Description Usage Arguments Details Value Author(s) References See Also Examples
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
1 | intraAnalysisGene(data, group, splitSize = 5, metaMethod = addCLT)
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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]) |
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
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
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
, link{bilevelAnalysisClassic}
1 2 3 | data(GSE33223)
X <- intraAnalysisGene(data_GSE33223, group_GSE33223)
head(X)
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