How do I ensure that filtration of lowly expressed counts in RNA seq data was done correctly? my goal is to find DEGs using limma voom This is a plot of data before and after filtering by filterbyExpr ?

Code should be placed in three backticks as shown below

cpm <- cpm(dge)
lcpm <- cpm(dge, log=TRUE)
L <- mean(dge$samples$lib.size) * 1e-6
M <- median(dge$samples$lib.size) * 1e-6
c(L, M)
lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
nsamples <- ncol(dge$counts)
col <- brewer.spectral(nsamples)
par(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.55), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col=col[i], lwd=2)
}

##FilterbyExpr

keep.exprs <- filterByExpr(dge, group = dge$samples$group)
dge <- dge[keep.exprs,, keep.lib.sizes=FALSE]
dim(dge)

lcpm <- cpm(dge, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.5), las=2, main="", xlab="")
title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
  den <- density(lcpm[,i])
  lines(den$x, den$y, col=col[i], lwd=2)
}

Filtering is discussed in the limma documentation and workflow examples. As has been discussed previously on this forum, the success of the filtering can be seen in the voom plot.

Making a density plot of log-cpm values does not assess the filtering. limma does not make any assumptions about the shape of the log-cpm histogram.