Hi all,
Perhaps a silly question but the default normalization method in Seurat is "LogNormalize". Instead of automatically running the code, I would like to understand what is happening when I perform the normalization and scaling steps in Seurat. And how exactly does this differ from z score normalization? How are the expression levels in e.g. the feature plots expressed?
Please take into consideration that I am not comfortable with statistics, so do not hesitate to try to give a simple and elaborate explanation!
Many thanks in advance, Florencia
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written
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f.morgana •
1 answer
LogNormalize scales the data by library size so it eliminates the confounding effect of different read depth across the cells and then applies log2 transformation.
Z-scoring (see Wikipedia article) is a metric that indicates how much a value deviates from the mean of many samples. In the (sc)RNA-seq context that would be how much the read count of one cell for a given gene deviates from the mean of the counts of all cells for that gene. Z-scoring is typically performed based on the log-normalized counts.
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