+                    </assert_contents>
+                </element>
+            </output_collection>
+        </test>
+    </tests>
+    <help><![CDATA[
+MuSiC Compare produces boxplots and heatmaps of cell type proportions within bulk RNA datasets, learned from single-cell RNA datasets.
+
+To discover the proportion of single-cell cell types within a bulk RNA dataset, create a scRNA group for each scRNA dataset, and the bulk datasets that you wish to discover the types in.
+
+Phenotype factors can also be specified, either for the entire dataset or for specific samples within a dataset given by a phenotype data column identifier.
+
+The resulting plots will combine all the bulk datasets and their learned cell type proportions into several summarizing plots.
+    ]]></help>
+    <citations>
+        <citation type="doi">https://doi.org/10.1038/s41467-018-08023-x</citation>
+    </citations>
+</tool>
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/compare.R	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,440 @@
+suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
+suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
+suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
+suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
+## We use this script to estimate the effectiveness of proportion methods
+
+## Load Conf
+args <- commandArgs(trailingOnly = TRUE)
+source(args[1])
+
+method_key <- list("MuSiC" = "est_music",
+                   "NNLS" = "est_nnls")[[est_method]]
+
+
+scale_yaxes <- function(gplot, value) {
+    if (is.na(value)) {
+        gplot
+    } else {
+        gplot + scale_y_continuous(lim = c(0, value))
+    }
+}
+
+
+set_factor_data <- function(bulk_data, factor_name = NULL) {
+    if (is.null(factor_name)) {
+        factor_name <- "None" ## change to something plottable
+    }
+    pdat <- pData(bulk_data)
+    sam_fact <- NULL
+    if (factor_name %in% colnames(pdat)) {
+        sam_fact <- cbind(rownames(pdat),
+                          as.character(pdat[[factor_name]]))
+        cat(paste0("   - factor: ", factor_name,
+                   " found in phenotypes\n"))
+    } else {
+        ## We assign this as the factor for the entire dataset
+        sam_fact <- cbind(rownames(pdat),
+                          factor_name)
+        cat(paste0("   - factor: assigning \"", factor_name,
+                   "\" to whole dataset\n"))
+    }
+    colnames(sam_fact) <- c("Samples", "Factors")
+    return(as.data.frame(sam_fact))
+}
+
+## Due to limiting sizes, we need to load and unload
+## possibly very large datasets.
+process_pair <- function(sc_data, bulk_data,
+                         ctypes_label, samples_label, ctypes,
+                         factor_group) {
+    ## - Generate
+    est_prop <- music_prop(
+        bulk.eset = bulk_data, sc.eset = sc_data,
+        clusters = ctypes_label,
+        samples = samples_label, select.ct = ctypes, verbose = T)
+    ## -
+    estimated_music_props <- est_prop$Est.prop.weighted
+    estimated_nnls_props <- est_prop$Est.prop.allgene
+    ## -
+    fact_data <- set_factor_data(bulk_data, factor_group)
+    ## -
+    return(list(est_music = estimated_music_props,
+                est_nnls = estimated_nnls_props,
+                bulk_sample_totals = colSums(exprs(bulk_data)),
+                plot_groups = fact_data))
+}
+
+music_on_all <- function(files) {
+    results <- list()
+    for (sc_name in names(files)) {
+        cat(paste0("sc-group:", sc_name, "\n"))
+        scgroup <- files[[sc_name]]
+        ## - sc Data
+        sc_est <- readRDS(scgroup$dataset)
+        ## - params
+        celltypes_label <- scgroup$label_cell
+        samples_label <- scgroup$label_sample
+        celltypes <- scgroup$celltype
+
+        results[[sc_name]] <- list()
+        for (bulk_name in names(scgroup$bulk)) {
+            cat(paste0(" - bulk-group:", bulk_name, "\n"))
+            bulkgroup <- scgroup$bulk[[bulk_name]]
+            ## - bulk Data
+            bulk_est <- readRDS(bulkgroup$dataset)
+            ## - bulk params
+            pheno_facts <- bulkgroup$pheno_facts
+            pheno_excl <- bulkgroup$pheno_excl
+            ##
+            results[[sc_name]][[bulk_name]] <- process_pair(
+                sc_est, bulk_est,
+                celltypes_label, samples_label,
+                celltypes, bulkgroup$factor_group)
+            ##
+            rm(bulk_est) ## unload
+        }
+        rm(sc_est) ## unload
+    }
+    return(results)
+}
+
+plot_all_individual_heatmaps <- function(results) {
+    pdf(out_heatmulti_pdf, width = 8, height = 8)
+    for (sc_name in names(results)) {
+        for (bk_name in names(results[[sc_name]])) {
+            res <- results[[sc_name]][[bk_name]]
+            plot_hmap <- Prop_heat_Est(
+                data.matrix(res[[method_key]]), method.name = est_method) +
+                ggtitle(paste0("[", est_method, "Cell type ",
+                               "proportions in ",
+                               bk_name, " (Bulk) based on ",
+                               sc_name, " (scRNA)")) +
+                xlab("Cell Types (scRNA)") +
+                ylab("Samples (Bulk)") +
+                theme(axis.text.x = element_text(angle = -90),
+                      axis.text.y = element_text(size = 6))
+            print(plot_hmap)
+        }
+    }
+    dev.off()
+}
+
+merge_factors_spread <- function(grudat_spread, factor_groups) {
+    ## Generated
+    merge_it <- function(matr, plot_groups, valname) {
+        ren <- melt(lapply(matr, function(mat) {
+            mat["ct"] <- rownames(mat); return(mat)}))
+        ## - Grab factors and merge into list
+        ren_new <- merge(ren, plot_groups, by.x = "variable", by.y = "Samples")
+        colnames(ren_new) <- c("Sample", "Cell", valname, "Bulk", "Factors")
+        return(ren_new)
+    }
+    tab <- merge(merge_it(grudat$spread$prop, factor_groups, "value.prop"),
+                 merge_it(grudat$spread$scale, factor_groups, "value.scale"),
+                 by = c("Sample", "Cell", "Bulk", "Factors"))
+    return(tab)
+}
+
+
+plot_grouped_heatmaps <- function(results) {
+    pdf(out_heatmulti_pdf, width = 8, height = 8)
+    for (sc_name in names(results)) {
+        named_list <- sapply(
+            names(results[[sc_name]]),
+            function(n) {
+                ## We transpose the data here, because
+                ## the plotting function omits by default
+                ## the Y-axis which are the samples.
