## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(stringsAsFactors = FALSE) ## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("AWFisher") ## ----------------------------------------------------------------------------- library(AWFisher) # Include the AWFisher package # load the data data(data_mouseMetabolism) # Verify gene names match across three tissues all(rownames(data_mouseMetabolism$brown) == rownames(data_mouseMetabolism$heart)) all(rownames(data_mouseMetabolism$brown) == rownames(data_mouseMetabolism$liver)) dataExp <- data_mouseMetabolism # Check the dimension of the three studies sapply(dataExp, dim) # Check the head of the three studies sapply(dataExp, function(x) head(x,n=2)) # Before performing differential expression analysis for each of these three tissues. # Create an empty matrix to store p-value. # Each row represents a gene and each column represent a study/tissue. pmatrix <- matrix(0,nrow=nrow(dataExp[[1]]),ncol=length(dataExp)) rownames(pmatrix) <- rownames(dataExp[[1]]) colnames(pmatrix) <- names(dataExp) ## ----------------------------------------------------------------------------- library(limma) # Include the limma package to perform differential expression analyses for the microarray data for(s in 1:length(dataExp)){ adata <- dataExp[[s]] ControlLabel = grep('wt',colnames(adata)) caseLabel = grep('LCAD',colnames(adata)) label <- rep(NA, ncol(adata)) label[ControlLabel] = 0 label[caseLabel] = 1 design = model.matrix(~label) # design matrix fit <- lmFit(adata,design) # fit limma model fit <- eBayes(fit) pmatrix[,s] <- fit$p.value[,2] } head(pmatrix, n=2) ## look at the head of the p-value matrix ## ----------------------------------------------------------------------------- res <- AWFisher_pvalue(pmatrix) ## Perform AW Fisehr meta analysis qvalue <- p.adjust(res$pvalue, "BH") ## Perform BH correction to control for multiple comparison. sum(qvalue < 0.05) ## Differentially expressed genes with FDR 5% head(res$weights) ## Show the AW weight of the first few genes ## ----------------------------------------------------------------------------- ## prepare the data to feed function biomarkerCategorization studies <- NULL for(s in 1:length(dataExp)){ adata <- dataExp[[s]] ControlLabel = grep('wt',colnames(adata)) caseLabel = grep('LCAD',colnames(adata)) label <- rep(NA, ncol(adata)) label[ControlLabel] = 0 label[caseLabel] = 1 studies[[s]] <- list(data=adata, label=label) } ## See help file about about how to use function biomarkerCategorization. ## Set B = 1,000 (at least) for real data application ## You may need to wrap up a function (i.e., function_limma) ## to perform differential expression analysis for each study. set.seed(15213) result <- biomarkerCategorization(studies,function_limma,B=100,DEindex=NULL) sum(result$DEindex) ## print out DE index at FDR 5% head(result$varibility, n=2) ## print out the head of variability index print(result$dissimilarity[1:4,1:4]) ## print out the dissimilarity matrix ## ----------------------------------------------------------------------------- library(tightClust) ## load tightClust package tightClustResult <- tight.clust(result$dissimilarity, target=4, k.min=15, random.seed=15213) clusterMembership <- tightClustResult$cluster ## ----fig.show='hold'---------------------------------------------------------- for(s in 1:length(dataExp)){ adata <- dataExp[[s]] aname <- names(dataExp)[s] bdata <- adata[qvalue<0.05, ][tightClustResult$cluster == 1 ,] cdata <- as.matrix(bdata) ddata <- t(scale(t(cdata))) # standardize the data such that for each gene, the mean is 0 and sd is 1. ColSideColors <- rep("black", ncol(adata)) ColSideColors[grep('LCAD',colnames(adata))] <- "red" B <- 16 redGreenColor <- rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1)) heatmap(ddata,Rowv=NA,ColSideColors=ColSideColors,col= redGreenColor ,scale='none',Colv=NA, main=aname) } ## ----------------------------------------------------------------------------- sessionInfo()