# HG changeset patch
# User artbio
# Date 1561397865 14400
# Node ID e63bd8f13679870b989bbc359f27413f136e30a6

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_filter_cells commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe

diff -r 000000000000 -r e63bd8f13679 filter_cells.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/filter_cells.R	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,204 @@
+# First step of the signature-based workflow
+# Remove low quality cells below a user-fixed cutoff of 
+# percentiles or raw values of number of genes detected or
+# total aligned reads
+
+# Example of command (that generates output files) :
+# Rscript filter_cells.R -f <input> --sep "/t" --absolute_genes 1700 --absolute_counts 90000 --pdfplot <plotfile.pdf>  --output <filtered_cells.tsv> --output_metada <filtered_metadata.tsv>
+
+# load packages that are provided in the conda env
+options( show.error.messages=F,
+       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+warnings()
+library(optparse)
+library(ggplot2)
+
+# Arguments
+option_list = list(
+  make_option(c("-f", "--file"), default=NA, type='character',
+              help="Input file that contains values to filter"),
+  make_option("--sep", default="\t", type='character',
+              help="File column separator [default : '%default' ]"),
+  make_option("--percentile_genes", default=0, type='integer',
+              help="nth Percentile of the number of genes detected by a cell distribution [default : '%default' ]"),
+  make_option("--percentile_counts", default=0, type='integer',
+              help="nth Percentile of the total counts per cell distribution [default : '%default' ]"),
+  make_option("--absolute_genes", default=0, type='integer',
+              help="Remove cells that didn't express at least this number of genes [default : '%default' ]"),
+  make_option("--absolute_counts", default=0, type='integer',
+              help="Number of transcript threshold for cell filtering [default : '%default' ]"),
+  make_option("--manage_cutoffs", default="intersect", type='character',
+              help="combine or intersect cutoffs for filtering"),
+  make_option("--pdfplot", type = 'character',
+              help="Path to pdf file of the plots"),
+  make_option("--output", type = 'character',
+              help="Path to tsv file of filtered cell data"),
+  make_option("--output_metada", type = 'character',
+              help="Path to tsv file of filtered cell metadata")
+)
+opt = parse_args(OptionParser(option_list=option_list), args = commandArgs(trailingOnly = TRUE))
+if (opt$sep == "tab") {opt$sep = "\t"}
+if (opt$sep == "comma") {opt$sep = ","}
+if (opt$sep == "space") {opt$sep = " "}
+
+
+# check consistency of filtering options
+if ((opt$percentile_counts > 0) & (opt$absolute_counts > 0)) {
+  opt$percentile_counts = 0 } # since input parameters are not consistent (one or either method, not both), no filtering
+# if ((opt$percentile_counts == 0) & (opt$absolute_counts == 0)) {
+#   opt$percentile_counts = 0 } # since input parameters are not consistent (one or either method, not both), no filtering
+if ((opt$percentile_genes > 0) & (opt$absolute_genes > 0)) {
+  opt$percentile_genes = 0 } # since input parameters are not consistent (one or either method, not both), no filtering
+# if ((opt$percentile_genes == 0) & (opt$absolute_genes == 0)) {
+#   opt$percentile_genes = 100 } # since input parameters are not consistent (one or either method, not both), no filtering
+
+# Import datasets
+data.counts <- read.table(
+  opt$file,
+  header = TRUE,
+  stringsAsFactors = F,
+  sep = opt$sep,
+  check.names = FALSE,
+  row.names = 1
+)
+
+QC_metrics <-
+  data.frame(cell_id = colnames(data.counts),
+             nGenes = colSums(data.counts != 0),    # nGenes : Number of detected genes for each cell
+             total_counts = colSums(data.counts),  # total_counts : Total counts per cell
+             stringsAsFactors = F)
+
+plot_hist <- function(mydata, variable, title, cutoff){
+  mybinwidth = round(max(mydata[, variable]) * 5 / 100)
+  mylabel = paste0("cutoff= ", cutoff)
+  hist_plot <- qplot(
+    mydata[, variable],
+    main = title,
