Depth normalization for single-cell genomics count data
Booeshaghi, A. S.; Hallgrimsdottir, I. B.; Galvez-Merchan, A.; Pachter, L.
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Single-cell genomics analysis requires normalization of feature counts that stabilizes variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an (additive in the raw counts) proportional fitting step followed by the logarithm and then another (multiplicative in the raw counts) proportional fitting step (PFlogPF) is the only feature-relabeling-equivariant method satisfying the three desiderata. We demonstrate superior performance of this method, which is equivalent to a shifted centered-log ratio transform, in comparison to other normalizations on numerous benchmarks across hundreds of single-cell RNA-seq datasets.
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