runQC: Command to execute quality control procedures.

In donkang75/MetaQC: MetaQC: Objective Quality Control and Inclusion/Exclusion Criteria for Genomic Meta-Analysis

Description

It it a utility function to RunQC method in MetaQC object.

Usage

runQC(QC, nPath=NULL, B=1e4, pvalCut=.05, 
	pvalAdjust=FALSE, fileForCQCp="c2.all.v3.0.symbols.gmt")

Arguments

QC	A proto R object which obtained by MetaQC function.
nPath	The number of top pathways which would be used for EQC calculation. The top pathways are automatically determined by their mean rank of over significance among given studies. It is important that gene sets used for EQC are expected to have higher correlation than background. For better performance, this should be set as a reasonably small number.
B	The number of permutation tests used for EQC calculation. More than 1e4 is recommended.
pvalCut	P-value threshold used for AQC calculation.
pvalAdjust	Whether to apply p-value adjustment due to multiple testing (B-H procedure is used).
fileForCQCp	Gene set used for CQCp calculation. Usually larger gene set is used than EQC calculation.

Value

A data frame showing a summary of each quality control score.

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.

See Also

MetaQC

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 >= 2.14.0 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 half cores
	## In windows, only 3 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)

donkang75/MetaQC documentation built on May 15, 2019, 10:41 a.m.