+                ##  Since the celltypes are the common factor
+                ## these should be the Y-axis instead.
+                t(data.matrix(results[[sc_name]][[n]][[method_key]]))
+            }, simplify = F, USE.NAMES = T)
+        named_methods <- names(results[[sc_name]])
+        ##
+        plot_hmap <- Prop_heat_Est(
+            named_list,
+            method.name = named_methods) +
+            ggtitle(paste0("[", est_method, "] Cell type ",
+                           "proportions of ",
+                           "Bulk Datasets based on ",
+                           sc_name, " (scRNA)")) +
+            xlab("Samples (Bulk)") +
+            ylab("Cell Types (scRNA)") +
+            theme(axis.text.x = element_text(angle = -90),
+                  axis.text.y = element_text(size = 6))
+        print(plot_hmap)
+    }
+    dev.off()
+}
+
+## Desired plots
+## 1. Pie chart:
+##  - Per Bulk dataset (using just normalised proportions)
+##  - Per Bulk dataset (multiplying proportions by nreads)
+
+unlist_names <- function(results, method, prepend_bkname=FALSE) {
+    unique(sort(
+        unlist(lapply(names(results), function(scname) {
+            lapply(names(results[[scname]]), function(bkname) {
+                res <- get(method)(results[[scname]][[bkname]][[method_key]])
+                if (prepend_bkname) {
+                    ## We *do not* assume unique bulk sample names
+                    ## across different bulk datasets.
+                    res <- paste0(bkname, "::", res)
+                }
+                return(res)
+            })
+        }))
+    ))
+}
+
+summarized_matrix <- function(results) {  # nolint
+    ## We assume that cell types MUST be unique, but that sample
+    ## names do not need to be. For this reason, we must prepend
+    ## the bulk dataset name to the individual sample names.
+    all_celltypes <- unlist_names(results, "colnames")
+    all_samples <- unlist_names(results, "rownames", prepend_bkname = TRUE)
+
+    ## Iterate through all possible samples and populate a table.
+    ddff <- data.frame()
+    ddff_scale <- data.frame()
+    for (cell in all_celltypes) {
+        for (sample in all_samples) {
+            group_sname <- unlist(strsplit(sample, split = "::"))
+            bulk <- group_sname[1]
+            id_sample <- group_sname[2]
+            for (scgroup in names(results)) {
+                if (bulk %in% names(results[[scgroup]])) {
+                    mat_prop <- results[[scgroup]][[bulk]][[method_key]]
+                    vec_counts <- results[[scgroup]][[bulk]]$bulk_sample_totals
+                    ## - We use sample instead of id_sample because we need to
+                    ##   extract bulk sets from the complete matrix later. It's
+                    ##   messy, yes.
+                    if (cell %in% colnames(mat_prop)) {
+                        ddff[cell, sample] <- mat_prop[id_sample, cell]
+                        ddff_scale[cell, sample] <- mat_prop[id_sample, cell] * vec_counts[[id_sample]] #nolint
+                    } else {
+                        ddff[cell, sample] <- 0
+                        ddff_scale[cell, sample] <- 0
+                    }
+                }
+            }
+        }
+    }
+    return(list(prop = ddff, scaled = ddff_scale))
+}
+
+flatten_factor_list <- function(results) {
+    ## Get a 2d DF of all factors across all bulk samples.
+    res <- c()
+    for (scgroup in names(results)) {
+        for (bulkgroup in names(results[[scgroup]])) {
+            dat <- results[[scgroup]][[bulkgroup]]$plot_groups
+            dat$Samples <- paste0(bulkgroup, "::", dat$Samples) #nolint
+            res <- rbind(res, dat)
+        }
+    }
+    return(res)
+}
+
+group_by_dataset <- function(summat) {
+    bulk_names <- unlist(
+        lapply(names(files), function(x) names(files[[x]]$bulk)))
+    mat_names <- colnames(summat$prop)
+    bd <- list()
+    bd_scale <- list()
+    bd_spread_scale <- list()
+    bd_spread_prop <- list()
+    for (bname in bulk_names) {
+        subs <- mat_names[startsWith(mat_names, paste0(bname, "::"))]
+        ## -
+        bd[[bname]] <- rowSums(summat$prop[, subs])
+        bd_scale[[bname]] <- rowSums(summat$scaled[, subs])
+        bd_spread_scale[[bname]] <- summat$scaled[, subs]
+        bd_spread_prop[[bname]] <- summat$prop[, subs]
+    }
+    return(list(prop = as.data.frame(bd),
+                scaled = as.data.frame(bd_scale),
+                spread = list(scale = bd_spread_scale,
+                              prop = bd_spread_prop)))
+}
+
+summarize_heatmaps <- function(grudat_spread_melt, do_factors) {
+    ## -
+    do_single <- function(grudat_melted, yaxis, xaxis, fillval, title,
+                          ylabs = element_blank(), xlabs = element_blank(),
+                          use_log = TRUE, size = 11) {
+        ## Convert from matrix to long format
+        melted <- grudat_melted ## copy?