+    xlab = variable,
+    geom="histogram",
+    binwidth = mybinwidth,
+    col = I("white")) +
+    geom_vline(xintercept = cutoff) +
+    annotate(geom="text",
+             x=cutoff + mybinwidth, y=1,
+             label=mylabel, color="white")
+    plot(hist_plot)
+}
+
+# returns the highest value such as the sum of the ordered values including this highest value
+# is lower (below) than the percentile threshold (n)
+percentile_cutoff <- function(n, qcmetrics, variable, plot_title, ...){
+  p = n / 100
+  percentile_threshold = quantile(qcmetrics[, variable], p)[[1]]
+  plot_hist(qcmetrics,
+            variable,
+            plot_title,
+            percentile_threshold)
+  return(percentile_threshold)
+}
+
+pdf(file = opt$pdfplot)
+
+# Determine thresholds based on percentile
+
+if (opt$percentile_counts > 0) {
+  counts_threshold <- percentile_cutoff(
+    opt$percentile_counts,
+    QC_metrics,
+    "total_counts",
+    "Histogram of Aligned read counts per cell"
+  )} else {
+  counts_threshold <- opt$absolute_counts
+  plot_hist(QC_metrics,
+            variable = "total_counts",
+            title = "Histogram of Total counts per cell",
+            cutoff = counts_threshold)
+}
+
+if (opt$percentile_genes > 0) {
+ 
+  genes_threshold <- percentile_cutoff(
+    opt$percentile_genes,
+    QC_metrics,
+    "nGenes",
+    "Histogram of Number of detected genes per cell"
+  )} else {
+  genes_threshold <- opt$absolute_genes
+  plot_hist(QC_metrics,
+            variable = "nGenes",
+            title = "Histogram of Number of detected genes per cell",         
+            cutoff = genes_threshold)
+}
+
+# Filter out rows below thresholds (genes and read counts)
+if (opt$manage_cutoffs == 'union'){
+    QC_metrics$filtered <- (QC_metrics$nGenes < genes_threshold) | (QC_metrics$total_counts < counts_threshold)
+} else {
+    QC_metrics$filtered <- (QC_metrics$nGenes < genes_threshold) & (QC_metrics$total_counts < counts_threshold)
+}
+
+## Plot the results
+
+# Determine title from the parameter logics
+if (opt$percentile_counts > 0){
+    part_one = paste0("Cells with aligned reads counts below the ",
+                      opt$percentile_counts,
+                      "th percentile of aligned read counts")} else {
+    part_one = paste0("Cells with aligned read counts below ",
+                      opt$absolute_counts)
+}
+
+if(opt$percentile_genes > 0){
+    part_two = paste0("with number of detected genes below the ",
+                      opt$percentile_genes,
+                      "th percentile of detected gene counts")} else {
+    part_two = paste0("with number of detected genes below ",
+                      opt$absolute_genes)
+}
+if (opt$manage_cutoffs == "intersect") {
+    conjunction = " and\n" } else {
+    conjunction = " or\n"
+}
+
+# plot with ggplot2
+ggplot(QC_metrics, aes(nGenes, total_counts, colour = filtered)) +
+     geom_point() + scale_y_log10() +
+     scale_colour_discrete(name  = "",
+                           breaks= c(FALSE, TRUE),
+                           labels= c(paste0("Not filtered (", table(QC_metrics$filtered)[1], " cells)"), 
+                                     paste0("Filtered (", table(QC_metrics$filtered)[2], " cells)"))) +
+     xlab("Detected genes per cell") + ylab("Aligned reads per cell (log10 scale)") +
+     geom_vline(xintercept = genes_threshold) + geom_hline(yintercept = counts_threshold) +
+     ggtitle( paste0(part_one, conjunction, part_two, "\nwere filtered out")) +
+     theme(plot.title = element_text(size = 8, face = "bold"))
+
+dev.off()
+
+# Retrieve identifier of kept cells
+kept.cells <- QC_metrics$cell_id[!QC_metrics$filtered]
+
+data.counts <- data.frame(Genes=rownames(data.counts[,kept.cells]), data.counts[,kept.cells], check.names = FALSE)
+
+# Save filtered cells 
+write.table(
+  data.counts,
+  opt$output,
+  sep = "\t",
+  quote = F,
+  col.names = T,
+  row.names = F
+)
+
+# Add QC metrics of filtered cells to a metadata file
+metadata <- QC_metrics
+
+# Save the metadata (QC metrics) file
+write.table(
+  metadata,
+  opt$output_metada,
+  sep = "\t",
+  quote = F,
+  col.names = T,
+  row.names = F
+)
