MetaQC (version 0.1.13)

MetaQC-package: 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

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)