prepCells: Prepare matrix of expression values for downstream analyses

In joeburns06/hocuspocus: A Suite of Single-Cell RNA-Seq Analysis Tools Built for Dummy Biologists

Description

Takes ExpressionSet object and (optionally) 1) normalizes the expression levels across samples using the median of the geometric means method found within the DESeq package, 2) transforms the values in the matrix of expression values to a log2 scale, 3) sets expression values below the set limit of detection (LOD) to zero prior to the log2 transformation, and 4) provides an option to eliminate potential batch effects specified in a phenoData column using the ComBat method within the sva package.

Usage

prepCells(cellData, LOD = 1, norm = TRUE, batchGroup)

Arguments

cellData	ExpressionSet object created with readCells.
LOD	Limit of detection (numeric). All expression values below this limit will be set to zero prior to the log2 transformation. LOD is best determined by spiking in a dilution series of RNA standards with known concentration.
norm	Boolean specifying whether to normalize the expression values of each gene across all samples in the ExpressionSet object using the median of the geometric means. This helps with making expression values more comparable across samples. See explanation of the estimateSizeFactorsForMatrix function within the DESeq package documentation for more explanation.
batchGroup	Character string specifying the name of the column header in pData where the batch group information is stored. All samples within a particular batch group should have the same designation. If no character string is specified, batch is not run. E.g. if there are four samples and two batch groups, the information in the batch group column could look like c('Batch1','Batch1','Batch2','Batch2').

Value

ExpressionSet object modified according to the optional parameters specified above. Note that it is highly recommended that you at least log-transform the data prior to any further downstream analysis.

joeburns06/hocuspocus documentation built on May 19, 2019, 2:59 p.m.