MetaQC-package: MetaQC: Objective Quality Control and Inclusion/Exclusion...

In MetaQC: MetaQC: Objective Quality Control and Inclusion/Exclusion Criteria for Genomic Meta-Analysis

Description

MetaQC implements our proposed quantitative quality control measures: (1) internal homogeneity of co-expression structure among studies (internal quality control; IQC); (2) external consistency of co-expression structure correlating with pathway database (external quality control; EQC); (3) accuracy of differentially expressed gene detection (accuracy quality control; AQCg) or pathway identification (AQCp); (4) consistency of differential expression ranking in genes (consistency quality control; CQCg) or pathways (CQCp). (See the reference for detailed explanation.) For each quality control index, the p-values from statistical hypothesis testing are minus log transformed and PCA biplots were applied to assist visualization and decision. Results generate systematic suggestions to exclude problematic studies in microarray meta-analysis and potentially can be extended to GWAS or other types of genomic meta-analysis. The identified problematic studies can be scrutinized to identify technical and biological causes (e.g. sample size, platform, tissue collection, preprocessing etc) of their bad quality or irreproducibility for final inclusion/exclusion decision.

Details

Package:	MetaQC
Type:	Package
Version:	0.1.13
Date:	2012-12-21
License:	GPL-2
LazyLoad:	yes

Author(s)

Don Kang ([e-mail]) and George Tseng ([e-mail])

References

Dongwan D. Kang, Etienne Sibille, Naftali Kaminski, and George C. Tseng. (Nucleic Acids Res. 2012) MetaQC: Objective Quality Control and Inclusion/Exclusion Criteria for Genomic Meta-Analysis.

Examples

## Not run: 
    requireAll(c("proto", "foreach"))

   ## Toy Example
    data(brain) #already hugely filtered
    #Two default gmt files are automatically downloaded, 
	#otherwise it is required to locate it correctly.
    #Refer to http://www.broadinstitute.org/gsea/downloads.jsp
    brainQC <- MetaQC(brain, "c2.cp.biocarta.v3.0.symbols.gmt", 
						filterGenes=FALSE, verbose=TRUE)
	#B is recommended to be >= 1e4 in real application					
    runQC(brainQC, B=1e2, fileForCQCp="c2.all.v3.0.symbols.gmt")
    brainQC
    plot(brainQC)

    ## For parallel computation with only 2 cores
	## R >= 2.11.0 in windows to use parallel computing
    brainQC <- MetaQC(brain, "c2.cp.biocarta.v3.0.symbols.gmt", 
			filterGenes=FALSE, verbose=TRUE, isParallel=TRUE, nCores=2)
    #B is recommended to be >= 1e4 in real application
    runQC(brainQC, B=1e2, fileForCQCp="c2.all.v3.0.symbols.gmt")
    plot(brainQC)

    ## For parallel computation with all cores
	## In windows, only 2 cores are used if not specified explicitly
    brainQC <- MetaQC(brain, "c2.cp.biocarta.v3.0.symbols.gmt", 
			filterGenes=FALSE, verbose=TRUE, isParallel=TRUE)
	#B is recommended to be >= 1e4 in real application					
    runQC(brainQC, B=1e2, fileForCQCp="c2.all.v3.0.symbols.gmt")
    plot(brainQC)

	## Real Example which is used in the paper
	#download the brainFull file 
	#from https://github.com/downloads/donkang75/MetaQC/brainFull.rda
	load("brainFull.rda")
    brainQC <- MetaQC(brainFull, "c2.cp.biocarta.v3.0.symbols.gmt", filterGenes=TRUE, 
			verbose=TRUE, isParallel=TRUE)
    runQC(brainQC, B=1e4, fileForCQCp="c2.all.v3.0.symbols.gmt") #B was 1e5 in the paper
    plot(brainQC)

	## Survival Data Example
	#download Breast data 
	#from https://github.com/downloads/donkang75/MetaQC/Breast.rda
	load("Breast.rda")
    breastQC <- MetaQC(Breast, "c2.cp.biocarta.v3.0.symbols.gmt", filterGenes=FALSE, 
			verbose=TRUE, isParallel=TRUE, resp.type="Survival")
    runQC(breastQC, B=1e4, fileForCQCp="c2.all.v3.0.symbols.gmt") 
    breastQC
    plot(breastQC)

## End(Not run)

MetaQC documentation built on May 30, 2017, 2:38 a.m.