Hello. I am working with RNA seq data, and I am trying to do differential expression analysis. I am trying to make a PCA plot with Deseq2. Howevere although I have 12 samples, the PCA plot appears to have 11 dots. When I am making MDS plot with limma voom I see the same (11 dots instead of 12). I have checked dds object (and rlog transformed respectively) and its dimensions is 12. What could be the cause? Thank you!
dds <- DESeqDataSetFromMatrix(countData=countData, colData=colData, design=~group)
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
rld <- rlog(dds)
plot_pca<-plotPCA(rld, intgroup = c("group"))
plotPCA(rld, intgroup = c("group"))
plot_pca <- plot_pca + geom_text(aes_string(label = "name"), color = "black")
print(plot_pca)
thank you. I tried and it is 12. I thought about overlapping, but then I added the labels, and I thought that if there was overlapping it would be obvious in the labels (that was not the case). what can I do about overlapping? Thank you a lot!
This is my PCA plot. The clustering is not that good, but in differential expression analysis I can see differences in expression. Should I remove some samples? What do you think? Thank you!
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eridanus •
Yes you are right. Thank you for your response!
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This is my PCA plot. I checked and finally all the samples are included. I wonder if I should remove some samples from the differential expression analysis. WHat do you think?