diff -r 000000000000 -r e63bd8f13679 filter_cells.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/filter_cells.xml	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,189 @@
+<tool id="filter_cells" name="Filter cells data" version="0.9.0">
+    <description>on total aligned reads and/or number of detected genes</description>
+    <requirements>
+        <requirement type="package" version="1.3.2=r3.3.2_0">r-optparse</requirement>
+        <requirement type="package" version="2.2.1=r3.3.2_0">r-ggplot2</requirement>
+    </requirements>
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Tool exception" />
+    </stdio>
+    <command detect_errors="exit_code"><![CDATA[ 
+        Rscript $__tool_directory__/filter_cells.R 
+            -f $input 
+            --sep 
+            #if $sep == 'tab':
+              'tab'
+            #elif $sep == 'comma':
+              'comma'
+            #end if
+            --percentile_genes '$percentile_genes'
+            --percentile_counts '$percentile_counts'
+            --absolute_genes '$absolute_genes'
+            --absolute_counts '$absolute_counts'
+            --manage_cutoffs
+            #if $manage_cutoffs == 'union':
+              'union'
+            #elif $manage_cutoffs == 'intersect':
+              'intersect'
+            #end if
+            --pdfplot $pdfplot
+            --output $output
+            --output_metada $output_metada
+]]></command>
+    <inputs>
+        <param name="input" type="data" format="txt,tabular" label="Raw counts of expression data"/>
+        <param name="sep" type="select" label="Indicate column separator">
+            <option value="tab" selected="true">Tabs</option>
+            <option value="comma">Comma</option>
+        </param>
+        <param name="percentile_genes" value="0" type="integer" label="Detected Genes Percentile Threshold [integer] %"
+               help="Cells with % of detected genes below this threshold will be filtered out. Leave at 0 for no filtering" />
+        <param name="percentile_counts" value="0" type="integer" label="Aligned read count Percentile Threshold [integer] %"
+               help="Cells with number of aligned reads below this % threshold will be filtered out. Leave at 0 for no filtering" />
+        <param name="absolute_genes" value="0" type="integer" label="Absolute number of Detected Genes Threshold [integer]"
+               help="Cells with number of detected genes below this threshold will be filtered out. Leave at 0 for no filtering" />
+        <param name="absolute_counts" value="0" type="integer" label="Absolute number of aligned read Threshold [integer]"
+               help="Cells with number of aligned reads below this absolute threshold will be filtered out. Leave at 0 for no filtering" />
+        <param name="manage_cutoffs" type="select" label=" filter out intersection or union of cutoffs"
+               help="If you use two cutoffs on number of detected genes and number of aligned reads, respectively, there are two options
+               for using these cutoffs in filtering: either excluding items that are below one or the other threshold (union) or
+               excluding items that are below both thresholds (intersection)" >
+            <option value="union" selected="true">Union of cutoffs</option>
+            <option value="intersect">Intersection of cutoffs</option>
+        </param>
+    </inputs>
+    <outputs>
+        <data name="pdfplot" format="pdf" label="Plots from ${on_string}" />
+        <data name="output" format="tabular" label="Cell data filtered with ${on_string}" />
+        <data name="output_metada" format="tabular" label="Cell metadata filtered with ${on_string}" />
+    </outputs>
+    <tests>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="percentile_genes" value="20"/>
+            <param name="percentile_counts" value="20"/>
+            <output name="pdfplot" file="percentile_gene-and-counts.pdf" ftype="pdf"/>
+            <output name="output" file="percentile_gene-and-counts.tab" ftype="tabular"/>
+            <output name="output_metada" file="percentile_gene-and-counts.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="percentile_genes" value="20"/>
