bilevelAnalysisGene: Bi-level meta-analysis of multiple expression datasets at the...

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

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

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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:

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

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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)

BLMA documentation built on Nov. 8, 2020, 8:15 p.m.