bilevelAnalysisGene {BLMA} | R Documentation

## Bi-level meta-analysis of multiple expression datasets at the gene-level

### Description

Perform a bi-level meta-analysis in conjunction with the moderate t-test (limma package) for the purpose of differential expression analysis of multiple gene expression datasets

### Usage

    bilevelAnalysisGene(dataList, groupList, splitSize = 5, metaMethod = addCLT)

### Arguments

`dataList` | a list of datasets. Each dataset is a data frame where the rows are the gene IDs and the columns are the samples
`groupList` | a list of vectors. Each vector represents the phenotypes of the corresponding dataset in dataList, which are either 'c' (control) or 'd' (disease).
`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

The bi-level framework combines the datasets at two levels: an intra- experiment analysis, and an inter-experiment analysis [1]. At the intra-experiment analysis, the framework splits a dataset into smaller datasets, performs a moderated t-test (limma package) on split datasets, and then combines p-values of individual genes using metaMethod. At the inter-experiment analysis, the p-values obtained for each individual datasets are combined using metaMethod

### Value

A data frame containing the following components:

- rownames: gene IDs that are common in all datasets

- pLimma: the p-values obtained by combining pLimma values of individual datasets

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

- pBilevel: the p-values obtained from combining pIntra values of individual datasets

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

### 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`

### Examples

    dataSets <- c("GSE17054", "GSE57194", "GSE33223", "GSE42140")
    data(list=dataSets, package="BLMA")
    names(dataSets) <- dataSets
    dataList <- lapply(dataSets, function(dataset) get(paste0("data_", dataset)))
    groupList <- lapply(dataSets, function(dataset) get(paste0("group_", dataset)))
    Z <- bilevelAnalysisGene(dataList = dataList, groupList = groupList)
    head(Z)

[Package BLMA version 1.18.0 Index]