Compound models and Pearson residuals for single-cell RNA-seq data without UMIs
Jan Lause, Christoph Ziegenhain, Leonard Hartmanis, Philipp Berens, Dmitry Kobak · 2026
Read Compound models and Pearson residuals for single-cell RNA-seq data without UMIs by Jan Lause, Christoph Ziegenhain, Leonard Hartmanis, Philipp Berens, Dmitry Kobak (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13595543.txt.
Description
Jan Lause and colleagues fix a statistical gap in single-cell RNA sequencing. The best normalization methods assumed data collected with molecular barcodes. That left richer full-length protocols such as Smart-seq2 without a principled option. Their 2026 paper models what actually happens to barcode-free data, where each captured molecule is amplified into many copies before counting.
Their compound model treats molecule capture as one random process and amplification as a second layered on top, and from it they derive corrected residuals for every gene in every cell. Amplification behavior across several protocols follows a broken power law, a pattern the model captures. The residuals selected meaningful genes and produced sensible cell maps of Smart-seq2 datasets, while explaining the excess scatter and zero counts that simpler models miss. The authors offer the approach as a drop-in normalization step for non-barcoded experiments.
Catalog record
| CREATOR | Jan Lause, Christoph Ziegenhain, Leonard Hartmanis, Philipp Berens, Dmitry Kobak |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67877 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |