Normalize expression matrix for total library size and then scale by a custom scalefactor.
This method does not work well when any cells/samples have a library size of 0, so filtering prior to this is recommended.
$$\LARGE x'_{i,j} = \frac{x_{i,j}}{\sum_{i} x_{i,j}} \times k $$ Where:
(\(x_{i,j}\)) is the raw count for feature \(i\) in sample \(j\)
(\(x'_{i,j}\)) is the library normalized and scaled expression value for feature \(i\) in sample \(j\)
(k) is a scalefactor applied after normalization
scalefactor | numeric (default = 6000). Scalefactor to use after library size normalization. Expressed as k in the above equation |
Other normalization parameters:
norm_arcsinh,
norm_default,
norm_l2,
norm_log,
norm_osmfish,
norm_pearson,
norm_quantile,
norm_tfidf