WAD: Calculate WAD statistic for individual genes

In TCC: TCC: Differential expression analysis for tag count data with robust normalization strategies

Description

This function performs WAD method to identify differentially expressed genes (DEGs) from two-group gene expression data. A high absolute value for the WAD statistic is evident of a high degree of differential expression.

Usage

WAD(data, group, logged = FALSE, floor = 1, sort = FALSE)

Arguments

data	numeric matrix or data frame containing count data or microarray data, where each row indicates the gene (or transcript or probeset ID), each column indicates the sample (or library), and each cell indicates the expression value (i.e., number of counts or signal intensity) of the gene in the sample.
group	numeric vector indicating the experimental group for each sample (or library).
logged	logical. If TRUE, the input data are regarded as log2-transformed. If FALSE, the log2-transformation is performed after the floor setting. The default is logged = FALSE.
floor	numeric scalar (> 0) specifying the floor value for taking logarithm. The default is floor = 1, indicating that values less than 1 are replaced by 1. Ignored if logged = TRUE.
sort	logical. If TRUE, the retrieved results are sorted in order of the rank of absolute WAD statistic. If FALSE, the results are retrieved by the original order.

Value

A numeric vector of WAD statistic for individual genes

References

Kadota K, Nakai Y, Shimizu K: A weighted average difference method for detecting differentially expressed genes from microarray data. Algorithms Mol Biol. 2008, 3: 8.

Examples

data(nakai)
group <- c(1, 1, 1, 1, 2, 2, 2, 2)

wad <- WAD(nakai, group, logged = TRUE, sort = TRUE)

TCC documentation built on Nov. 8, 2020, 8:20 p.m.