Stouffer’s meta-analysis with weight and direction effect in R

I often have to perform meta-analysis of past experiments for which the only info I have is the fold change and the p-value (of any measure you may imagine: species richness, gene expression, depth of coverage, plates of carbonara eaten in 5 minutes, everything).The hardest thing to find out for me was how to take the direction of the changes into account. E.g. if monday I eat 10 carbonara more than my brother (p=0.01), on tuesday 10 more (p=0.01), on wednesday 5 more (p=0.1), on thursday 10 less (p=0.01), on friday ten less (p=0.01) and on saturday 5 less (p=0.1), a standard Stouffer meta-analysis would return a highly significant p-value, completely disregarding the fact that the significant changes were half in my favor, and half in favor of my brother.How can I take into account the information on the direction?I…
Original Post: Stouffer’s meta-analysis with weight and direction effect in R

Visualize KEGG pathway and fold enrichment

As a useful note to self, I paste here an easy example on the use of the pathview package by Weijun Luo to plot the log fold change of gene expression across a given KEGG pathway. This example is on Vitis vinifera (as the prefix vvi can suggest), but the approach is general.Basically, you just need to feed pathview the pathway argument and a gene.data argument. In this case, gene.data is a named vector, with names being the (entrez) gene names, and the value is the log Fold Change.I selected a pathway for which KEGG has a nice representation, you might not be so lucky! library(pathview) mypathway<-“vvi03060” genes<-c(“100241050″,”100243802″,”100244217″,”100244265″,”100247624″,”100247887″,”100248517″,” 100248990″,”100250268″,”100250385″,”100250458″,”100251379″,”100252350″,”100252527″,”100252725″,” 100252902″,”100253826″,”100254350″,”100254429″,”100254996″,”100255515″,”100256046″,”100256113″,” 100256412″,”100256941″,”100257568″,”100257730″,”100258179″,”100258854″,”100259285″,”100259443″,” 100260422″,”100260431″,”100261219″,”100262919″,”100263033″,”100264739″,”100265371″,”100266802″,” 100267343″,”100267692″,”100852861″,”100853033″,”100854110″,”100854416″,”100854647″,”100855182″,” 104879783″,”109122671″) logFC<-rnorm(length(genes),-0.5,1) names(logFC)<-genes pathview(gene.data=logFC,species=”vvi”,pathway=mypathway) The result should be something like this: Related R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data…
Original Post: Visualize KEGG pathway and fold enrichment