expDiff {CeTF} | R Documentation

## Differential expression analysis

### Description

This function returns the differentially expressed genes when comparing two conditions.

### Usage

    expDiff(
      exp,
      anno = NULL,
      conditions = NULL,
      lfc = 1.5,
      padj = 0.05,
      diffMethod = "Reverter"
    )

### Arguments

`exp` | Count data where the rows are genes and coluns the samples.
`anno` | A single column dataframe. The column name must be 'cond', and the rownames must be the names of samples.
`conditions` | A character vector containing the name of the two conditions. The first name will be selected as reference.
`lfc` | log2 fold change module threshold to define a gene as differentially expressed (default: 1.5).
`padj` | Significance value to define a gene as differentially expressed (default: 0.05).
`diffMethod` | Choose between Reverter or DESeq2 method (default: 'Reverter'). The DESeq2 method is only for counts data (see details).

### Details

The Reverter option to diffMethod parameter works as follows:

1. Calculation of mean between samples of each condition for all genes;

2. Subtraction between mean of control condition relative to other condition;

3. Calculation of variance of subtraction previously obtained;

4. The last step calculates the differential expression using the following formula, where x is the result of substraction (item 2) and var is the variance calculated in item 3:

   diff = \frac{x - (sum(x)/length(x))}{√{var}}

The DESeq2 option to diffMethod parameter is recommended only for count data. This method apply the differential expression analysis based on the negative binomial distribution (see `DESeq`).

### Value

Returns an list with all calculations of differentially expressed genes and the subsetted differentially expressed genes by lfc and/or padj.

### References

REVERTER, Antonio et al. Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer. Bioinformatics, v. 22, n. 19, p. 2396-2404, 2006. https://academic.oup.com/bioinformatics/article/22/19/2396/240742

### Examples

    # loading a simulated counts data
    data('simCounts')

    # creating the dataframe with annotation for each sample
    anno <- data.frame(cond = c(rep('cond1', 10), rep('cond2', 10)))

    # renaming colums of simulated counts data
    colnames(simCounts) <- paste(colnames(simCounts), anno$cond, sep = '_')

    # renaming anno rows
    rownames(anno) <- colnames(simCounts)

    # performing differential expression analysis using Reverter method
    out <- expDiff(exp = simCounts,
                   anno = anno,
                   conditions = c('cond1', 'cond2'),
                   lfc = 2,
                   padj = 0.05,
                   diffMethod = 'Reverter')

[Package CeTF version 1.6.0 Index]