+        if (use_log) {
+            melted[[fillval]] <- log10(melted[[fillval]] + 1)
+        }
+        return(ggplot(melted) +
+               geom_tile(aes_string(y = yaxis, x = xaxis, fill = fillval),
+                         colour = "white") +
+               scale_fill_gradient2(low = "steelblue", high = "red",
+                                    mid = "white", name = element_blank()) +
+               theme(axis.text.x = element_text(angle = -90, hjust = 0,
+                                                size = size)) +
+               ggtitle(label = title) + xlab(xlabs) + ylab(ylabs))
+    }
+
+    do_gridplot <- function(title, xvar, plot="both", ncol=2, size = 11) {
+        do_logged <- (plot %in% c("log", "both"))
+        do_normal <- (plot %in% c("normal", "both"))
+        plist <- list()
+        if (do_logged) {
+            plist[["1"]] <- do_single(grudat_spread_melt, "Cell", xvar,
+                                      "value.scale", "Reads (log10+1)",
+                                      size = size)
+            plist[["2"]] <- do_single(grudat_spread_melt, "Cell", xvar,
+                                      "value.prop", "Sample (log10+1)",
+                                      size = size)
+        }
+        if (do_normal) {
+            plist[["A"]] <- do_single(grudat_spread_melt, "Cell", xvar,
+                                      "value.scale", "Reads", use_log = F,
+                                      size = size)
+            plist[["B"]] <- do_single(grudat_spread_melt, "Cell", xvar,
+                                      "value.prop", "Sample", use_log = F,
+                                      size = size)
+        }
+        return(plot_grid(ggdraw() + draw_label(title, fontface = "bold"),
+                         plot_grid(plotlist = plist, ncol = ncol),
+                         ncol = 1, rel_heights = c(0.05, 0.95)))
+
+    }
+    p1 <- do_gridplot("Cell Types vs Bulk Datasets", "Bulk", "both", )
+    p2a <- do_gridplot("Cell Types vs Samples", "Sample", "normal", 1,
+                       size = 8)
+    p2b <- do_gridplot("Cell Types vs Samples (log10+1)", "Sample", "log", 1,
+                       size = 8)
+    p3 <- ggplot + theme_void()
+    if (do_factors) {
+        p3 <- do_gridplot("Cell Types against Factors", "Factors", "both")
+    }
+    return(list(bulk = p1,
+                samples = list(log = p2b, normal = p2a),
+                factors = p3))
+}
+
+summarize_boxplots <- function(grudat_spread, do_factors) {
+    common1 <- ggplot(grudat_spread, aes(x = value.prop)) + ggtitle("Sample") +
+        xlab(element_blank()) + ylab(element_blank())
+    common2 <- ggplot(grudat_spread, aes(x = value.scale)) + ggtitle("Reads") +
+        xlab(element_blank()) + ylab(element_blank())
+
+    A <- B <- list() #nolint
+    ## Cell type by sample
+    A$p1 <- common2 + geom_boxplot(aes(y = Cell, color = Bulk))
+    A$p2 <- common1 + geom_boxplot(aes(y = Cell, color = Bulk))
+    ## Sample by Cell type
+    B$p1 <- common2 + geom_boxplot(aes(y = Bulk, color = Cell)) +
+        ylab("Bulk Dataset")
+    B$p2 <- common1 + geom_boxplot(aes(y = Bulk, color = Cell)) +
+        ylab("Bulk Dataset")
+    ## -- Factor plots are optional
+    A$p3 <- B$p3 <- A$p4 <- B$p4 <- ggplot() + theme_void()
+
+    if (do_factors) {
+        A$p3 <- common1 + geom_boxplot(aes(y = Cell, color = Factors))
+        A$p4 <- common2 + geom_boxplot(aes(y = Cell, color = Factors))
+        B$p3 <- common1 + geom_boxplot(aes(y = Bulk, color = Factors)) +
+            ylab("Bulk Dataset")
+        B$p4 <- common2 + geom_boxplot(aes(y = Bulk, color = Factors)) +
+            ylab("Bulk Dataset")
+    }
+
+    title_a <- "Cell Types against Bulk"
+    title_b <- "Bulk Datasets against Cells"
+    if (do_factors) {
+        title_a <- paste0(title_a, " and Factors")
+        title_b <- paste0(title_b, " and Factors")
+    }
+
+    a_all <- plot_grid(ggdraw() + draw_label(title_a, fontface = "bold"),
+                       plot_grid(plotlist = A, ncol = 2),
+                       ncol = 1, rel_heights = c(0.05, 0.95))
+    b_all <- plot_grid(ggdraw() + draw_label(title_b, fontface = "bold"),
+                       plot_grid(plotlist = B, ncol = 2),
+                       ncol = 1, rel_heights = c(0.05, 0.95))
+    return(list(cell = a_all, bulk = b_all))
+}
+
+filter_output <- function(grudat_spread_melt, out_filt) {
+    print_red <- function(comment, red_list) {
+        cat(paste(comment, paste(red_list, collapse = ", "), "\n"))
+    }
+    grudat_filt <- grudat_spread_melt
+    print_red("Total Cell types:", unique(grudat_filt$Cell))
+    if (!is.null(out_filt$cells)) {
+        grudat_filt <- grudat_filt[grudat_filt$Cell %in% out_filt$cells, ]
+        print_red(" - selecting:", out_filt$cells)
+    }
+    print_red("Total Factors:", unique(grudat_spread_melt$Factors))
