expDiff | R Documentation |

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

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

`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). |

The **Reverter** option to diffMethod parameter works as follows:

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

Subtraction between mean of control condition relative to other condition;

Calculation of variance of subtraction previously obtained;

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`

).

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

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

# 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')

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