comparison rgedgeR/rgedgeRpaired.xml @ 16:cddf60746340 draft

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15:993d35bcf98c

  outpdfname="edgeR_heatmap.pdf"
  hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle)
  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')
  norm.factor = DGEList\$samples\$norm.factors
  topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
  edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
  edgeRcounts = rep(0, length(allgenes))
  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits

    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
    resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
    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')
    cat("# DESeq top 50\n")
    print.noquote(srDESeq[1:50,])
    write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))

      dev.off()
      # Use limma to fit data
      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')
      rownames(rvoom) = rownames(workCM)
      rvoom = cbind(rvoom,NReads=cmrowsums,URL=contigurls)
      srvoom = rvoom[order(rvoom\$P.Value),]
      cat("# VOOM top 50\n")
      print(srvoom[1:50,])

Some helpful plots and analysis results. Note that most of these are produced using R code 
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.

***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) 
breaking this and all other code that assumed the old name for this variable, 
between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing). 

16:cddf60746340

  outpdfname="edgeR_heatmap.pdf"
  hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle)
  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 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))
  edgeRcounts = rep(0, length(allgenes))
  edgeRcounts[edgeRcountsindex] = 1  # Create venn diagram of hits

    deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
    resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
    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 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)
    topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
    DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))

      dev.off()
      # Use limma to fit data
      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 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),]
      cat("# VOOM top 50\n")
      print(srvoom[1:50,])

Some helpful plots and analysis results. Note that most of these are produced using R code 
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) 
breaking this and all other code that assumed the old name for this variable, 
between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).