categorizeGene {eegc} | R Documentation

## Gene Categorization

### Description

This function categorizes differential genes of each pair-wise comparison among e.g. initiating A, derived B and primary C samples during a cellular engineering, into five categories named from Reversed,Inactive,Insufficient, Successful and Over representing the gene reprogrammed states, and calculates the ratio of expression difference (ED) between B and A to the ED between C and A.

### Usage

    categorizeGene(expr, diffGene, from.sample,to.sample, target.sample)

### Arguments

`expr` | a data frame with expression for all genes in `diffGene`, alternatively output from `diffGene` function.
`diffGene` | a list of differential genes in three comparisons, alternatively output by `diffGene` function.
`from.sample, to.sample, target.sample` | character to specify the name of initiating sample, derived sample and primary sample during a cellular engineering, must be consistent with sample names in the `expr` data frame.

### Details

Gene (g) categorization is achieved by considering the pair-wise comparisons (Expression Difference, ED eq. 1) among the three types of samples and the ratio of such differences (EDg ratio, eq. 2). EDg(B, A) = average expression of g in B minus average expression of g in A (1) EDg ratio = EDg(B, A)/EDg(C, A) (2)

Reversed, EDg(B,A) and EDg(B,C) are significantly differential, while EDg(C,A) is not limited by differential or not, EDg ratio is smaller than 0; Inactive, EDg(C,A) and EDg(B,C) are significantly differential, while EDg(B,A) is not differential; Insufficient, EDg(B,A) ,EDg(C,A) and EDg(B,C) are all significantly differential, EDg ratio is between 0 and 1; Successful, EDg(B,A) and EDg(C,A) are significantly differential, while EDg(B,C) is not differential; Over, EDg(B,A) and EDg(B,C) are significantly differential, while EDg(C,A) is not limited by differential or not, EDg ratio is greater than 1. For the Inactive and Successful categories, genes with bottom and top 5 percentage ED ratios are removed to avoid the ambiguous categorization with Reversed, Insufficient or Over categories.

### Value

A list with components: a list of the five gene categories a list of the ED ratios for the five gene categories.

### Examples

    data(expr.filer)
    data(diffgene.genes)

    category = categorizeGene(expr = expr.filter,diffGene = diffgene.genes,
               from.sample="DMEC", to.sample="rEChMPP", target.sample="CB")
    cate.gene = category[[1]]
    cate.ratio = category[[2]]

[Package eegc version 1.18.0 Index]