DSS.DE: Perform RNA-seq differential expression analysis in two-group...

In haowulab/DSS: Dispersion shrinkage for sequencing data

Perform RNA-seq differential expression analysis in two-group comparison

Description

This is the top level wrapper function for RNA-seq differential expression analysis in a two-group comparison. Users only need to provide the count matrix and a vector for design, and obtain DE test results.

Usage

DSS.DE(counts, design)

Arguments

counts	A matrix of integers with rows corresponding to genes and columns for samples.
design	A vector representing the treatment groups. It must be a vector of 0 and 1. The length of the vector must match the number of columns of input count matrix.

Value

A data frame with each row corresponding to a gene. Rows are sorted according to wald test statistics. The columns are:

gene Index	index for input gene orders, integers from 1 to the number of genes.
muA	sample mean (after normalization) for sample A.
muB	sample mean (after normalization) for sample B.
lfc	log fold change of expressions between two groups.
difExpr	differences in expressions between two groups.
stats	Wald test statistics.
pval	p-values.
others	input gene annotations supplied as AnnotatedDataFrame when constructed the SeqCountData object.

Author(s)

Hao Wu <[e-mail]>

Examples

counts = matrix(rpois(600, 10), ncol=6)
design = c(0,0,0,1,1,1)
result = DSS.DE(counts, design)
head(result)

haowulab/DSS documentation built on Oct. 28, 2023, 6:59 p.m.