[BioC] SmearPlot edgeR - adding gene names to genes of interest

[Aliaksei Holik]

Just spotted a typo. It should have said:
gene.labels <- x.top$table[x.top$table$Symbols %in% ids,]

On 16/12/13 9:26 PM, Aliaksei Holik wrote:
> Hi Christine,
>
> There's probably a more elegant way, but that's how I do it.
>
> You should be able to use R's 'text' function by taking logCPM values of
> the genes you're interested in as 'x' argument, logFC values as 'y'
> argument, and the gene names or ID's as 'labels' argument.
>
> Say top.x is your resulting "TopTags" object and it contains dataframe
> called 'table', which contains among others the following columns:
> Symbols - logFC - logCPM
>
> You should be able to subset it to the genes you're interested in by:
>
> ids <- c("...", "...")
> gene.labels <- x$table[x$table$Symbols %in% ids,]
>
> Where 'ids' is a vector of gene symbols for genes of interest, but you
> can use Entrez Gene IDs or any other thing you can think of, for
> instance all genes with logFC > 1 etc.
>
> After you've plotted your smear plot from DGELRT or DGEExact object you
> can mark the genes of interest with the following:
>
> text(x=gene.labels$logCPM,
>       y=gene.labels$logFC,
>       labels=gene.labels$Symbols, cex=0.7, pos=1)
>
> Note that this will not work if you're plotting the SmearPlot from
> DGEList as your TopTags object will contain squeezed logCPM values
> different from those in DGEList.
>
> Hope it helps,
>
> Aliaksei.
>
> On 16/12/13 7:07 PM, Christine Gläßer wrote:
>> Dear all,
>>
>> I have used edgeR for analyzing differential expression. I also created
>> a smear plot visualizing DGE results (code see below). I'd now like to
>> highlight some genes of interest (they are significantly diff.
>> expressed) in this plot by labeling them; do you know if there's a way
>> to do so?
>>
>> Kind regards, and many thanks in advance,
>>
>> Christine Gläßer
>>
>>
>> ###### Code ########
>>
>> counts <- as.matrix(read.table(infile, header = TRUE, sep = "\t",
>> row.names = 1, as.is = TRUE))
>> cpms<-cpm(counts)
>> keep <- rowSums(cpms>1)>=4  ### at least 4 replicates
>> counts <- counts[keep,]
>>
>> group <- factor(c(1,1,1,1,1,2,2,2,2,3,3,3,3))
>>
>> d <- DGEList(counts=counts, group=group)
>> d <- calcNormFactors(d)
>>
>> design <- model.matrix(~ 0+group)
>> colnames(design) <- cols
>>
>> contrasts <- makeContrasts(con1 = CONTRAST1, con2 =  CONTRAST2,
>> levels=design)
>>
>> y <- estimateGLMCommonDisp(d,design)
>> y <- estimateGLMTrendedDisp(y,design)
>> y <- estimateGLMTagwiseDisp(y,design)
>> fit <- glmFit(y,design)
>>
>> for (i in colnames(contrasts))
>> {
>>    print (i)
>>
>>    outfilename <- paste(paste(outdir, i, sep="/"))
>>    outfilename <- paste(paste(outfilename, ".txt", sep=""))
>>    outfilesmearplot <- paste(paste(outdir, i, sep="/"))
>>    outfilesmearplot <- paste(paste(outfilesmearplot, "_smearplot.pdf",
>> sep=""))
>>
>>    lrt <- glmLRT(fit, contrast=contrasts[,i])
>>    tt <- topTags(lrt, n=nrow(d), adjust.method="BH")
>>    rn <- rownames(tt$table)
>>    deg <- rn[tt$table$FDR < 0.05]
>>
>>    pdf(outfilesmearplot)
>>   ###### SMEAR PLOT STARTS HERE #######
>>    plotSmear(d, de.tags=deg)
>>    abline(h = c(-1, 1), col = "dodgerblue")
>>    dev.off()
>>    write.table(tt$table, file=outfilename, sep="\t")
>> }
>>
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>>
>
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