Concerning: Seurat's normalization in analysis/kb_seurat_pp.rmd
Seurat has made updates over the years in their normalization methods. One new feature they build is: SCTransform. In this function they combine normalization, HVG selection and scaling + optional regression.
I was always used to using the "older" normalization from Seurat: log((count/scaling factor)*10.000)+1) in short, so with the addition of a pseudocount and taking the natural log. Which is also incorporated in the rmd here.
For the lab as a general method, it would be better to use the updated method for normalization (I discussed this with Simon as well).
Based on this paper, the newer normalization method they set-up at the same time as SCTransform, shows better results for scRNA-seq data.
Options:
Incorporating SCTransform: this will change some naming in the assays of the object as well. To check when using this: the default method in SCTransform is still log(p1) if I saw this correctly.
I think it might be worth a try to adjust the method used in normalizeData() of Seurat (instead of LogNormalize, the CLR for instance), and check if this does the same (as the SCTransform)? However, this needs more reading into the paper to understand what are the other differences between the methods, because probably there's more! Otherwise this might be an "easy" fix (we would change the normalization and the regression is already performed in scaling, however, I think there might still be a difference in HVG selection, this could be different in SCTransform, than if one performs this step separately).
Concerning: Seurat's normalization in analysis/kb_seurat_pp.rmd
Seurat has made updates over the years in their normalization methods. One new feature they build is: SCTransform. In this function they combine normalization, HVG selection and scaling + optional regression.
I was always used to using the "older" normalization from Seurat: log((count/scaling factor)*10.000)+1) in short, so with the addition of a pseudocount and taking the natural log. Which is also incorporated in the rmd here.
For the lab as a general method, it would be better to use the updated method for normalization (I discussed this with Simon as well).
Based on this paper, the newer normalization method they set-up at the same time as SCTransform, shows better results for scRNA-seq data.
Options:
Incorporating SCTransform: this will change some naming in the assays of the object as well. To check when using this: the default method in SCTransform is still log(p1) if I saw this correctly.
I think it might be worth a try to adjust the method used in
normalizeData()of Seurat (instead of LogNormalize, the CLR for instance), and check if this does the same (as the SCTransform)? However, this needs more reading into the paper to understand what are the other differences between the methods, because probably there's more! Otherwise this might be an "easy" fix (we would change the normalization and the regression is already performed in scaling, however, I think there might still be a difference in HVG selection, this could be different in SCTransform, than if one performs this step separately).