| Next changeset 1:2e41b35b5bdd (2019-09-03) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278 |
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added:
README.md macros.xml scater-create-qcmetric-ready-sce.R scater-manual-filter.R scater-normalize.R scater-pca-filter.R scater-plot-dist-scatter.R scater-plot-dist-scatter.xml scater-plot-exprs-freq.R scater-plot-pca.R test-data/annotation.txt test-data/counts.txt test-data/mt_controls.txt test-data/scater_exprs_freq.pdf test-data/scater_filtered_normalised.loom test-data/scater_manual_filtered.loom test-data/scater_pca_filtered.loom test-data/scater_pca_plot.pdf test-data/scater_qcready.loom test-data/scater_reads_genes_dist.pdf |
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| diff -r 000000000000 -r 4887c4c69847 README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,84 @@ +# Wrappers for Scater + +This code wraps a number of [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) functions as Galaxy wrappers. Briefly, the `scater-create-qcmetric-ready-sce` tool takes a sample gene expression matrix (usually read-counts) and a cell annotation file, creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics` function (using other supplied files such as ERCC's and mitochondrial gene features). +Various filter scripts are provided, along with some plotting functions for QC. + + +## Typical workflow + +1. Read in data with `scater-create-qcmetric-ready-sce`. +2. Visualise it.\ + Take a look at the distribution of library sizes, expressed features and mitochondrial genes with `scater-plot-dist-scatter`. + Then look at the distibution of genes across cells with `scater-plot-exprs-freq`. +3. Guided by the plots, filter the data with `scater-filter`.\ + You can either manually filter with user-defined parameters or use PCA to automatically removes outliers. +4. Visualise data again to see how the filtering performed using `scater-plot-dist-scatter`.\ + Decide if you're happy with the data. If not, try increasing or decreasing the filtering parameters. +5. Normalise data with `scater-normalize`. +6. Investigate other confounding factors.\ + Plot the data (using PCA) and display various annotated properties of the cells using `scater-plot-pca`. + +## Command-line usage + +For help with any of the following scripts, run: + `<script-name> --help` + +--- + +`scater-create-qcmetric-ready-sce.R` +Takes an expression matrix (usually read-counts) of samples (columns) and gene/transcript features (rows), along with other annotation information, such as cell metadata, control genes (mitochondrail genes, ERCC's), creates a [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) object and runs scater's `calculateQCMetrics`. Save the resulting SingleCellExperiment object in Loom format. + + +``` +./scater-create-qcmetric-ready-sce.R -a test-data/counts.txt -c test-data/annotation.txt -f test-data/mt_controls.txt -o test-data/scater_qcready.loom +``` + +--- + +`scater-plot-dist-scatter.R` +Takes SingleCellExperiment object (from Loom file) and plots a panel of read and feature graphs, including the distribution of library sizes, distribution of feature counts, a scatterplot of reads vs features, and % of mitochondrial genes in library. + +``` +./scater-plot-dist-scatter.R -i test-data/scater_qcready.loom -o test-data/scater_reads_genes_dist.pdf +``` + +--- + +`scater-plot-exprs-freq.R` +Plots mean expression vs % of expressing cells and provides information as to the number of genes expressed in 50% and 25% of cells. + +--- + +`scater-pca-filter.R` +Takes SingleCellExperiment object (from Loom file) and automatically removes outliers from data using PCA. Save the filtered SingleCellExperiment object in Loom format. + +``` +./scater-pca-filter.R -i test-data/scater_qcready.loom -o test-data/scater_pca_filtered.loom +``` + +--- + +`scater-manual-filter.R` +Takes SingleCellExperiment object (from Loom file) and filters data using user-provided parameters. Save the filtered SingleCellExperiment object in Loom format. + +``` +./scater-manual-filter.R -i test-data/scater_qcready.loom -l 10000 -d 4 -m 33 -o test-data/scater_manual_filtered.loom +``` + +--- + +`scater-normalize.R` +Compute log-normalized expression values from count data in a SingleCellExperiment object, using the size factors stored in the object. Save the normalised SingleCellExperiment object in Loom format. + +``` +./scater-normalize.R -i test-data/scater_manual_filtered.loom -o test-data/scater_man_filtered_normalised.loom +``` + +--- + +`scater-plot-pca.R` +PCA plot of a normalised SingleCellExperiment object (produced with `scater-normalize.R`). The options `-c`, `-p`, and `-s` all refer to cell annotation features. These are the column headers of the `-c` option used in `scater-create-qcmetric-ready-sce.R`. + +``` +./scater-plot-pca.R -i test-data/scater_man_filtered_normalised.loom -c Treatment -p Mutation_Status -o test-data/scater_pca_plot.pdf +``` |
