WAD {TCC} | R Documentation

## Calculate WAD statistic for individual genes

### 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)

[Package TCC version 1.26.0 Index]