# AWFisher Github repository for adaptively weighted fisher's method (AW-Fisher) ## Install This Package from github * In R console ```{R} 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. ```{R} library(AWFisher) K <- 50 ## combining K studies G <- 10000 ## simulate G genes set.seed(15213) p.values = matrix(runif(K*G), ncol=K) 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 ```