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You should copy the two *.pdf files that were created back to your Desktop to view them.
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Exercises
- What are the numbers returned by
sizeFactors( cds )?Expand Answer... Answer... They are roughly speaking the relative average coverage of each data set? There are roughly 5 times as many counts of reads in genes for wt2 as there are for mut2. Specifically, they are the size parameter of the negative binomial fit to the counts per gene per data file.
- What are the dispersion estimates?
Expand Answer... Answer... The model assumes there is also a per-gene aspect to the variance in counts observed, that is again fit to a negative binomial distribution (=overdispersed Poisson distribution). The program fits a model where the lower the counts are the more dispersion is expected (red line in graph), and thus the less significant a change in counts becomes.
- What was the predominant effect of the mutation on gene expression in this Listeria strain?
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These commands use the negative binomial model, calculate the false discovery rate (FDR ~ adjusted p-value), and make a plot similar to the one from DEseqDESeq.
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login1$ R
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> library("edgeR")
> counts = read.delim("gene_counts.tab", header=F, row.names=1)
> colnames(counts) = c("wt1", "mut1", "wt2", "mut2")
> head(counts)
> group <- factor(c("wt","mut","wt","mut"))
> dge = DGEList(counts=counts,group=group)
> dge <- estimateCommonDisp(dge)
> dge <- estimateTagwiseDisp(dge)
> et <- exactTest(dge)
> etp <- topTags(et, n=100000)
> etp$table$logFC = -etp$table$logFC
> pdf("edgeR-MA-plot.pdf")
> plot(
etp$table$logCPM,
-etp$table$logFC,
xlim=c(-3, 20), ylim=c(-12, 12), pch=20, cex=.3,
col = ifelse( etp$table$FDR < .1, "red", "black" ) )
> dev.off()
> write.csv(etp$table, "DESeq-wt-vs-mut.csv")
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Note that the "FC" fold change is reversed calculated is initially the reverse of that for the DESeq example for the output here. It is wt relative to mut. (WHich is why To fix this, we put a negative in the graphing function.)there for the log fold change.
Exercises
- Compare the expression changes predicted by DESeq and edgeR to each other.
- Does edgeR or DESeq predict more significant changes?
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