+            <param name="percentile_counts" value="20"/>
+            <param name="manage_cutoffs" value="intersect"/>
+            <output name="pdfplot" file="intersect_percentile_gene-and-counts.pdf" ftype="pdf"/>
+            <output name="output" file="intersect_percentile_gene-and-counts.tab" ftype="tabular"/>
+            <output name="output_metada" file="intersect_percentile_gene-and-counts.meta" ftype="tabular"/>
+        </test>
+       <test>
+            <param name="input" value="input.tsv" ftype="txt"/>
+            <param name="sep" value='tab' />
+            <param name="percentile_genes" value="20"/>
+            <param name="percentile_counts" value="20"/>
+            <output name="pdfplot" file="percentile_gene-and-counts.pdf" ftype="pdf"/>
+            <output name="output" file="percentile_gene-and-counts.tab" ftype="tabular"/>
+            <output name="output_metada" file="percentile_gene-and-counts.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="percentile_genes" value="20"/>
+            <output name="pdfplot" file="percentile_gene-only.pdf" ftype="pdf"/>
+            <output name="output" file="percentile_gene-only.tab" ftype="tabular"/>
+            <output name="output_metada" file="percentile_gene-only.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="percentile_counts" value="20"/>
+            <output name="pdfplot" file="percentile_counts-only.pdf" ftype="pdf"/>
+            <output name="output" file="percentile_counts-only.tab" ftype="tabular"/>
+            <output name="output_metada" file="percentile_counts-only.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <output name="pdfplot" file="no-filtering.pdf" ftype="pdf"/>
+            <output name="output" file="no-filtering.tab" ftype="tabular"/>
+            <output name="output_metada" file="no-filtering.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="absolute_genes" value="5"/>
+            <param name="absolute_counts" value="1000"/>
+            <output name="pdfplot" file="absolute_gene-and-counts.pdf" ftype="pdf"/>
+            <output name="output" file="absolute_gene-and-counts.tab" ftype="tabular"/>
+            <output name="output_metada" file="absolute_gene-and-counts.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="absolute_genes" value="5"/>
+            <output name="pdfplot" file="absolute_gene-only.pdf" ftype="pdf"/>
+            <output name="output" file="absolute_gene-only.tab" ftype="tabular"/>
+            <output name="output_metada" file="absolute_gene-only.meta" ftype="tabular"/>
+        </test>
+        <test>
+            <param name="input" value="input.csv" ftype="txt"/>
+            <param name="sep" value='comma' />
+            <param name="absolute_counts" value="1000"/>
+            <output name="pdfplot" file="absolute_counts-only.pdf" ftype="pdf"/>
+            <output name="output" file="absolute_counts-only.tab" ftype="tabular"/>
+            <output name="output_metada" file="absolute_counts-only.meta" ftype="tabular"/>
+        </test>
+    </tests>
+    <help>
+
+**What it does**
+
+The tools takes a table of gene (rows) expression values (as number of reads aligned to genes)
+in single cell RNAseq sequencing libraries (columns) and filters out cells with low number
+of detected genes and/or cells with low number of aligned reads.
+
+Cutoffs can be applied to absolute numbers of aligned reads or of detected genes, or to
+percentile thresholds for these variables.
+
+For both absolute or percentile thresholds, only cells exclusively below
+these threshold are excluded (cell cutoffs do not include the threshold values).
+
+If you choose to combine cutoffs for both the number of detected genes
+and the total number of aligned reads, then you have 2 options: either exclude libraries that
+do not satisfy one OR the other threshold (Union) or exclude libraries that do not satisfy
+both thresholds (Intersection).
+
+Specifying a value both for an absolute and an percentile threshold of a variable
+(Number of detected genes or Number of aligned counts) is not consistent. In this
+situation, the tools *does not* filter cells with respect to the corresponding variable threshold.
