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

Lause, J.; Ziegenhain, C.; Hartmanis, L.; Berens, P.; Kobak, D.

2023-08-05 bioinformatics
10.1101/2023.08.02.551637 bioRxiv
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Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest 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.

Published in Genome Biology (predicted rank #2) · training set

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