+    if (!is.null(out_filt$facts)) {
+        grudat_filt <- grudat_filt[grudat_filt$Factors %in% out_filt$facts, ]
+        print_red(" - selecting:", out_filt$facts)
+    }
+    return(grudat_filt)
+}
+
+
+results <- music_on_all(files)
+
+if (heat_grouped_p) {
+    plot_grouped_heatmaps(results)
+} else {
+    plot_all_individual_heatmaps(results)
+}
+
+save.image("/tmp/sesh.rds")
+
+summat <- summarized_matrix(results)
+grudat <- group_by_dataset(summat)
+grudat_spread_melt <- merge_factors_spread(grudat$spread,
+                                           flatten_factor_list(results))
+
+
+
+## The output filters ONLY apply to boxplots, since these take
+do_factors <- (length(unique(grudat_spread_melt[["Factors"]])) > 1)
+
+grudat_spread_melt_filt <- filter_output(grudat_spread_melt, out_filt)
+
+heat_maps <- summarize_heatmaps(grudat_spread_melt_filt, do_factors)
+box_plots <- summarize_boxplots(grudat_spread_melt_filt, do_factors)
+
+pdf(out_heatsumm_pdf, width = 14, height = 14)
+print(heat_maps)
+print(box_plots)
+dev.off()
+
+## Generate output tables
+stats_prop <- lapply(grudat$spread$prop, function(x) {
+    t(apply(x, 1, summary))})
+stats_scale <- lapply(grudat$spread$scale, function(x) {
+    t(apply(x, 1, summary))})
+
+writable2 <- function(obj, prefix, title) {
+    write.table(obj,
+                file = paste0("report_data/", prefix, "_",
+                              title, ".tabular"),
+                quote = F, sep = "\t", col.names = NA)
+}
+## Make the value table printable
+grudat_spread_melt$value.scale <- as.integer(grudat_spread_melt$value.scale) # nolint
+colnames(grudat_spread_melt) <- c("Sample", "Cell", "Bulk", "Factors",
+                                  "CT Prop in Sample", "Number of Reads")
+
+writable2(grudat_spread_melt, "values", "Data Table")
+writable2(summat$prop, "values", "Matrix of Cell Type Sample Proportions")
+writable2({
+    aa <- as.matrix(summat$scaled); mode(aa) <- "integer"; aa
+}, "values", "Matrix of Cell Type Read Counts")
+
+for (bname in names(stats_prop)) {
+    writable2(stats_prop[[bname]], "stats", paste0(bname, ": Sample Props"))
+    writable2(stats_scale[[bname]], "stats", paste0(bname, ": Read Props"))
+}

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/dendrogram.R	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,132 @@
+##
+suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
+suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
+suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
+suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
+## We use this script to generate a clustering dendrogram of cell
+## types, using the prior labelling from scRNA.
+
+read_list <- function(lfile) {
+    if (lfile == "None") {
+        return(NULL)
+    }
+    return(read.table(file = lfile, header = FALSE, check.names = FALSE,
+                      stringsAsFactors = FALSE)$V1)
+}
+
+args <- commandArgs(trailingOnly = TRUE)
+source(args[1])
+
+
+## Perform the estimation
+## Produce the first step information
+sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
+                         samples = samples_label,
+                         select.ct = celltypes)
+
+## Plot the dendrogram of design matrix and cross-subject mean of
+## realtive abundance
+## Hierarchical clustering using Complete Linkage
+d1 <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
+hc1 <- hclust(d1, method = "complete")
+## Hierarchical clustering using Complete Linkage
+d2 <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
+hc2 <- hclust(d2, method = "complete")
+
+
+if (length(data.to.use) > 0) {
+    ## We then perform bulk tissue cell type estimation with pre-grouping
+    ## of cell types: C, list_of_cell_types, marker genes name, marker
+    ## genes list.
+    ## data.to.use = list(
+    ##     "C1" = list(cell.types = c("Neutro"),
+    ##                 marker.names=NULL,
+    ##                 marker.list=NULL),
+    ##     "C2" = list(cell.types = c("Podo"),
+    ##                 marker.names=NULL,
+    ##                 marker.list=NULL),
+    ##     "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
+    ##                 marker.names = "Epithelial",
+    ##                 marker.list = read_list("../test-data/epith.markers")),
+    ##     "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
+    ##                 marker.names = "Immune",
+    ##                 marker.list = read_list("../test-data/immune.markers"))
+    ## )
+    grouped_celltypes <- lapply(data.to.use, function(x) {
+        x$cell.types
+    })
+    marker_groups <- lapply(data.to.use, function(x) {
+        x$marker.list
+    })
+    names(marker_groups) <- names(data.to.use)
+
+
+    cl_type <- as.character(scrna_eset[[celltypes_label]])
+
+    for (cl in seq_len(length(grouped_celltypes))) {