+If a 0 is applied both for an absolute and an percentile threshold of a variable, then
+this variable is not used to filter the cells.
+
+The tools returns a gene expression table for cells that were retained, a metadata table
+that contains numbers of detected genes and aligned reads for retained cell library and
+a pdf file with three plots illustrating the performed filtering operation.
+
+**Input**
+
+A table of comma (csv) or tabulation (tsv) separated expression values, in number (integers)
+of reads aligned to genes.
+Gene names should be in the first column and cell names should be in the first row.
+Note that in a number of a csv files, header of the gene column is omitted, resulting in
+a first row with one item less than in other rows. This is handled by the tool that
+recognises this situation.
+
+    </help>
+    <citations>
+        <citation type="bibtex">
+        @Manual{,
+             title = {R: A Language and Environment for Statistical Computing},
+             author = {{R Core Team}},
+             organization = {R Foundation for Statistical Computing},
+             address = {Vienna, Austria},
+             year = {2014},
+             url = {http://www.R-project.org/},
+        }
+        </citation>
+    </citations>
+</tool>
diff -r 000000000000 -r e63bd8f13679 test-data/absolute_counts-only.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_counts-only.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
+1001000235.G10	5	437	TRUE
+1001000174.B1	12	7118	FALSE
+1001000177.C5	11	1686	FALSE
+1001000182.H4	14	8634	FALSE
+1001000175.A2	8	2986	FALSE
+1001000230.B11	6	1738	FALSE
+1001000237.E9	15	3784	FALSE
+1001000173.E10	16	5648	FALSE
+1001000241.E7	13	2488	FALSE
+1001000249.A3	15	3036	FALSE
+1001000179.A7	7	681	TRUE
+1001000247.F11	15	7576	FALSE
+1001000240.D9	6	1987	FALSE
+1001000178.G5	5	3421	FALSE
+1001000249.E3	10	2264	FALSE
+1001000174.H6	17	5763	FALSE
+1001000181.F8	6	2680	FALSE
+1001000177.A10	19	9240	FALSE
+1001000235.D2	11	5361	FALSE
+1001000240.G10	4	987	TRUE
+1001000248.E7	7	1103	FALSE
+1001000012.B6	10	5303	FALSE
+1001000179.H4	5	1038	FALSE
+1001000230.C1	9	3134	FALSE
+1001000175.H9	9	198	TRUE
+1001000241.G11	8	1143	FALSE
+1001000174.G2	5	1131	FALSE
+1001000252.C6	13	1904	FALSE
+1001000186.D11	6	1803	FALSE
+1001000239.G7	3	243	TRUE
+1001000258.G11	4	949	TRUE
+1001000264.A1	4	692	TRUE
+1001000174.A10	19	9134	FALSE
+1001000255.E9	13	7859	FALSE
+1001000242.B5	10	8515	FALSE
+1001000179.F3	10	2839	FALSE
+1001000185.F9	17	3974	FALSE
+1001000267.F8	9	3324	FALSE
+1001000183.G10	17	4570	FALSE
+1001000247.E7	6	4377	FALSE
+1001000031.A2	4	5245	FALSE
+1001000271.B1	11	2822	FALSE
+1001000187.G6	8	1636	FALSE
+1001000236.C6	13	1496	FALSE
+1001000238.C12	5	671	TRUE
+1001000187.D6	13	3802	FALSE
+1001000235.E10	19	4385	FALSE
+1001000036.C1	3	1662	FALSE
+1001000253.H2	3	190	TRUE
+1001000231.C2	12	1701	FALSE
+1001000178.C10	5	1776	FALSE
+1001000267.C1	14	5677	FALSE
+1001000180.E4	7	3661	FALSE
+1001000173.E5	18	7659	FALSE
+1001000179.F5	6	1479	FALSE
+1001000245.G11	10	3552	FALSE
+1001000185.D5	22	11179	FALSE
+1001000012.A7	7	3324	FALSE
+1001000010.B4	6	2054	FALSE
+1001000265.D11	12	1053	FALSE
+1001000032.F1	12	4096	FALSE
+1001000036.H9	8	1475	FALSE
+1001000245.B3	4	97	TRUE
+1001000185.A8	1	3	TRUE
+1001000178.C6	7	2496	FALSE
+1001000037.F10	9	13783	FALSE
+1001000245.H4	15	6833	FALSE
+1001000012.B10	6	3815	FALSE
+1001000245.F2	8	4747	FALSE
+1001000249.G2	9	3160	FALSE
+1001000187.E11	4	1692	FALSE
+1001000266.A4	11	3913	FALSE
+1001000266.G4	6	270	TRUE
+1001000179.E3	3	1179	FALSE
+1001000178.C11	4	2273	FALSE
+1001000031.D12	5	2682	FALSE
+1001000037.D6	7	3993	FALSE
+1001000250.G2	18	4537	FALSE
