# data visualization RNAseq : scaling data for PCA and cluster dendogram

I have count data from a RNAseq experiment (2 samples are from normal cells and 3 samples are cells with a disease), and the data is already standardized by trimmed mean of M values (TMM). I want to do some plots: biplot of Principal Component Analysis (PCA) and a cluster dendrogram to see if the samples normal/disease are well separated (there is a clear difference between them). Since the data is already standardized (TMM) should I scale and center the data prior to perform PCA and cluster dendrogram??

thank you!

• by TMM you meaning running limma or edgeR on it i supposed? Or obtaining the cpms? Oct 7 '20 at 12:58
• Using kallisto I get the TMM, and before looking for differentially expressed genes with edgeR I am doing PCA and cluster dendogram to look into the data.
– Mee
Oct 8 '20 at 8:19

• What TMM does is to "make different samples comparable" by adjusting for library size and more. It does not deal with individual genes. So yes, scaling would help when expression levels of different genes vary a lot (it is often the case). Since you are using edgeR, you can use the function cpm() to calculate log2 of CPM and then scale these before clustering for example.