+        cl_type[cl_type %in%
+                grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl]
+    }
+    pData(scrna_eset)[[clustertype_label]] <- factor(
+        cl_type, levels = c(names(grouped_celltypes),
+                            "CD-Trans", "Novel1", "Novel2"))
+
+    est_bulk <- music_prop.cluster(
+        bulk.eset = bulk_eset, sc.eset = scrna_eset,
+        group.markers = marker_groups, clusters = celltypes_label,
+        groups = clustertype_label, samples = samples_label,
+        clusters.type = grouped_celltypes
+    )
+
+    estimated_music_props <- est_bulk$Est.prop.weighted.cluster
+    ## NNLS is not calculated here
+
+    ## Show different in estimation methods
+    ## Jitter plot of estimated cell type proportions
+    methods_list <- c("MuSiC")
+
+    jitter_fig <- Jitter_Est(
+        list(data.matrix(estimated_music_props)),
+        method.name = methods_list, title = "Jitter plot of Est Proportions",
+        size = 2, alpha = 0.7) +
+        theme_minimal() +
+        labs(x = element_blank(), y = element_blank()) +
+        theme(axis.text = element_text(size = 6),
+              axis.text.x = element_blank(),
+              legend.position = "none")
+
+    plot_box <- Boxplot_Est(list(
+        data.matrix(estimated_music_props)),
+        method.name = methods_list) +
+        theme_minimal() +
+        labs(x = element_blank(), y = element_blank()) +
+        theme(axis.text = element_text(size = 6),
+              axis.text.x = element_blank(),
+              legend.position = "none")
+
+    plot_hmap <- Prop_heat_Est(list(
+        data.matrix(estimated_music_props)),
+        method.name = methods_list) +
+        labs(x = element_blank(), y = element_blank()) +
+        theme(axis.text.y = element_text(size = 6),
+              axis.text.x = element_text(angle = -90, size = 5),
+              plot.title = element_text(size = 9),
+              legend.key.width = unit(0.15, "cm"),
+              legend.text = element_text(size = 5),
+              legend.title = element_text(size = 5))
+
+}
+    
+pdf(file = outfile_pdf, width = 8, height = 8)
+par(mfrow = c(1, 2))
+plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
+plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")
+if (length(data.to.use) > 0) {
+    plot_grid(jitter_fig, plot_box, plot_hmap, ncol = 2, nrow = 2)
+}
+message(dev.off())
+
+if (length(data.to.use) > 0) {
+    write.table(estimated_music_props,
+                file = outfile_tab, quote = F, col.names = NA, sep = "\t")
+}

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/estimateprops.R	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,250 @@
+suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
+suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
+suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
+suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
+## We use this script to estimate the effectiveness of proportion methods
+
+## Load Conf
+args <- commandArgs(trailingOnly = TRUE)
+source(args[1])
+
+## Estimate cell type proportions
+est_prop <- music_prop(
+    bulk.eset = bulk_eset, sc.eset = scrna_eset,
+    clusters = celltypes_label,
+    samples = samples_label, select.ct = celltypes, verbose = T)
+
+
+estimated_music_props <- est_prop$Est.prop.weighted
+estimated_nnls_props <- est_prop$Est.prop.allgene
+##
+estimated_music_props_flat <- melt(estimated_music_props)
+estimated_nnls_props_flat <- melt(estimated_nnls_props)
+
+scale_yaxes <- function(gplot, value) {
+    if (is.na(value)) {
+        gplot
+    } else {
+        gplot + scale_y_continuous(lim = c(0, value))
+    }
+}
+
+sieve_data <- function(func, music_data, nnls_data) {
+    if (func == "list") {
+        res <- list(if ("MuSiC" %in% methods) music_data else NULL,
+                    if ("NNLS" %in% methods) nnls_data else NULL)
+        res[lengths(res) > 0] ## filter out NULL elements
+    } else if (func == "rbind") {
+        rbind(if ("MuSiC" %in% methods) music_data else NULL,
+              if ("NNLS" %in% methods) nnls_data else NULL)
+    } else if (func == "c") {
+        c(if ("MuSiC" %in% methods) music_data else NULL,
+          if ("NNLS" %in% methods) nnls_data else NULL)
+    }
+}
+
+
+## Show different in estimation methods
+## Jitter plot of estimated cell type proportions
+jitter_fig <- scale_yaxes(Jitter_Est(
+    sieve_data("list",
+               data.matrix(estimated_music_props),
+               data.matrix(estimated_nnls_props)),
+    method.name = methods, title = "Jitter plot of Est Proportions",
+    size = 2, alpha = 0.7) + theme_minimal(), maxyscale)
+
+## Make a Plot
+## A more sophisticated jitter plot is provided as below. We separated
+## the T2D subjects and normal subjects by their disease factor levels.
+m_prop <- sieve_data("rbind",
