Stouffer.test {metaSeq} | R Documentation

## Stouffer's weighted Z-score method (Inverse normal method)

### Description

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`.

### Usage

    Stouffer.test(pvals, na.mode = "notignore")

### Arguments

`pvals` | A matrix coming from `meta.oneside.noiseq` function or `other.oneside.pvalues`, which is used for any one-sided p-values or probability.
`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".

### Author(s)

Koki Tsuyuzaki, Itoshi Nikaido

### References

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.

### Examples

    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)

[Package metaSeq version 1.30.0 Index]