Depth normalization for single-cell genomics count data

Booeshaghi, A. S.; Hallgrimsdottir, I. B.; Galvez-Merchan, A.; Pachter, L.

doi:10.1101/2022.05.06.490859

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bioRxiv · 10.1101/2022.05.06.490859

Depth normalization for single-cell genomics count data

Booeshaghi, A. S.·Hallgrimsdottir, I. B.·Galvez-Merchan, A.·Pachter, L.

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Abstract

Genomics data analysis requires normalization of feature counts that stabilizes technical variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an optimal variance stabilizing transform for negative binomial count data followed by a proportional fitting step (PFlog) 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. We further show that both the shifted-log scale and centered-log ratio geometry are important for preserving PCA and k-NN structure.

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Booeshaghi, A. S., Hallgrimsdottir, I. B., Galvez-Merchan, A., Pachter, L.. 2022-05-06. Depth normalization for single-cell genomics count data. https://doi.org/10.1101/2022.05.06.490859

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