+                     estimated_music_props_flat,
+                     estimated_nnls_props_flat)
+colnames(m_prop) <- c("Sub", "CellType", "Prop")
+
+if (is.null(celltypes)) {
+    celltypes <- levels(m_prop$CellType)
+    message("No celltypes declared, using:")
+    message(celltypes)
+}
+
+if (is.null(phenotype_factors)) {
+    phenotype_factors <- colnames(pData(bulk_eset))
+}
+## filter out unwanted factors like "sampleID" and "subjectName"
+phenotype_factors <- phenotype_factors[
+    !(phenotype_factors %in% phenotype_factors_always_exclude)]
+message("Phenotype Factors to use:")
+message(paste0(phenotype_factors, collapse = ", "))
+
+m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
+m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
+                        levels = methods)
+
+if (use_disease_factor) {
+
+    if (phenotype_target_threshold == -99) {
+        phenotype_target_threshold <- -Inf
+        message("phenotype target threshold set to -Inf")
+    }
+    ## the "2" here is to do with the sample groups, not number of methods
+    m_prop$Disease_factor <- rep(bulk_eset[[phenotype_target]], 2 * length(celltypes)) # nolint
+    m_prop <- m_prop[!is.na(m_prop$Disease_factor), ]
+    ## Generate a TRUE/FALSE table of Normal == 1 and Disease == 2
+    sample_groups <- c("Normal", sample_disease_group)
+    m_prop$Disease <- factor(sample_groups[(m_prop$Disease_factor > phenotype_target_threshold) + 1], # nolint
+                             levels = sample_groups)
+
+    ## Binary to scale: e.g. TRUE / 5 = 0.2
+    m_prop$D <- (m_prop$Disease ==   # nolint
+                 sample_disease_group) / sample_disease_group_scale
+    ## NA's are not included in the comparison below
+    m_prop <- rbind(subset(m_prop, Disease != sample_disease_group),
+                    subset(m_prop, Disease == sample_disease_group))
+
+    jitter_new <- scale_yaxes(
+        ggplot(m_prop, aes(Method, Prop)) +
+        geom_point(aes(fill = Method, color = Disease,
+                       stroke = D, shape = Disease),
+                   size = 2, alpha = 0.7,
+                   position = position_jitter(width = 0.25, height = 0)) +
+        facet_wrap(~ CellType, scales = "free") +
+        scale_colour_manual(values = c("white", "gray20")) +
+        scale_shape_manual(values = c(21, 24)) + theme_minimal(), maxyscale)
+
+}
+
+if (use_disease_factor) {
+
+    ## Plot to compare method effectiveness
+    ## Create dataframe for beta cell proportions and Disease_factor levels
+    ## - Ugly code. Essentially, doubles the cell type proportions for each
+    ##   set of MuSiC and NNLS methods
+    m_prop_ana <- data.frame(
+        pData(bulk_eset)[rep(1:nrow(estimated_music_props), length(methods)), #nolint
+                         phenotype_factors],
+        ## get proportions of target cell type
+        ct.prop = sieve_data("c",
+                             estimated_music_props[, phenotype_scrna_target],
+                             estimated_nnls_props[, phenotype_scrna_target]),
+        ##
+        Method = factor(rep(methods,
+                            each = nrow(estimated_music_props)),
+                        levels = methods))
+    ## - fix headers
+    colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
+    ## - drop NA for target phenotype (e.g. hba1c)
+    m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
+    m_prop_ana$Disease <- factor(   # nolint
+        ## - Here we set Normal/Disease assignments across the methods
+        sample_groups[(
+            m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1
+            ],
+        sample_groups)
+    ## - Then we scale this binary assignment to a plotable factor
+    m_prop_ana$D <- (m_prop_ana$Disease ==        # nolint
+                     sample_disease_group) / sample_disease_group_scale
+
+    jitt_compare <- scale_yaxes(
+        ggplot(m_prop_ana, aes_string(phenotype_target, "ct.prop")) +
+        geom_smooth(method = "lm",  se = FALSE, col = "black", lwd = 0.25) +
+        geom_point(aes(fill = Method, color = Disease,
+                       stroke = D, shape = Disease),
+                   size = 2, alpha = 0.7) +  facet_wrap(~ Method) +
+        ggtitle(paste0(toupper(phenotype_target), " vs. ",
+                       toupper(phenotype_scrna_target),
+                       " Cell Type Proportion")) +
+        theme_minimal() +
+        ylab(paste0("Proportion of ",
+                    phenotype_scrna_target, " cells")) +
+        xlab(paste0("Level of bulk factor (", phenotype_target, ")")) +
+        scale_colour_manual(values = c("white", "gray20")) +
+        scale_shape_manual(values = c(21, 24)), maxyscale)
+}
+
+## BoxPlot
+plot_box <- scale_yaxes(Boxplot_Est(
+    sieve_data("list",
+               data.matrix(estimated_music_props),
+               data.matrix(estimated_nnls_props)),
