Work

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

Year: 2026

Type: article

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.

Source: Genome biology

DOI: 10.1186/s13059-026-04161-4

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

Institutions: University of Tübingen, Karolinska Institutet, Tübingen AI Center, Ghent University, VIB.AI

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funders: National Institute of Mental Health, National Institute of Neurological Disorders and Stroke