bi.deg {DEComplexDisease} | R Documentation

## Transformation an expression matrix to binary differential expression matrix

### Description

Transform the RNA-seq counts or normalized expression matrix into binary differential expression matrix of -1, 0 and 1, which indicates the down-regulation, no change and up-regulation.

### Usage

    bi.deg(exp, cl, method = c("edger", "deseq2", "normalized")[1],
      cutoff = 0.05, cores = 1)

### Arguments

`exp` | a matrix or data frame for expression data. The expression value can be counts or normalized expression data
`cl` | a vector of 0 and 1. It has equal length with the column number of exp. 1 indicates the corresponding samples are patients and 0 is control or normal
`method` | defines the methods applied for DE analysis. The possible value is 'edger', 'deseq2', 'normalized'. 'edger' or 'deseq2' is used for RNA-seq count data; 'normalized' is used for normalized RNA-seq or microarray data
`cutoff` | the p-value cutoff for DEGs
`cores` | the thread number

### Details

For each sample in 'exp', 'cl' defines the patients and normal. The normal samples are used to construct the expression references with negative binomial distribution (e.g. method='edger' or method='deseq2') or a normal distribution (method='normalized').

When counts data are used, the DEG analysis is performed using the functions implemented by 'DESeq2' or 'edgeR'. The dispersion and mu values are estimated.

### Value

A deg class object with value of 1, 0 and -1.

### Author(s)

Guofeng Meng

### Examples

    deg <- bi.deg(exp,cl=cl, method='edger', cutoff=0.05) # exp is the RNA-seq counts matrix

[Package DEComplexDisease version 1.8.0 Index]