I have miRNA data and I am analyzing it for differential expression using edgeR. The data consists of four different groups (A,B,C,D) that each consist of five replicates.

I have a few miRNAs, that turned out to be differentially expressed, and I would like to make a plot for each individually to show the expression.

This is the plot that I am currently generating:

Each point stands for one sample (replicate). The y axis displays the count of the miRNA in the sample and the x axis displays the group that the replicate belongs to. Currently, I am just plotting the counts from the dataDGE object that I have used throughout the analysis. (filterByExpr(), calcNormFactors and estimateDisp() have been "applied").

Do I need to apply cpm() to the counts? Is there a better plot type or option to display my results?

mirna = "mirna"
levels = c("A", "B", "C", "D")
plot_data <- data.frame(counts = strtoi(dataDGE$counts[mirna, ]), groups = factor(dataDGE$sample$group, levels = levels))
png(paste("name.pdf"))
p <- ggplot(plot_data) +
geom_point(aes(x = groups,y = counts))
print(p)
dev.off()

Disclaimer: This is not a plot of real data, I randomly inserted some points.

Yes, you should convert to logCPM before plotting. You might consider using the Glimma package, which is quite nice for that sort of thing.