Stouffer.test {metaSeq} | R Documentation |
Stouffer's method combines multiple weighted Z-scores which are calculated in each study. Although many weight can be introduced but weighting by sample-size is used in meta.oneside.noiseq
.
Stouffer.test(pvals, na.mode = "notignore")
pvals |
A matrix coming from |
na.mode |
A string indicating how to treat NA in pvals. "notignore" means that genes having at least one NA is regarded as NA. "ignore" means NA is ignored and remaining data is used. By default, na.mode = "notignore". |
Koki Tsuyuzaki, Itoshi Nikaido
Stouffer, S. A. and Suchman, E. A. and DeVinney, L. C. and Star, S. A. and Williams, R. M. Jr. (1949) The American Soldier, Vol. 1 - Adjustment during Army Life. Princeton, Princeton University Press.
data(BreastCancer) library("snow") # Experimental condition (1: BreastCancer, 0: Normal) flag1 <- c(1,1,1,0,0, 1,0, 1,1,1,1,1,1,1,0, 1,1,0) # Source of data flag2 <- c("A","A","A","A","A", "B","B", "C","C","C","C","C","C","C","C", "D","D","D") # readData function for meta-analysis cds <- meta.readData(data = BreastCancer, factor = flag1, studies = flag2) # oneside NOISeq for meta-analysis # cl <- makeCluster(4, "SOCK") # result <- meta.oneside.noiseq(cds, k = 0.5, norm = "tmm", replicates = "biological", factor = flag1, conditions = c(1, 0), studies = flag2, cl = cl) # stopCluster(cl) # Script above is very time-consumming step. Please use this pre-calculated result instead data(Result.Meta) result <- Result.Meta # Fisher's method (without weighting) F <- Fisher.test(result) str(F) # Stouffer's method (with weighting by sample-size) S <- Stouffer.test(result) str(S)