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 (donkang75@gmail.com) and George Tseng (ctseng@pitt.edu)
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 >= 2.11.0 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)