DSS.DE {DSS} | R Documentation

## 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 <hao.wu@emory.edu>

### Examples

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

[Package DSS version 2.36.0 Index]