Hi,
In LogNormalize function feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor prior to log transformation. My question is what does exactly multiplying by the scale factor do and why it is by default 10000?
Thank you
Hi Elyas,
To me, you get an ideal difference between your counts (especially low counts). If a gene is not expressed in a sample and lowly expressed in another, log2(counts) would give much higher log-ratios than log2(counts/10000), which would give many more significant genes among these low expressed.
Like RPKM in bulk RNA-seq data that you divide the count per million ( because you usually get millions of counts for bulk RNA). While with UMI single-cell data you get 1000s to 10,000s of counts (as LogNormalize usually uses for single-cell UMI data).
For instance, if you consider a given gene across 4 cells and each with 10,000 total counts, with 0, 1, 2, and 3 counts respectively. The below you can see the difference between data based on 1M and 10K:
Using log(1+counts/million) gives:
[0, 1, 2, 3] -> [0, 4.6, 5.3, 5.7]
Using log(1+counts/10k) gives:
[0, 1, 2, 3] -> [0, .7, 1.1, 1.4]
Thank you Soheil Yousefi for your prompt response. I see what you mentioned. What make me confused is that we should multiply the counts by the scale factor (10k by default) prior to log transformation not dividing them. This is the instruction in Seurat package in R for single cell clustering. This is the exact statement in the tutorial:
After removing unwanted cells from the dataset, the next step is to normalize the data. By default, we employ a global-scaling normalization method "LogNormalize" that normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result.
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