+    method.name = methods) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 8)) +
+    ggtitle(element_blank()) + theme_minimal(), maxyscale)
+
+## Heatmap
+plot_hmap <- Prop_heat_Est(
+    sieve_data(
+        "list",
+        data.matrix(estimated_music_props),
+        data.matrix(estimated_nnls_props)),
+    method.name = methods) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 6))
+
+pdf(file = outfile_pdf, width = 8, height = 8)
+if (length(celltypes) <= 8) {
+    plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
+} else {
+    print(jitter_fig)
+    plot_box
+}
+if (use_disease_factor) {
+    plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
+}
+plot_hmap
+message(dev.off())
+
+writable <- function(obj, prefix, title) {
+    write.table(obj,
+                file = paste0("report_data/", prefix, "_",
+                              title, ".tabular"),
+                quote = F, sep = "\t", col.names = NA)
+}
+
+## Output Proportions
+if ("NNLS" %in% methods) {
+    writable(est_prop$Est.prop.allgene, "prop",
+             "NNLS Estimated Proportions of Cell Types")
+}
+
+if ("MuSiC" %in% methods) {
+    writable(est_prop$Est.prop.weighted, "prop",
+             "Music Estimated Proportions of Cell Types")
+    writable(est_prop$Weight.gene, "weightgene",
+             "Music Estimated Proportions of Cell Types (by Gene)")
+    writable(est_prop$r.squared.full, "rsquared",
+             "Music R-sqr Estimated Proportions of Each Subject")
+    writable(est_prop$Var.prop, "varprop",
+             "Matrix of Variance of MuSiC Estimates")
+}
+
+
+if (use_disease_factor) {
+    ## Summary table of linear regressions of disease factors
+    for (meth in methods) {
+        ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
+        sub_data <- subset(m_prop_ana, Method == meth)
+
+        ## We can only do regression where there are more than 1 factors
+        ## so we must find and exclude the ones which are not
+        gt1_facts <- sapply(phenotype_factors, function(facname) {
+            return(length(unique(sort(sub_data[[facname]]))) == 1)
+        })
+        form_factors <- phenotype_factors
+        exclude_facts <- names(gt1_facts)[gt1_facts]
+        if (length(exclude_facts) > 0) {
+            message("Factors with only one level will be excluded:")
+            message(exclude_facts)
+            form_factors <- phenotype_factors[
+                !(phenotype_factors %in% exclude_facts)]
+        }
+        lm_beta_meth <- lm(as.formula(
+            paste("ct.prop", paste(form_factors, collapse = " + "),
+                  sep = " ~ ")), data = sub_data)
+        message(paste0("Summary: ", meth))
+        capture.output(summary(lm_beta_meth),
+                       file = paste0("report_data/summ_Log of ",
+                                     meth,
+                                     " fitting.txt"))
+    }
+}

Binary file test-data/EMTABesethealthy.subset.rds has changed

Binary file test-data/GSE50244bulkeset.subset.rds has changed

Binary file test-data/Mousebulkeset.rds has changed

Binary file test-data/Mousesubeset.degenesonly2.half.rds has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/array.tsv	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,4 @@
+	Sample1	Sample2
+Gene1	1	3
+Gene2	2	4
+Gene3	3	5

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Binary file test-data/default_output_no_disease_nnls.pdf has changed

Binary file test-data/dendro.pdf has changed

Binary file test-data/dendro_1.pdf has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/epith.markers	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,2614 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/immune.markers	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,8126 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mouse_scrna_exprs.tabular	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,101 @@
+	TGGTTCCGTCGGCTCA-2	CGAGCCAAGCGTCAAG-4	GAATGAAGTTTGGGCC-5	CTCGTACGTTGCCTCT-7	TTCTCAATCCACGCAG-5	CCTTCCCCATACCATG-4	ACTTTCACAGCTGGCT-7	TGGGAAGCAAAGTGCG-7	TCTATTGAGTAGGCCA-7	TCGGTAACATCACGTA-2	GGGTTGCCAGCTGTAT-2	TGCGGGTGTCATATCG-6	ACTTGTTTCATATCGG-5	CCAATCCCACGGCGTT-2	CTAAGACCACCAGGCT-7	TTAACTCAGTAGGCCA-6	GTACTCCGTAACGCGA-1	GCCTCTAGTTGTACAC-2	TTCGAAGTCCTGCAGG-3	TTCTACAAGTTGTAGA-7	CCGTTCAGTTGAACTC-7	GTGTTAGTCAGCTCGG-1	GGATTACGTGTGCGTC-6	TACGGTATCCGTTGTC-6	TTAGTTCGTATTAGCC-5	CCCAATCGTAGCGATG-3	ACACCAATCTGCGTAA-7	AATCCAGTCCAAACTG-7	CAGAATCAGCAATATG-1	GCACATAAGCCGGTAA-5	CCTTCCCAGGAGTTTA-5	CGGAGCTAGGACTGGT-5	TACGGATGTAAATGTG-4	GGCAATTCATTCACTT-2	CTCGGGAGTCTGCGGT-4	CATTCGCGTCCTCTTG-2	CGCGTTTAGATCGATA-1	GGGTTGCCACCAACCG-4	TGTGTTTCATCGATGT-2	AGAGTGGAGCTGTTCA-7	CTCACACGTCTCACCT-3	AGTTGGTTCCACGAAT-7	ATCTGCCAGACCACGA-6	TGTATTCCATTGAGCT-7	TGAAAGAGTAGCCTAT-7	AAATGCCAGAACTGTA-7	TTTGCGCTCTACCAGA-4	ACATACGGTTTCCACC-6	GCCTCTAGTTCCACAA-7	