Normalization for sampled count data

2022/05/06 by A. Sina Booeshaghi, Ingileif B. Hallgrímsdóttir, Ángel Gálvez-Merchán +1 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · #Extracellular vesicles in disease #Immune cells in cancer #Single-cell and spatial transcriptomics

paper · pdf · doi:10.1101/2022.05.06.490859

openalex publication_date 2022/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14

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.

Citations

• Solving the chemical master equation for monomolecular reaction systems analytically
• Bayesian inference of gene expression states from single-cell RNA-seq data

Cited by

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Discussions

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