bilevelAnalysisPathway: Bi-level meta-analysis - applied to pathway analysis

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

Description

Perform a bi-level meta-analysis conjunction with Impact Analysis to integrate multiple gene expression datasets

Usage

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bilevelAnalysisPathway(kpg, kpn, dataList, groupList, splitSize = 5,
  metaMethod = addCLT, pCutoff = 0.05, percent = 0.05, mc.cores = 1,
  nboot = 200, seed = 1)

Arguments

kpg

list of pathway graphs as objects of type graph (e.g., graphNEL)

kpn

names of the pathways.

dataList

a list of datasets to be combined. 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]), fisherMethod (Fisher's method [5]), stoufferMethod (Stouffer's method [6]), max (maxP method [7]), or min (minP method [8])

pCutoff

cutoff p-value used to identify differentially expressed (DE) genes. This parameter is used only when the enrichment method is "ORA". By default, pCutoff=0.05 (five percent)

percent

percentage of genes with highest foldchange to be considered as differentially expressed (DE). This parameter is used when the enrichment method is "ORA". By default percent=0.05 (five percent). Please note that only genes with p-value less than pCutoff will be considered

mc.cores

the number of cores to be used in parallel computing. By default, mc.cores=1

nboot

number of bootstrap iterations. By default, nboot=200

seed

seed. By default, seed=1.

Details

The bi-level framework combines the datasets at two levels: an intra-experiment analysis, and an inter-experiment analysis [1]. At the intra-level analysis, the framework splits a dataset into smaller datasets, performs pathway analysis for each split dataset using Impact Analysis [2,3], and then combines the results of these split datasets using metaMethod. At the inter-level analysis, the results obtained for individual datasets are combined using metaMethod

Value

A data frame (rownames are geneset/pathway IDs) that consists of the following information:

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.

[2] A. L. Tarca, S. Draghici, P. Khatri, S. S. Hassan, P. Mittal, J.-s. Kim, C. J. Kim, J. P. Kusanovic, and R. Romero. A novel signaling pathway impact analysis. Bioinformatics, 25(1):75-82, 2009.

[3] S. Draghici, P. Khatri, A. L. Tarca, K. Amin, A. Done, C. Voichita, C. Georgescu, and R. Romero. A systems biology approach for pathway level analysis. Genome Research, 17(10):1537-1545, 2007.

[4] R. A. Fisher. Statistical methods for research workers. Oliver & Boyd, Edinburgh, 1925.

[5] S. Stouffer, E. Suchman, L. DeVinney, S. Star, and J. Williams, RM. The American Soldier: Adjustment during army life, volume 1. Princeton University Press, Princeton, 1949.

[6] L. H. C. Tippett. The methods of statistics. The Methods of Statistics, 1931.

[7] B. Wilkinson. A statistical consideration in psychological research. Psychological Bulletin, 48(2):156, 1951.

See Also

bilevelAnalysisGeneset, pe, phyper

Examples

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# load KEGG pathways
x <- loadKEGGPathways()  

# load example data
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)))

IAComb <- bilevelAnalysisPathway(x$kpg, x$kpn, dataList, groupList)
head(IAComb[, c("Name", "pBLMA", "pBLMA.fdr", "rBLMA")])

nguyentin/BLMA documentation built on May 23, 2019, 4:43 p.m.