GGGAGATGTACTCTCC-6	GAACGGATCTTGTACT-7	TACCTTATCCTAGAAC-1	GCGCGATAGATGCCAG-2	GACAGAGCAAGTTGTC-7	TGACTAGGTATGAATG-3	CACACTCAGTCACGCC-6	ATTGGTGGTTAGGGTG-5	AGCAGCCCAGCGTAAG-2	CATTCGCAGCCTTGAT-6	GCGAGAACATAGACTC-2	AGTCTTTGTAATAGCA-7	TCGCGAGCAGACACTT-7	CGGAGTCCAGCAGTTT-2	GGTGTTACACACATGT-7	TTCTCAAGTAAGTGTA-2	TGCTGCTAGTCAATAG-2	GATGAGGTCTACCAGA-2	ACATACGGTTGTACAC-5	ACGAGGACAGCTATTG-7	CGATGTATCGGCGGTT-2	CTGCGGATCACAACGT-2	CGAACATAGTTGAGTA-5	TAGTTGGTCGCGATCG-6	GCAGCCACAATGTAAG-4	CTCACACCAATAACGA-7	CCTTTCTCATGAAGTA-2	AGTGTCAAGAGCAATT-7	AGCATACGTAAAGGAG-1	ACACCAATCTCGCTTG-5	GGGATGAGTATCAGTC-6	TGACAACAGAAGCCCA-2	CGAATGTTCACAATGC-1	GCACTCTTCCGCATAA-1	CACCAGGTCCCAAGAT-2	GTTACAGCACCGCTAG-6	TAGCCGGCAGTACACT-2	ACGATGTGTTAAAGAC-2	CCTTCCCAGTCTCGGC-7	TAGTGGTTCTCTGTCG-7	TAGCCGGAGGCTAGCA-5	TTGTAGGTCAGCACAT-1	GAATAAGCAGCTTCGG-7	TCGCGAGAGTCCGGTC-3	TCAACGAAGAGTAAGG-2	CAGGTGCCACGAAATA-5	TGTGTTTCACTATCTT-2	TGGCCAGAGTGAAGAG-6	ACCAGTAAGTAGCCGA-2	GCGGGTTAGAAGGTTT-1	CAGTCCTGTCATTAGC-2
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/mouse_scrna_pheno.tabular	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,101 @@
+	sampleID	SubjectName	cellTypeID	cellType
+TGGTTCCGTCGGCTCA-2	2	Mouse2	3	PT
+CGAGCCAAGCGTCAAG-4	4	Mouse4	5	DCT
+GAATGAAGTTTGGGCC-5	5	Mouse5	3	PT
+CTCGTACGTTGCCTCT-7	7	Mouse7	3	PT
+TTCTCAATCCACGCAG-5	5	Mouse5	4	LOH
+CCTTCCCCATACCATG-4	4	Mouse4	14	T lymph
+ACTTTCACAGCTGGCT-7	7	Mouse7	3	PT
+TGGGAAGCAAAGTGCG-7	7	Mouse7	3	PT
+TCTATTGAGTAGGCCA-7	7	Mouse7	3	PT
+TCGGTAACATCACGTA-2	2	Mouse2	3	PT
+GGGTTGCCAGCTGTAT-2	2	Mouse2	2	Podo
+TGCGGGTGTCATATCG-6	6	Mouse6	3	PT
+ACTTGTTTCATATCGG-5	5	Mouse5	14	T lymph
+CCAATCCCACGGCGTT-2	2	Mouse2	5	DCT
+CTAAGACCACCAGGCT-7	7	Mouse7	3	PT
+TTAACTCAGTAGGCCA-6	6	Mouse6	3	PT
+GTACTCCGTAACGCGA-1	1	Mouse1	3	PT
+GCCTCTAGTTGTACAC-2	2	Mouse2	3	PT
+TTCGAAGTCCTGCAGG-3	3	Mouse3	3	PT
+TTCTACAAGTTGTAGA-7	7	Mouse7	3	PT
+CCGTTCAGTTGAACTC-7	7	Mouse7	7	CD-IC
+GTGTTAGTCAGCTCGG-1	1	Mouse1	4	LOH
+GGATTACGTGTGCGTC-6	6	Mouse6	3	PT
+TACGGTATCCGTTGTC-6	6	Mouse6	3	PT
+TTAGTTCGTATTAGCC-5	5	Mouse5	3	PT
+CCCAATCGTAGCGATG-3	3	Mouse3	3	PT
+ACACCAATCTGCGTAA-7	7	Mouse7	5	DCT
+AATCCAGTCCAAACTG-7	7	Mouse7	5	DCT
+CAGAATCAGCAATATG-1	1	Mouse1	3	PT
+GCACATAAGCCGGTAA-5	5	Mouse5	5	DCT
+CCTTCCCAGGAGTTTA-5	5	Mouse5	3	PT
+CGGAGCTAGGACTGGT-5	5	Mouse5	4	LOH
+TACGGATGTAAATGTG-4	4	Mouse4	3	PT
+GGCAATTCATTCACTT-2	2	Mouse2	3	PT
+CTCGGGAGTCTGCGGT-4	4	Mouse4	6	CD-PC
+CATTCGCGTCCTCTTG-2	2	Mouse2	8	CD-Trans
+CGCGTTTAGATCGATA-1	1	Mouse1	6	CD-PC
+GGGTTGCCACCAACCG-4	4	Mouse4	7	CD-IC
+TGTGTTTCATCGATGT-2	2	Mouse2	3	PT
+AGAGTGGAGCTGTTCA-7	7	Mouse7	3	PT
+CTCACACGTCTCACCT-3	3	Mouse3	3	PT
+AGTTGGTTCCACGAAT-7	7	Mouse7	3	PT
+ATCTGCCAGACCACGA-6	6	Mouse6	3	PT
+TGTATTCCATTGAGCT-7	7	Mouse7	3	PT
+TGAAAGAGTAGCCTAT-7	7	Mouse7	3	PT
+AAATGCCAGAACTGTA-7	7	Mouse7	5	DCT
+TTTGCGCTCTACCAGA-4	4	Mouse4	3	PT
+ACATACGGTTTCCACC-6	6	Mouse6	3	PT
+GCCTCTAGTTCCACAA-7	7	Mouse7	3	PT
+GGGAGATGTACTCTCC-6	6	Mouse6	1	Endo
+GAACGGATCTTGTACT-7	7	Mouse7	3	PT
+TACCTTATCCTAGAAC-1	1	Mouse1	3	PT
+GCGCGATAGATGCCAG-2	2	Mouse2	3	PT
+GACAGAGCAAGTTGTC-7	7	Mouse7	3	PT
+TGACTAGGTATGAATG-3	3	Mouse3	4	LOH
+CACACTCAGTCACGCC-6	6	Mouse6	3	PT
+ATTGGTGGTTAGGGTG-5	5	Mouse5	3	PT
+AGCAGCCCAGCGTAAG-2	2	Mouse2	1	Endo
+CATTCGCAGCCTTGAT-6	6	Mouse6	3	PT
+GCGAGAACATAGACTC-2	2	Mouse2	14	T lymph
+AGTCTTTGTAATAGCA-7	7	Mouse7	3	PT
+TCGCGAGCAGACACTT-7	7	Mouse7	3	PT
+CGGAGTCCAGCAGTTT-2	2	Mouse2	3	PT
+GGTGTTACACACATGT-7	7	Mouse7	3	PT
+TTCTCAAGTAAGTGTA-2	2	Mouse2	1	Endo
+TGCTGCTAGTCAATAG-2	2	Mouse2	3	PT
+GATGAGGTCTACCAGA-2	2	Mouse2	3	PT
+ACATACGGTTGTACAC-5	5	Mouse5	3	PT
+ACGAGGACAGCTATTG-7	7	Mouse7	4	LOH
+CGATGTATCGGCGGTT-2	2	Mouse2	3	PT
+CTGCGGATCACAACGT-2	2	Mouse2	13	B lymph
+CGAACATAGTTGAGTA-5	5	Mouse5	3	PT
+TAGTTGGTCGCGATCG-6	6	Mouse6	5	DCT
+GCAGCCACAATGTAAG-4	4	Mouse4	1	Endo
+CTCACACCAATAACGA-7	7	Mouse7	3	PT
+CCTTTCTCATGAAGTA-2	2	Mouse2	7	CD-IC
+AGTGTCAAGAGCAATT-7	7	Mouse7	3	PT
+AGCATACGTAAAGGAG-1	1	Mouse1	6	CD-PC
+ACACCAATCTCGCTTG-5	5	Mouse5	3	PT
+GGGATGAGTATCAGTC-6	6	Mouse6	3	PT
+TGACAACAGAAGCCCA-2	2	Mouse2	3	PT
+CGAATGTTCACAATGC-1	1	Mouse1	1	Endo
+GCACTCTTCCGCATAA-1	1	Mouse1	3	PT
+CACCAGGTCCCAAGAT-2	2	Mouse2	3	PT
+GTTACAGCACCGCTAG-6	6	Mouse6	3	PT
+TAGCCGGCAGTACACT-2	2	Mouse2	11	Macro
+ACGATGTGTTAAAGAC-2	2	Mouse2	9	Novel1
+CCTTCCCAGTCTCGGC-7	7	Mouse7	3	PT
+TAGTGGTTCTCTGTCG-7	7	Mouse7	7	CD-IC
+TAGCCGGAGGCTAGCA-5	5	Mouse5	7	CD-IC
+TTGTAGGTCAGCACAT-1	1	Mouse1	4	LOH
+GAATAAGCAGCTTCGG-7	7	Mouse7	3	PT
+TCGCGAGAGTCCGGTC-3	3	Mouse3	14	T lymph
+TCAACGAAGAGTAAGG-2	2	Mouse2	5	DCT
+CAGGTGCCACGAAATA-5	5	Mouse5	3	PT
+TGTGTTTCACTATCTT-2	2	Mouse2	3	PT
+TGGCCAGAGTGAAGAG-6	6	Mouse6	3	PT
+ACCAGTAAGTAGCCGA-2	2	Mouse2	6	CD-PC
+GCGGGTTAGAAGGTTT-1	1	Mouse1	3	PT
+CAGTCCTGTCATTAGC-2	2	Mouse2	7	CD-IC

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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pheno.tsv	Thu Feb 10 12:51:28 2022 +0000
@@ -0,0 +1,3 @@
+	gender	type	score
+Sample1	1	3	5
+Sample2	2	4	9