+1001000018.F11	5	4684	FALSE
+1001000175.F9	21	10873	FALSE
+1001000254.G1	7	615	TRUE
+1001000264.F12	7	1648	FALSE
+1001000183.B3	9	2185	FALSE
+1001000241.E6	7	2771	FALSE
+1001000183.E6	20	8590	FALSE
+1001000181.F10	9	3255	FALSE
+1001000176.B1	12	4110	FALSE
+1001000235.B7	10	764	TRUE
+1001000231.D12	10	1999	FALSE
+1001000230.E7	14	2447	FALSE
+1001000186.H6	6	1228	FALSE
+1001000258.H5	14	2413	FALSE
+1001000237.H10	8	1306	FALSE
+1001000231.B7	23	3144	FALSE
+1001000270.H8	9	2180	FALSE
+1001000240.G1	3	209	TRUE
+1001000177.D11	5	766	TRUE
+1001000185.D3	21	9950	FALSE
+1001000238.B5	3	929	TRUE
+1001000174.E10	13	6680	FALSE
diff -r 000000000000 -r e63bd8f13679 test-data/absolute_counts-only.pdf
Binary file test-data/absolute_counts-only.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/absolute_counts-only.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_counts-only.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-and-counts.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_gene-and-counts.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-and-counts.pdf
Binary file test-data/absolute_gene-and-counts.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-and-counts.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_gene-and-counts.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-only.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_gene-only.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-only.pdf
Binary file test-data/absolute_gene-only.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/absolute_gene-only.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/absolute_gene-only.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/input.csv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input.csv	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/input.tsv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/input.tsv	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+""	"1001000235.G10"	"1001000174.B1"	"1001000177.C5"	"1001000182.H4"	"1001000175.A2"	"1001000230.B11"	"1001000237.E9"	"1001000173.E10"	"1001000241.E7"	"1001000249.A3"	"1001000179.A7"	"1001000247.F11"	"1001000240.D9"	"1001000178.G5"	"1001000249.E3"	"1001000174.H6"	"1001000181.F8"	"1001000177.A10"	"1001000235.D2"	"1001000240.G10"	"1001000248.E7"	"1001000012.B6"	"1001000179.H4"	"1001000230.C1"	"1001000175.H9"	"1001000241.G11"	"1001000174.G2"	"1001000252.C6"	"1001000186.D11"	"1001000239.G7"	"1001000258.G11"	"1001000264.A1"	"1001000174.A10"	"1001000255.E9"	"1001000242.B5"	"1001000179.F3"	"1001000185.F9"	"1001000267.F8"	"1001000183.G10"	"1001000247.E7"	"1001000031.A2"	"1001000271.B1"	"1001000187.G6"	"1001000236.C6"	"1001000238.C12"	"1001000187.D6"	"1001000235.E10"	"1001000036.C1"	"1001000253.H2"	"1001000231.C2"	"1001000178.C10"	"1001000267.C1"	"1001000180.E4"	"1001000173.E5"	"1001000179.F5"	"1001000245.G11"	"1001000185.D5"	"1001000012.A7"	"1001000010.B4"	"1001000265.D11"	"1001000032.F1"	"1001000036.H9"	"1001000245.B3"	"1001000185.A8"	"1001000178.C6"	"1001000037.F10"	"1001000245.H4"	"1001000012.B10"	"1001000245.F2"	"1001000249.G2"	"1001000187.E11"	"1001000266.A4"	"1001000266.G4"	"1001000179.E3"	"1001000178.C11"	"1001000031.D12"	"1001000037.D6"	"1001000250.G2"	"1001000018.F11"	"1001000175.F9"	"1001000254.G1"	"1001000264.F12"	"1001000183.B3"	"1001000241.E6"	"1001000183.E6"	"1001000181.F10"	"1001000176.B1"	"1001000235.B7"	"1001000231.D12"	"1001000230.E7"	"1001000186.H6"	"1001000258.H5"	"1001000237.H10"	"1001000231.B7"	"1001000270.H8"	"1001000240.G1"	"1001000177.D11"	"1001000185.D3"	"1001000238.B5"	"1001000174.E10"
