diffExpression: Conduct differential gene expression analysis for a given...

In hemoshear/pathwayTalk:

Conduct differential gene expression analysis for a given gene expression dataset.

Description

Calculates log2 fold-changes and associated p-values for a given matrix of gene expression data. diffExpression() will split the data according to the levels of the 'group' column in the supplied 'groups' object, and generate appropriately cross-sectioned expression matrices, groups dataframes, and design matrices, which are returned with the differential expression results. These objects can be supplied to subsequent functions in the pathwayTalk pipeline.

Usage

diffExpression(
  expression_matrix,
  groups,
  platform,
  processes = 4,
  robust = TRUE,
  trend = TRUE
)

Arguments

expression_matrix	An matrix of gene expression data where the row names are gene probe identifiers and column names are sample identifiers. For RNAseq data, a matrix of un-normalized integer counts. For microarray data, a matrix of intensity values for gene probes with pre-processing completed, including log2 transformation, normalization, removal of control sequences.
groups	A dataframe containing the mappings between sample identifiers ('sample_id', a factor with the reference condition as the first level) and associated treatment conditions ('group'). The sample identifiers must be in the same order as the columns of the count_matrix.
platform	A string specifying the data type. Either 'rnaseq' or 'microarray'.

Value

A named list of contrasts, with each element containing the following objects

• data - The expression matrix cross-section relevant to the contrast.

• groups - The groups dataframe cross-section relevant to the contrast.

• design - The design matrix relevant to the contrast.

• DEG - A dataframe containing the results of the differential expression analysis.

hemoshear/pathwayTalk documentation built on July 16, 2022, 12:09 a.m.