In bhklab/MetaGxPancreas: Transcriptomic Pancreatic Cancer Datasets

Installing the Package

The MetaGxPancreas package is a compendium of Pancreatic Cancer datasets. The package is publicly available and can be installed from Bioconductor into R version 3.6.0 or higher. Currently, the phenoData for the datasets is overall survival status and overall survival time. This survival information is available for 11 of the 15 datasets.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("MetaGxPancreas")

Loading Datasets

First we load the MetaGxPancreas package into the workspace.

library(MetaGxPancreas)

pancreasData <- loadPancreasDatasets()
duplicates <- pancreasData$duplicates
SEs <- pancreasData$SEs

This will load 15 expression datasets. Users can modify the parameters of the function to restrict datasets that do not meet certain criteria for loading. Some example parameters are shown below:

removeDuplicates: remove patients with a Spearman correlation greater than or equal to 0.98 with other patient expression profiles (default TRUE)
quantileCutoff: A numeric between 0 and 1 specifying to remove genes with standard deviation below the required quantile (default 0)
rescale: apply centering and scaling to the expression sets (default FALSE)
minNumberGenes: an integer specifying to remove expression sets with less genes than this number (default 0)
minSampleSize: an integer specifying the minimum number of patients required in an SE (default 0)
minNumberEvents: an integer specifying how man survival events must be in the dataset to keep the dataset (default 0)
removeSeqSubset currently only removes the ICGSSEQ dataset as it contains the same patients as the ICGS microarray dataset (defeault TRUE, currently just ICGSSEQ)
keepCommonOnly remove probes not common to all datasets (default FALSE)
imputeMissing impute missing expression value via knn

Obtaining Sample Counts in Datasets

To obtain the number of samples per dataset, run the following:

numSamples <- vapply(SEs, function(SE) length(colnames(SE)), FUN.VALUE=numeric(1))

sampleNumberByDataset <- data.frame(numSamples=numSamples,
                                    row.names=names(SEs))

totalNumSamples <- sum(sampleNumberByDataset$numSamples)
sampleNumberByDataset <- rbind(sampleNumberByDataset, totalNumSamples)
rownames(sampleNumberByDataset)[nrow(sampleNumberByDataset)] <- 'Total'

knitr::kable(sampleNumberByDataset)