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

Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. Thes...

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Autores principales: Lause, Jan, Ziegenhain, Christoph, Hartmanis, Leonard, Berens, Philipp, Kobak, Dmitry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10418209/
https://www.ncbi.nlm.nih.gov/pubmed/37577688
http://dx.doi.org/10.1101/2023.08.02.551637
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author Lause, Jan
Ziegenhain, Christoph
Hartmanis, Leonard
Berens, Philipp
Kobak, Dmitry
author_facet Lause, Jan
Ziegenhain, Christoph
Hartmanis, Leonard
Berens, Philipp
Kobak, Dmitry
author_sort Lause, Jan
collection PubMed
description Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for UMI-based sequencing protocols, where unique molecular identifiers (UMIs) help to remove PCR amplification noise by keeping track of the original molecules. In contrast, full-length protocols such as Smart-seq2 lack UMIs and retain amplification noise, making negative binomial models inapplicable. Here, we extend Pearson residuals to such read count data by modeling them as a compound process: we assume that the captured RNA molecules follow the negative binomial distribution, but are replicated according to an amplification distribution. Based on this model, we introduce compound Pearson residuals and show that they can be analytically obtained without explicit knowledge of the amplification distribution. Further, we demonstrate that compound Pearson residuals lead to a biologically meaningful gene selection and low-dimensional embeddings of complex Smart-seq2 datasets. Finally, we empirically study amplification distributions across several sequencing protocols, and suggest that they can be described by a broken power law. We show that the resulting compound distribution captures overdispersion and zero-inflation patterns characteristic of read count data. In summary, compound Pearson residuals provide an efficient and effective way to normalize read count data based on simple mechanistic assumptions.
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spelling pubmed-104182092023-08-12 Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs Lause, Jan Ziegenhain, Christoph Hartmanis, Leonard Berens, Philipp Kobak, Dmitry bioRxiv Article Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for UMI-based sequencing protocols, where unique molecular identifiers (UMIs) help to remove PCR amplification noise by keeping track of the original molecules. In contrast, full-length protocols such as Smart-seq2 lack UMIs and retain amplification noise, making negative binomial models inapplicable. Here, we extend Pearson residuals to such read count data by modeling them as a compound process: we assume that the captured RNA molecules follow the negative binomial distribution, but are replicated according to an amplification distribution. Based on this model, we introduce compound Pearson residuals and show that they can be analytically obtained without explicit knowledge of the amplification distribution. Further, we demonstrate that compound Pearson residuals lead to a biologically meaningful gene selection and low-dimensional embeddings of complex Smart-seq2 datasets. Finally, we empirically study amplification distributions across several sequencing protocols, and suggest that they can be described by a broken power law. We show that the resulting compound distribution captures overdispersion and zero-inflation patterns characteristic of read count data. In summary, compound Pearson residuals provide an efficient and effective way to normalize read count data based on simple mechanistic assumptions. Cold Spring Harbor Laboratory 2023-08-05 /pmc/articles/PMC10418209/ /pubmed/37577688 http://dx.doi.org/10.1101/2023.08.02.551637 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Lause, Jan
Ziegenhain, Christoph
Hartmanis, Leonard
Berens, Philipp
Kobak, Dmitry
Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title_full Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title_fullStr Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title_full_unstemmed Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title_short Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs
title_sort compound models and pearson residuals for normalization of single-cell rna-seq data without umis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10418209/
https://www.ncbi.nlm.nih.gov/pubmed/37577688
http://dx.doi.org/10.1101/2023.08.02.551637
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