view scater-create-qcmetric-ready-sce.R @ 0:87757f7b9974 draft

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

#!/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')