runQC: Command to execute quality control procedures.

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

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.

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.

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

Run this code

## 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)