Hi all,

after consulting the manual on data normalization, I have one question left to ask:

The way I see it, there are 4 ways described to obtain normalized data:

• The first one is to extract data, normalized using the normalization factors for a gene x sample matrix, and size factors for a single number per sample. This can be done using the following code:

  counts(dds, normalized=TRUE)

• The second way is to perform log2 transformation log2(n + 1), using the following function:

  normTransform(dds)

• The third and fourth way is to use the vst and rlog transformation, using the following functions respectively: vst(dds, blind=FALSE) rlog(dds, blind=FALSE)

When I just got started, I used the the first function (counts(dds, normalized=TRUE)), to obtain the normalized data, which I later used for clustering etc. . However, now I doubt that this was the correct decision and that the normalized data, obtained this way, is only used during the DE genes analysis and that for clustering, the second, third and fourth way of normalization is preferred.

I was hoping that any of you could share a more expert opinion on the what normalization to use and whether or not the "counts(dds, normalized=TRUE)" is a viable option as well.

Thank you a lot in advance.

Kind regards, Jonas

Take a look at the workflow (linked from the top of the vignette).

There we suggest to use transformations for anything involving a distance (also we say this in the DESeq2 paper). We give reasons for this suggestion and in the paper we evaluated alternatives.

My preferred transformation of the two we provide is VST, because it is fast.

In my opinion, the main role of normalization factors(or your first one) is for DE. you have to normized your count to deal with some sequence factor or biology factor before your do DE analysis. For the practical ways, you can extract norm count and show these to your collaborator, or you can plot single gene expression tendcy. But you can not just use these normalized counts to do some operation like Heatmap plot, Hierarchical clustering, k-means because of different orders of magnitude.

For the vst or rlog, the main role has been writen in the DESeq2 paper:

The results, shown in Additional file 1: Figure S17, revealed that when the size factors were equal for all samples, the Poisson distance and the Euclidean distance of rlog-transformed or VST counts outperformed other methods. However, when the size factors were not equal across samples, the rlog approach generally outperformed the other methods. Finally, we note that the rlog transformation provides normalized data, which can be used for a variety of applications, of which distance calculation is one.

When you do some operation based on distance calculation(maybe some machine learning application?), you can choose vst or rlog, even log(normCount + 1). For the practical ways, you can plot single gene expression tendcy. But I do not recommend it, because it have less biology meaning compared with normCount. And for PCA, clustering, or kmeans, it is more suitable compared with normCount.

But I also have a question. someone may use Z-scale of normCount to do kmeans or Heatmap plot. I am wodering what's the pros and cons of z-scale of normCount and vst or rlog ?

Hi,

I hope it is fine to add my question here. I did rlog and VST followed by PCA. The experimental design has two factors each with 3 levels each, and there are 45 samples. With rlog the PCA clustered 43 samples together and 2 samples were outliers. With the VST the PCA plot corresponds to the experimental design. My questions are what can I learn from this result about the data? and can I use this information to improve the differential analysis? I upload the PCA images below.

Thanks in advance,

Guy