awfisher's Introduction

AWFisher

Github repository for adaptively weighted fisher's method (AW-Fisher)

Install This Package from github

In R console

library(devtools)
install_github("Caleb-Huo/AWFisher")

Citation

Huo, Z., Tang, S., Park, Y. and Tseng, G., 2020. P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher’s meta-analysis method in omics applications. Bioinformatics, 36(2), pp.524-532.
The manuscript can be found here: https://www.ncbi.nlm.nih.gov/pubmed/31359040

Full tutorial

Including a real data example using mouse metabolism data of three tissues
Perform transcriptomic meta-analysis and differential expression pattern detection

http://htmlpreview.github.io/?https://github.com/Caleb-Huo/AWFisher/blob/master/vignettes/AWFisher.html

Short tutorial

This short tutorial is about how to perform meta-analysis combining p-values from multiple studies.
Currently, only K=2, 3, ..., 100 (number of studies) are allowed in the R package.
The input should be a matrix. If not, please convert the data to a matrix (i.e., as.matrix()).

library(AWFisher)

K <- 50 ## combining K studies
G <- 10000 ## simulate G genes

set.seed(15213)
p.values = matrix(runif(K*G), ncol=K) ## This should be a matrix (if not, try as.matrix) instead of a dataframe.
res = AWFisher_pvalue(p.values)

hist(res$pvalues, breaks=40)

ks<-ks.test(res$pvalues, "punif", min=0, max=1, alternative = "two.sided"); ## KS test to test if the AW p-values are uniformly distributed under the null
ks

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