Hi all, I'm curious to know how I can get and highlight the top 10% of the genes based on p-values that I get from my limma analysis in a volcano plot. I can get the genes highlighted based on an absolute logFC >2 and a p-value<0.01(code below) but I would like to have an idea of the number of genes in the top 10% based simply on p-values. Any help will be greatly appreciated. Thanks. -Avoks results$threshold = as.factor(abs(results$logFC) > 2 & results$P.Value < 0.01) windows() pdf("VolcanoPlot_GSE25724_9.pdf"); g = ggplot(data=results, aes(x=logFC, y=-log10(P.Value), colour=threshold)) + geom_point(alpha=0.4, size=1.75) + opts(legend.position = "none") + xlim(c(-8, 8)) + ylim(c(0, 10)) + xlab("log2 fold change") + ylab("-log10 p-value") g dev.off()

Hi Ovokeraye, if your current approach works fine, just change the first line would do, results$threshold = as.factor(results$P.Value<=quantile(results$P.Value, 0.1)) and btw, limma does provide a function volcanoplot to do exactly the same thing. HTH Dan On Mon, 2012-05-07 at 14:15 +0200, Ovokeraye Achinike-Oduaran wrote: > Hi all, > > I'm curious to know how I can get and highlight the top 10% of the > genes based on p-values that I get from my limma analysis in a volcano > plot. > > I can get the genes highlighted based on an absolute logFC >2 and a > p-value<0.01(code below) but I would like to have an idea of the > number of genes in the top 10% based simply on p-values. > > Any help will be greatly appreciated. > > Thanks. > > -Avoks > > results$threshold = as.factor(abs(results$logFC) > 2 & results$P.Value < 0.01) > windows() > pdf("VolcanoPlot_GSE25724_9.pdf"); > > g = ggplot(data=results, aes(x=logFC, y=-log10(P.Value), colour=threshold)) + > geom_point(alpha=0.4, size=1.75) + > opts(legend.position = "none") + > xlim(c(-8, 8)) + ylim(c(0, 10)) + > xlab("log2 fold change") + ylab("-log10 p-value") > g > dev.off() > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Thanks a bunch, Dan. Regards, Avoks. On 08 May 2012, at 1:32 PM, Dan Du <tooyoung at="" gmail.com=""> wrote: > hi Avoks, > > well, that sounds like a totally different plot. > To create such a cutoff benchmarking graph you have to make a new data > structure holding your possible p threshold and the corresponding no. of > significant genes. And this doesnot need ggplot and its advance syntax. > > x<-rnorm(100)^2/10 > cut<-c(0.5,0.1, 0.05,0.01,0.005,0.001,0.0005,0.0001) > plot(cut, sapply(cut, function(y) sum(x<=y)), ylab='No.SIG.DE.Gene', > main='Title') > > HTH, > Dan > > On Mon, 2012-05-07 at 14:55 +0200, Ovokeraye Achinike-Oduaran wrote: >> Thanks Dan. One more question: I want to plot the no. of genes vs >> p-value, so what goes into the aes portion of the command for the >> non-p.value axis? >> >> >> Thanks again. >> >> -Avoks >> >> On Mon, May 7, 2012 at 2:38 PM, Dan Du <tooyoung at="" gmail.com=""> wrote: >>> Hi Ovokeraye, >>> >>> if your current approach works fine, just change the first line would >>> do, >>> >>> results$threshold = as.factor(results$P.Value<=quantile(results$P.Value, >>> 0.1)) >>> >>> and btw, limma does provide a function volcanoplot to do exactly the >>> same thing. >>> >>> HTH >>> Dan >>> >>> On Mon, 2012-05-07 at 14:15 +0200, Ovokeraye Achinike-Oduaran wrote: >>>> Hi all, >>>> >>>> I'm curious to know how I can get and highlight the top 10% of the >>>> genes based on p-values that I get from my limma analysis in a volcano >>>> plot. >>>> >>>> I can get the genes highlighted based on an absolute logFC >2 and a >>>> p-value<0.01(code below) but I would like to have an idea of the >>>> number of genes in the top 10% based simply on p-values. >>>> >>>> Any help will be greatly appreciated. >>>> >>>> Thanks. >>>> >>>> -Avoks >>>> >>>> results$threshold = as.factor(abs(results$logFC) > 2 & results$P.Value < 0.01) >>>> windows() >>>> pdf("VolcanoPlot_GSE25724_9.pdf"); >>>> >>>> g = ggplot(data=results, aes(x=logFC, y=-log10(P.Value), colour=threshold)) + >>>> geom_point(alpha=0.4, size=1.75) + >>>> opts(legend.position = "none") + >>>> xlim(c(-8, 8)) + ylim(c(0, 10)) + >>>> xlab("log2 fold change") + ylab("-log10 p-value") >>>> g >>>> dev.off() >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> > >