diff rgedgeR/rgedgeRpaired.xml @ 16:cddf60746340 draft

Uploaded

--- a/rgedgeR/rgedgeRpaired.xml	Sat Jul 27 02:25:17 2013 -0400
+++ b/rgedgeR/rgedgeRpaired.xml	Sat Jul 27 04:19:51 2013 -0400
@@ -539,7 +539,7 @@
   outSmear = "edgeR_smearplot.pdf"
   outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
   smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
-  qqPlot(descr=paste(myTitle,'edgeR QQ plot'),pvector=DE\$table\$PValue,outpdf='edgeR_qqplot.pdf')
+  qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf')
   norm.factor = DGEList\$samples\$norm.factors
   topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
   edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
@@ -564,7 +564,7 @@
     rDESeq = as.data.frame(results(resDESeq))
     rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
     srDESeq = rDESeq[order(rDESeq\$pvalue),]
-    qqPlot(descr=paste(myTitle,'DESeq2 qqplot'),pvector=rDESeq\$pvalue,outpdf='DESeq2_qqplot.pdf')
+    qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
     cat("# DESeq top 50\n")
     print.noquote(srDESeq[1:50,])
     write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
@@ -619,7 +619,7 @@
       fit = lmFit(dat.voomed, mydesign)
       fit = eBayes(fit)
       rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
-      qqPlot(descr=paste(myTitle,'VOOM-limma QQ plot'),pvector=rvoom\$P.Value,outpdf='VOOM_qqplot.pdf')
+      qqPlot(descr=paste(myTitle,'VOOM-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf')
       rownames(rvoom) = rownames(workCM)
       rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
       srvoom = rvoom[order(rvoom\$P.Value),]
@@ -804,6 +804,45 @@
 suggested by the excellent documentation and vignettes for the Bioconductor
 packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
 
+**Note on Voom**
+
+The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
+
+This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
+
+voom is an acronym for mean-variance modelling at the observational level.
+The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
+Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
+This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
+The weights are then used in the linear modelling process to adjust for heteroscedasticity.
+
+In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
+The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
+The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
+Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
+Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
+This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
+
+
+Author(s)
+
+Charity Law and Gordon Smyth
+
+References
+
+Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
+
+Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
+Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
+http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
+
+See Also
+
+A voom case study is given in the edgeR User's Guide.
+
+vooma is a similar function but for microarrays instead of RNA-seq.
+
+
 ***old rant on changes to Bioconductor package variable names between versions***
 
 The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)