Hi,
I am wondering despite all the advantages SCT has over the conventional log-normalization method, is it necessary to use it with all datasets? I have 5 samples from mouse and the data is of high quality and small batch effect. I tested clustering with log-normalization and SCT (in seurat and RPCA integration) and actually the results from log-normalization looks more reasonable.
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sarahmanderni
No, your log-transform might indeed be ok (or even better) for your analysis of interest.
It's perfectly fine to use log-transformation. The advantages of these more complicated methods usually depend on context and also on the way people benchmark them. log-transformation is faster, perfectly reproducible and scales well to any dataset size. I always use it by default.
Thanks for the response! I also was wondering to ask if you have found the log-norm also working fine in terms of downstream analysis like differential expression analysis?
That is an entirely different analysis and depends on the testing Framework. If you use the commonly-used Wilcox test or something like limma-trend then yes, data on log2-scale could be used. If you use some (G)LM framework such as DESeq2 or limma-voom then it expects raw counts.
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To be honest, I really do not use SCT, and use log normalization all the time...