17 months ago by
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
You should always filter out genes that have consistently very low counts, and the guidelines for voom are the same as for edgeR. You could for example use:
keep <- filterByExpr(dge, design)
dge <- dge[keep,,keep.lib.size=FALSE]
dge <- calcNormFactors(dge)
There is no change to this even if you wish to find genes that have very low expression in one group, although you could try reducing the filter thresholds a little if you want to live dangerously.
You want to find genes that are down-regulated in individual tumors relative to normal. Let's assume that the factor "Tumor" takes values "Normal", "Tumor1", "Tumor2" etc. All the normal samples should have the same name but each distinct tumor should have a different name.
Tumor <- relevel(Tumor, ref="Normal")
design <- model.matrix(~Tumor)
Now we can just do a regular voom analysis:
v <- voom(dge,design)
fit <- lmFit(v,design)
fit <- eBayes(fit, robust=TRUE)
The usual limma tests will tell you which genes are down in which tumors. For example:
will show you which genes are down-regulated in Tumor1 relative to normal.
You don't need to compute z-scores explicitly. A z-score < -2 corresponds to a p-value of 0.0455:
To find genes with zscore < -2 in individual tumors, you can simply use:
Low <- (fit$p.value[,-1] < 2*pnorm(-2)) & (fit$coef[,-1] < 0)
This will give you a matrix of genes by tumors, which an entry of TRUE if z-score < -2 and FALSE if z-score > -2.
Note the use of "[,-1]" in the previous code line, which simply gets rid of the intercept term.