Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.

Authors

Jan Lause, Christoph Ziegenhain, Leonard Hartmanis, Philipp Berens, Dmitry Kobak

Published in

Genome biology. Jun 27, 2026. Epub Jun 27, 2026.

Abstract

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI-based scRNA-seq data. To extend this approach to non-UMI data, we model the amplification step with a compound distribution: we assume that captured RNA molecules follow a negative binomial distribution and are replicated following an amplification distribution. This model leads to compound Pearson residuals, yielding meaningful gene selection and embeddings of Smart-seq2 datasets. Furthermore, we show that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

PMID:
42365288
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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