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| diff -r 000000000000 -r 4887c4c69847 macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,17 @@ +<macros> + <token name="@TOOL_VERSION@">1.10.1</token> + <xml name="requirements"> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">bioconductor-scater</requirement> + <requirement type="package" version="1.6.2">r-optparse</requirement> + <requirement type="package" version="0.0.4">r-workflowscriptscommon</requirement> + <requirement type="package" version="1.0.4">bioconductor-loomexperiment</requirement> + <yield /> + </requirements> + </xml> + <xml name="citations"> + <citations> + <citation type="doi">10.1093/bioinformatics/btw777</citation> + </citations> + </xml> +</macros> |
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| diff -r 000000000000 -r 4887c4c69847 scater-create-qcmetric-ready-sce.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-create-qcmetric-ready-sce.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,142 @@ +#!/usr/bin/env Rscript +#Creates a SingleCellExperiment object, which scater's calculateQCMetrics already applied + +library(optparse) +library(workflowscriptscommon) +library(scater) +library(LoomExperiment) + +# parse options +#SCE-specific options +option_list = list( + make_option( + c("-a", "--counts"), + action = "store", + default = NA, + type = 'character', + help = "A tab-delimited expression matrix. The first column of all files is assumed to be feature names and the first row is assumed to be sample names." + ), + make_option( + c("-r", "--row-data"), + action = "store", + default = NULL, + type = 'character', + help = "Path to TSV (tab-delimited) format file describing the features. Row names from the expression matrix (-a), if present, become the row names of the SingleCellExperiment." + ), + make_option( + c("-c", "--col-data"), + action = "store", + default = NULL, + type = 'character', + help = "Path to TSV format file describing the samples (annotation). The number of rows (samples) must equal the number of columns in the expression matrix." + ), + #The scater-specific options + make_option( + c("--assay-name"), + action = "store", + default = 'counts', + type = 'character', + help= "String specifying the name of the 'assay' of the 'object' that should be used to define expression." + ), + make_option( + c("-f", "--mt-controls"), + action = "store", + default = NULL, + type = 'character', + help = "Path to file containing a list of the mitochondrial control genes" + ), + make_option( + c("-p", "--ercc-controls"), + action = "store", + default = NULL, + type = 'character', + help = "Path to file containing a list of the ERCC controls" + ), + make_option( + c("-l", "--cell-controls"), + action = "store", + default = NULL, + type = 'character', + help = "Path to file (one cell per line) to be used to derive a vector of cell (sample) names used to identify cell controls (for example, blank wells or bulk controls)." + ), + make_option( + c("-o", "--output-loom"), + action = "store", + default = NA, + type = 'character', + help = "File name in which to store the SingleCellExperiment object in Loom format." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('counts', 'output_loom')) + +# Read the expression matrix + +counts <- wsc_split_string(opt$counts) +reads <- read.table(counts) + +# Read row and column annotations + +rowdata <- opt$row_data + +if ( ! is.null(opt$row_data) ){ + rowdata <- read.delim(opt$row_data) +} + +coldata <- opt$col_data + +if ( ! is.null(opt$col_data) ){ + coldata <- read.delim(opt$col_data) +} + +# Now build the object +assays <- list(as.matrix(reads)) +names(assays) <- c(opt$assay_name) +scle <- SingleCellLoomExperiment(assays = assays, colData = coldata, rowData = rowdata) +# Define spikes (if supplied) + + +#Scater options + +# Check feature_controls (only mitochondrial and ERCC used for now) +feature_controls_list = list() +if (! is.null(opt$mt_controls) && opt$mt_controls != 'NULL'){ + if (! file.exists(opt$mt_controls)){ + stop((paste('Supplied feature_controls file', opt$mt_controls, 'does not exist'))) + } else { + mt_controls <- readLines(opt$mt_controls) + feature_controls_list[["MT"]] <- mt_controls + } +} + +if (! is.null(opt$ercc_controls) && opt$ercc_controls != 'NULL'){ + if (! file.exists(opt$ercc_controls)){ + stop((paste('Supplied feature_controls file', opt$ercc_controls, 'does not exist'))) + } else { + ercc_controls <- readLines(opt$ercc_controls) + feature_controls_list[["ERCC"]] <- ercc_controls + } +} else { + ercc_controls <- character() +} + +# Check cell_controls +cell_controls_list <- list() +if (! is.null(opt$cell_controls) && opt$cell_controls != 'NULL'){ + if (! file.exists(opt$cell_controls)){ + stop((paste('Supplied feature_controls file', opt$cell_controls, 'does not exist'))) + } else { + cell_controls <- readLines(opt$cell_controls) + cell_controls_list[["empty"]] <- cell_controls + } +} + + +# calculate QCMs +scle <- calculateQCMetrics(scle, exprs_values = opt$assay_name, feature_controls = feature_controls_list, cell_controls = cell_controls_list) + +# Output to a Loom file +if (file.exists(opt$output_loom)) { + file.remove(opt$output_loom) +} +export(scle, opt$output_loom, format='loom') |
