Fisher.test {metaSeq} | R Documentation

## Fisher's combined probability method

### Description

Fisher's method combines multiple p-values which are calculated in each study.

### Usage

    Fisher.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

Fisher, R. A. (1932) Statistical Methods for Research Workers, 4th edition, Oliver and Boyd, London.

### See Also

`meta.readData`, `meta.oneside.noiseq`, `other.oneside.pvalues`

### 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]