DE.voom: Differential Expression Analysis Using Voom-limma Pipeline

In LXQin/PRECISION.seq: Comparison of Depth Normalization Methods

Description

Perform DEA using the voom-limma pipeline on a normalized dataset. The normalized data can be provided as normalized counts or by adjusting factor for the original count data.

Usage

DE.voom(RC, groups, Pval = 0.01, normalized = TRUE, adjust = NULL)

Arguments

RC	Data in the format of a data frame or matrix, with columns for samples and rows for genes.
groups	Vector of characters indicating the group label for each sample.
Pval	Cut-off point for p-values to identify differentially expressed genes.
normalized	Logical, whether the data is provided as normalized counts. If set to FALSE, adjustment factors must be provided using the adjust parameter.
adjust	Adjusting factors for normalizing the count data. Must be provided if the data is not normalized beforehand as indicated by the normalized parameter.

Value

list, containing id.list (names of DE genes), p.val, and log2.FC.

References

Differential Expression with Limma-Voom

Examples

voom.benchmark <- DE.voom(data.benchmark, data.group)
str(voom.benchmark)

LXQin/PRECISION.seq documentation built on Dec. 18, 2021, 3:41 a.m.