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| diff -r 000000000000 -r 4887c4c69847 scater-manual-filter.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-manual-filter.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,84 @@ +#!/usr/bin/env Rscript +# Manually filter SingleCellExperiment with user-defined parameters + +# Load optparse we need to check inputs +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) + +# parse options +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-d", "--detection-limit"), + action = "store", + default = 0, + type = 'numeric', + help = "Numeric scalar providing the value above which observations are deemed to be expressed" + ), + make_option( + c("-l", "--library-size"), + action = "store", + default = 0, + type = 'numeric', + help = "Minimum library size (mapped reads) to filter cells on" + ), + make_option( + c("-m", "--percent-counts-MT"), + action = "store", + default = 100, + type = 'numeric', + help = "Maximum % of mitochondrial genes expressed per cell. Cells that exceed this value will be filtered out." + ), + make_option( + c("-o", "--output-loom"), + action = "store", + default = NA, + type = 'character', + help = "File name in which to store the SingleCellExperiment object in Loom format." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom')) + +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + +# Filter out unexpressed features + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') +print(paste("Starting with", ncol(scle), "cells and", nrow(scle), "features.")) + +# Create a logical vector of features that are expressed (above detection_limit) +feature_expressed <- nexprs(scle, detection_limit = opt$detection_limit, exprs_values = 1, byrow=TRUE) > 0 +scle <- scle[feature_expressed, ] + +print(paste("After filtering out unexpressed features: ", ncol(scle), "cells and", nrow(scle), "features.")) + +# Filter low library sizes +to_keep <- scle$total_counts > opt$library_size +scle <- scle[, to_keep] + +print(paste("After filtering out low library counts: ", ncol(scle), "cells and", nrow(scle), "features.")) + +# Filter out high MT counts +to_keep <- scle$pct_counts_MT < opt$percent_counts_MT +scle <- scle[, to_keep] + +print(paste("After filtering out high MT gene counts: ", ncol(scle), "cells and", nrow(scle), "features.")) + +# Output to a Loom file +if (file.exists(opt$output_loom)) { + file.remove(opt$output_loom) +} +export(scle, opt$output_loom, format='loom') |
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| diff -r 000000000000 -r 4887c4c69847 scater-normalize.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-normalize.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,50 @@ +#!/usr/bin/env Rscript +#Normalises a SingleCellExperiment object + +# Load optparse we need to check inputs +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) + +# parse options +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-o", "--output-loom"), + action = "store", + default = NA, + type = 'character', + help = "File name in which to store the SingleCellExperiment object in Loom format." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom')) + +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + +# Input from Loom format + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') +print(paste("Normalising....")) + +#Normalise +scle <- normalize(scle, exprs_values = 1) + +print(paste("Finished normalising")) + +# Output to a Loom file +if (file.exists(opt$output_loom)) { + file.remove(opt$output_loom) +} +export(scle, opt$output_loom, format='loom') |
