Description Details Author(s) References Examples
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.
Package: | MetaQC |
Type: | Package |
Version: | 0.1.14 |
Date: | 2013-02-20 |
License: | GPL-2 |
LazyLoad: | yes |
Don Kang (donkang75@gmail.com) and George Tseng (ctseng@pitt.edu)
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ## 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)
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