# HG changeset patch
# User iuc
# Date 1563462785 14400
# Node ID 87757f7b997422128f7a8d35004e5c4da25363ae

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278

diff -r 000000000000 -r 87757f7b9974 README.md
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md	Thu Jul 18 11:13:05 2019 -0400
@@ -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
+```
diff -r 000000000000 -r 87757f7b9974 macros.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml	Thu Jul 18 11:13:05 2019 -0400
@@ -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>
diff -r 000000000000 -r 87757f7b9974 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 11:13:05 2019 -0400
@@ -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$output_loom, format='loom')
diff -r 000000000000 -r 87757f7b9974 scater-manual-filter.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-manual-filter.R	Thu Jul 18 11:13:05 2019 -0400
@@ -0,0 +1,84 @@
+#!/usr/bin/env Rscript
+# Manually filter SingleCellExperiment with user-defined parameters
+
+# Load optparse we need to check inputs
+library(optparse)
+library(workflowscriptscommon)
+library(LoomExperiment)
+library(scater)
+
+# parse options
+option_list = list(
+  make_option(
+    c("-i", "--input-loom"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "A SingleCellExperiment object file in Loom format."
+  ),
+  make_option(
+    c("-d", "--detection-limit"),
+    action = "store",
+    default = 0,
+    type = 'numeric',
+    help = "Numeric scalar providing the value above which observations are deemed to be expressed"
+  ),
+  make_option(
+    c("-l", "--library-size"),
+    action = "store",
+    default = 0,
+    type = 'numeric',
+    help = "Minimum library size (mapped reads) to filter cells on"
+  ),
+  make_option(
+    c("-m", "--percent-counts-MT"),
+    action = "store",
+    default = 100,
+    type = 'numeric',
+    help = "Maximum % of mitochondrial genes expressed per cell. Cells that exceed this value will be filtered out."
+  ),
+  make_option(
+    c("-o", "--output-loom"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "File name in which to store the SingleCellExperiment object in Loom format."
+  )
+)
+
+opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom'))
+
+# Check parameter values
+
+if ( ! file.exists(opt$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')
diff -r 000000000000 -r 87757f7b9974 scater-normalize.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-normalize.R	Thu Jul 18 11:13:05 2019 -0400
@@ -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')
diff -r 000000000000 -r 87757f7b9974 scater-normalize.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-normalize.xml	Thu Jul 18 11:13:05 2019 -0400
@@ -0,0 +1,28 @@
+<tool id="scater_normalize" name="Scater: normalize SCE" version="@TOOL_VERSION@">
+    <description>Compute normalised expression values for SCE</description>
+    <macros>
+        <import>macros.xml</import>
+    </macros>
+    <expand macro="requirements" />
+    <command detect_errors="exit_code"><![CDATA[
+Rscript '$__tool_directory__/scater-normalize.R'
+-i '$input_loom'
+-o '$output_loom'
+    ]]></command>
+    <inputs>
+        <param name="input_loom" type="data" format="loom" label="Input SingleCellLoomExperiment dataset to normalize" />
+    </inputs>
+    <outputs>
+        <data name="output_loom" format="loom" label="${tool.name} on ${on_string}" />
+    </outputs>
+    <tests>
+        <test>
+            <param name="input_loom" value="scater_manual_filtered.loom" ftype="loom" />
+            <output name="output_loom" file="scater_filtered_normalised.loom" compare="sim_size" />
+        </test>
+    </tests>
+    <help><![CDATA[
+Normalize a SingleCellLoomExperiment object using Scater. Adds 'logcounts' to assays slot.
+    ]]></help>
+    <expand macro="citations" />
+</tool>
diff -r 000000000000 -r 87757f7b9974 scater-pca-filter.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-pca-filter.R	Thu Jul 18 11:13:05 2019 -0400
@@ -0,0 +1,60 @@
+#!/usr/bin/env Rscript
+#Filters a SingleCellExperiment object, using PCA on the following metrics:
+# "pct_counts_top_100_features"
+# "total_features"
+# "pct_counts_feature_control"
+# "total_features_feature_control"
+# "log10_total_counts_endogenous"
+# "log10_total_counts_feature_control"
+
+# Load optparse we need to check inputs
+library(optparse)
+library(workflowscriptscommon)
+library(LoomExperiment)
+library(scater)
+library(mvoutlier)
+
+# parse options
+option_list = list(
+  make_option(
+    c("-i", "--input-loom"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "A SingleCellExperiment object file in Loom format."
+  ),
+  make_option(
+    c("-o", "--output-loom"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "File name in which to store the SingleCellExperiment object in Loom format."
+  )
+)
+
+opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_loom'))
+
+# Check parameter values
+
+if ( ! file.exists(opt$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')
diff -r 000000000000 -r 87757f7b9974 scater-plot-dist-scatter.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-plot-dist-scatter.R	Thu Jul 18 11:13:05 2019 -0400
@@ -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")
diff -r 000000000000 -r 87757f7b9974 scater-plot-exprs-freq.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-plot-exprs-freq.R	Thu Jul 18 11:13:05 2019 -0400
@@ -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")
diff -r 000000000000 -r 87757f7b9974 scater-plot-pca.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-plot-pca.R	Thu Jul 18 11:13:05 2019 -0400
@@ -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")
diff -r 000000000000 -r 87757f7b9974 test-data/annotation.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/annotation.txt	Thu Jul 18 11:13:05 2019 -0400
@@ -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
diff -r 000000000000 -r 87757f7b9974 test-data/counts.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/counts.txt	Thu Jul 18 11:13:05 2019 -0400
@@ -0,0 +1,2001 @@
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diff -r 000000000000 -r 87757f7b9974 test-data/mt_controls.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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