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| diff -r 000000000000 -r 4887c4c69847 scater-pca-filter.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-pca-filter.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,60 @@ +#!/usr/bin/env Rscript +#Filters a SingleCellExperiment object, using PCA on the following metrics: +# "pct_counts_top_100_features" +# "total_features" +# "pct_counts_feature_control" +# "total_features_feature_control" +# "log10_total_counts_endogenous" +# "log10_total_counts_feature_control" + +# Load optparse we need to check inputs +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) +library(mvoutlier) + +# parse options +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-o", "--output-loom"), + action = "store", + default = NA, + type = 'character', + help = "File name in which to store the SingleCellExperiment object in Loom format." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom')) + +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + +# Input from Loom format + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') +print(paste("Starting with", ncol(scle), "cells and", nrow(scle), "features.")) + +# Run PCA on data and detect outliers +scle <- runPCA(scle, use_coldata = TRUE, detect_outliers = TRUE) + +# Filter out outliers +scle <- scle[, !scle$outlier] + +print(paste("Ending with", ncol(scle), "cells and", nrow(scle), "features.")) + +# Output to a Loom file +if (file.exists(opt$output_loom)) { + file.remove(opt$output_loom) +} +export(scle, opt$output_loom, format='loom') |
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| diff -r 000000000000 -r 4887c4c69847 scater-plot-dist-scatter.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-plot-dist-scatter.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,62 @@ +#!/usr/bin/env Rscript + +# Plot the distribution of read counts and feature counts, side by side, then a scatter plot of read counts vs feature counts below + +# Load optparse we need to check inputs + +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) +library(ggpubr) + +# parse options + +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-o", "--output-plot-file"), + action = "store", + default = NA, + type = 'character', + help = "Path of the PDF output file to save plot to." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_plot_file')) + +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + +# Input from Loom format + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') + +#do the scatter plot of reads vs genes +total_counts <- scle$total_counts +total_features <- scle$total_features_by_counts +count_feats <- cbind(total_counts, total_features) +cf_dm <- as.data.frame(count_feats) + +# Calculate binwidths for reads and features plots. Use 20 bins +read_bins <- max(total_counts / 1e6) / 20 +feat_bins <- max(total_features) / 20 + +#make the plots +plot <- ggplot(cf_dm, aes(x=total_counts / 1e6, y=total_features)) + geom_point(shape=1) + geom_smooth() + xlab("Read count (millions)") + + ylab("Feature count") + ggtitle("Scatterplot of reads vs features") +plot1 <- qplot(total_counts / 1e6, geom="histogram", binwidth = read_bins, ylab="Number of cells", xlab = "Read counts (millions)", fill=I("darkseagreen3")) + ggtitle("Read counts per cell") +plot2 <- qplot(total_features, geom="histogram", binwidth = feat_bins, ylab="Number of cells", xlab = "Feature counts", fill=I("darkseagreen3")) + ggtitle("Feature counts per cell") +plot3 <- plotColData(scle, y="pct_counts_MT", x="total_features_by_counts") + ggtitle("% MT genes") + geom_point(shape=1) + theme(text = element_text(size=15)) + theme(plot.title = element_text(size=15)) + +final_plot <- ggarrange(plot1, plot2, plot, plot3, ncol=2, nrow=2) +ggsave(opt$output_plot_file, final_plot, device="pdf") |
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| diff -r 000000000000 -r 4887c4c69847 scater-plot-dist-scatter.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-plot-dist-scatter.xml Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,30 @@ +<tool id="scater_plot_dist_scatter" name="Scater: plot library QC" version="@TOOL_VERSION@"> + <description>Create plots to visualise library size, feature counts and mito gene expression</description> + <macros> + <import>macros.xml</import> + </macros> + <expand macro="requirements"> + <requirement type="package" version="0.2.1">r-ggpubr</requirement> + </expand> + <command detect_errors="exit_code"><![CDATA[ +Rscript '$__tool_directory__/scater-plot-dist-scatter.R' +-i '$input_loom' +-o '$output_plot' + ]]></command> + <inputs> + <param name="input_loom" type="data" format="loom" label="Input SingleCellLoomExperiment dataset" /> + </inputs> + <outputs> + <data name="output_plot" format="pdf" label="${tool.name} on ${on_string}" /> + </outputs> + <tests> + <test> + <param name="input_loom" value="scater_qcready.loom" ftype="loom" /> + <output name="output_plot" file="scater_reads_genes_dist.pdf" compare="sim_size" /> + </test> + </tests> + <help><![CDATA[ +Plot distribution of reads and features for single-cell RNA-seq analysis using Scater. + ]]></help> + <expand macro="citations" /> +</tool> |