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diff -r 000000000000 -r e63bd8f13679 test-data/intersect_percentile_gene-and-counts.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/intersect_percentile_gene-and-counts.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/intersect_percentile_gene-and-counts.pdf
Binary file test-data/intersect_percentile_gene-and-counts.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/intersect_percentile_gene-and-counts.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/intersect_percentile_gene-and-counts.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/no-filtering.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/no-filtering.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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+1001000235.D2	11	5361	FALSE
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diff -r 000000000000 -r e63bd8f13679 test-data/no-filtering.pdf
Binary file test-data/no-filtering.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/no-filtering.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/no-filtering.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_counts-only.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_counts-only.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_counts-only.pdf
Binary file test-data/percentile_counts-only.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/percentile_counts-only.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_counts-only.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+Genes	1001000174.B1	1001000177.C5	1001000182.H4	1001000175.A2	1001000230.B11	1001000237.E9	1001000173.E10	1001000241.E7	1001000249.A3	1001000247.F11	1001000240.D9	1001000178.G5	1001000249.E3	1001000174.H6	1001000181.F8	1001000177.A10	1001000235.D2	1001000012.B6	1001000230.C1	1001000241.G11	1001000174.G2	1001000252.C6	1001000186.D11	1001000174.A10	1001000255.E9	1001000242.B5	1001000179.F3	1001000185.F9	1001000267.F8	1001000183.G10	1001000247.E7	1001000031.A2	1001000271.B1	1001000187.G6	1001000236.C6	1001000187.D6	1001000235.E10	1001000036.C1	1001000231.C2	1001000178.C10	1001000267.C1	1001000180.E4	1001000173.E5	1001000179.F5	1001000245.G11	1001000185.D5	1001000012.A7	1001000010.B4	1001000032.F1	1001000036.H9	1001000178.C6	1001000037.F10	1001000245.H4	1001000012.B10	1001000245.F2	1001000249.G2	1001000187.E11	1001000266.A4	1001000179.E3	1001000178.C11	1001000031.D12	1001000037.D6	1001000250.G2	1001000018.F11	1001000175.F9	1001000264.F12	1001000183.B3	1001000241.E6	1001000183.E6	1001000181.F10	1001000176.B1	1001000231.D12	1001000230.E7	1001000186.H6	1001000258.H5	1001000237.H10	1001000231.B7	1001000270.H8	1001000185.D3	1001000174.E10
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-and-counts.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_gene-and-counts.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-and-counts.pdf
Binary file test-data/percentile_gene-and-counts.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-and-counts.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_gene-and-counts.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-only.meta
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_gene-only.meta	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
+cell_id	nGenes	total_counts	filtered
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diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-only.pdf
Binary file test-data/percentile_gene-only.pdf has changed
diff -r 000000000000 -r e63bd8f13679 test-data/percentile_gene-only.tab
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/percentile_gene-only.tab	Mon Jun 24 13:37:45 2019 -0400
@@ -0,0 +1,101 @@
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