intraAnalysisGene {BLMA} | R Documentation

## Intra-experiment analysis of an expression dataset at the gene-level

### Description

perform an intra-experiment analysis in conjunction with the moderated t-test (limma package) for the purpose of differential expression analysis of a gene expression dataset

### Usage

    intraAnalysisGene(data, group, splitSize = 5, metaMethod = addCLT)

### Arguments

`data` | a data frame where the rows are the gene IDs and the columns are the samples
`group` | sample grouping. The elements of group are either 'c' (control) or 'd' (disease). names(group) should be identical to colnames(data)
`splitSize` | the minimum number of disease samples in each split dataset. splitSize should be at least 3. By default, splitSize=5
`metaMethod` | the method used to combine p-values. This should be one of addCLT (additive method [1]), fishersMethod (Fisher's method [5]), stoufferMethod (Stouffer's method [6]), max (maxP method [7]), or min (minP method [8])

### Details

This function performs an intra-experiment analysis [1] for individual genes of the given dataset. The function first splits the dataset into smaller datasets, performs a moderated t-test (limma package) for the genes of the split datasets, and then combines the p-values for individual genes using metaMethod

### Value

A data frame (rownames are gene IDs) that consists of the following information:

- logFC: log foldchange (diseases versus controls)

- pLimma: p-value obtained from limma without spliting

- pLimma.fdr: FDR-corrected p-values of pLimma

- pIntra: p-value obtained from intra-experiment analysis

- pIntra.fdr: FDR-corrected p-values of pIntra

### Author(s)

Tin Nguyen and Sorin Draghici

### References

[1] T. Nguyen, R. Tagett, M. Donato, C. Mitrea, and S. Draghici. A novel bi-level meta-analysis approach – applied to biological pathway analysis. Bioinformatics, 32(3):409-416, 2016.

### See Also

`bilevelAnalysisGene`, `intraAnalysisClassic`, `link{bilevelAnalysisClassic}`

### Examples

    data(GSE33223)
    X <- intraAnalysisGene(data_GSE33223, group_GSE33223)
    head(X)

[Package BLMA version 1.4.0 Index]