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| diff -r 000000000000 -r 4887c4c69847 scater-plot-exprs-freq.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-plot-exprs-freq.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,45 @@ +#!/usr/bin/env Rscript + +#Plots mean expression vs % of expressing cells and provides information as to the number of genes expressed in 50% and 25% of cells. +# Load optparse we need to check inputs + +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) + +# parse options + +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-o", "--output-plot-file"), + action = "store", + default = NA, + type = 'character', + help = "Path of the PDF output file to save plot to." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_plot_file')) + +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + + +# Input from Loom format + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') + +#produce and save the scatter plot of reads vs genes +plot <- plotExprsFreqVsMean(scle, controls = "is_feature_control_MT") +ggsave(opt$output_plot_file, plot, device="pdf") |
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| diff -r 000000000000 -r 4887c4c69847 scater-plot-pca.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scater-plot-pca.R Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,68 @@ +#!/usr/bin/env Rscript + +# Creates a PCA plot of a normalised SingleCellExperiment object. + +# Load optparse we need to check inputs + +library(optparse) +library(workflowscriptscommon) +library(LoomExperiment) +library(scater) + +# parse options + +option_list = list( + make_option( + c("-i", "--input-loom"), + action = "store", + default = NA, + type = 'character', + help = "A SingleCellExperiment object file in Loom format." + ), + make_option( + c("-c", "--colour-by"), + action = "store", + default = NULL, + type = 'character', + help = "Feature (from annotation file) to colour PCA plot points by. The values represented in this options should be categorical" + ), + make_option( + c("-s", "--size-by"), + action = "store", + default = NULL, + type = 'character', + help = "Feature (from annotation file) to size PCA plot points by. The values represented in this options should be numerical and not categorical" + ), + make_option( + c("-p", "--shape-by"), + action = "store", + default = NULL, + type = 'character', + help = "Feature (from annotation file) to shape PCA plot points by. The values represented in this options should be categorical" + ), + make_option( + c("-o", "--output-plot-file"), + action = "store", + default = NA, + type = 'character', + help = "Path of the PDF output file to save plot to." + ) +) + +opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_plot_file')) +# Check parameter values + +if ( ! file.exists(opt$input_loom)){ + stop((paste('File', opt$input_loom, 'does not exist'))) +} + + +# Input from Loom format + +scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment') +scle <- normalize(scle, exprs_values = 1) +scle <- runPCA(scle) +plot <- plotReducedDim(scle, "PCA", colour_by = opt$colour_by, size_by = opt$size_by, shape_by = opt$shape_by) +#do the scatter plot of reads vs genes + +ggsave(opt$output_plot_file, plot, device="pdf") |
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| diff -r 000000000000 -r 4887c4c69847 test-data/annotation.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/annotation.txt Thu Jul 18 11:12:33 2019 -0400 |
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| @@ -0,0 +1,41 @@ +Cell Mutation_Status Cell_Cycle Treatment +Cell_001 positive S treat1 +Cell_002 positive G0 treat1 +Cell_003 negative G1 treat1 +Cell_004 negative S treat1 +Cell_005 negative G1 treat2 +Cell_006 negative G0 treat1 +Cell_007 positive S treat1 +Cell_008 positive G1 treat2 +Cell_009 negative G0 treat1 +Cell_010 positive G0 treat1 +Cell_011 negative G0 treat2 +Cell_012 negative G0 treat1 +Cell_013 positive S treat1 +Cell_014 negative G1 treat2 +Cell_015 negative G1 treat1 +Cell_016 negative G0 treat1 +Cell_017 positive G0 treat1 +Cell_018 negative S treat1 +Cell_019 negative S treat1 +Cell_020 negative G1 treat2 +Cell_021 positive G1 treat2 +Cell_022 positive G0 treat1 +Cell_023 negative G0 treat2 +Cell_024 positive S treat1 +Cell_025 negative G0 treat1 +Cell_026 positive G0 treat2 +Cell_027 positive G1 treat1 +Cell_028 negative G2M treat1 +Cell_029 positive G0 treat2 +Cell_030 negative G1 treat2 +Cell_031 negative S treat1 +Cell_032 positive G0 treat2 +Cell_033 positive S treat1 +Cell_034 negative G1 treat1 +Cell_035 positive G1 treat1 +Cell_036 negative G0 treat1 +Cell_037 negative G0 treat1 +Cell_038 negative G0 treat2 +Cell_039 negative G1 treat1 +Cell_040 negative G0 treat2 |
| b |
| diff -r 000000000000 -r 4887c4c69847 test-data/counts.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/counts.txt Thu Jul 18 11:12:33 2019 -0400 |
| b |
| b'@@ -0,0 +1,2001 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| diff -r 000000000000 -r 4887c4c69847 test-data/mt_controls.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mt_controls.txt Thu Jul 18 11:12:33 2019 -0400 |
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