bilevelAnalysisGene: Bi-level meta-analysis of multiple expression datasets at the...
In nguyentin/BLMA: BLMA: A package for bi-level meta-analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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])

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

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

Tin Nguyen and Sorin Draghici

[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.

bilevelAnalysisGene, intraAnalysisClassic

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)