I am analyzing a single cell dataset from SMART-SEQ having 300 cells. I am following Seurat's guided tutorial for clustering. I am confused about how to normalize the data, and I am using NormalizeData() from Seurat.
However, I do not see much difference between raw and normalized counts. The distribution still appears to be bi-modal; how should I proceed with the normalization? I there a workaround through which I can evaluate the normalized counts? It is a follow-up question from the previous post
Here's how the data looks,
1 answer
Likely because count normalization has nothing to do with the plotted metric - it's just the number of RNA molecules detected per cell. Count normalization isn't going to change that value.
See this answer for more info.
You could compute some of the metrics used in this pre-print and compare them between multiple normalization methods:
https://www.biorxiv.org/content/10.1101/2022.05.06.490859v1.full
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It looks like you plotted the same thing 4 times with different labels. In particular, your raw counts here looks very different than in the previous question (i would expect to see some counts near 0 and no counts near 10^6)