Fisher.test {metaSeq} | R Documentation |
Fisher's method combines multiple p-values which are calculated in each study.
Fisher.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
Fisher, R. A. (1932) Statistical Methods for Research Workers, 4th edition, Oliver and Boyd, London.
meta.readData
, meta.oneside.noiseq
, other.oneside.pvalues
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)