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bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data
MOTIVATION: Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an ef...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703772/ https://www.ncbi.nlm.nih.gov/pubmed/31584606 http://dx.doi.org/10.1093/bioinformatics/btz726 |
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author | Tang, Wenhao Bertaux, François Thomas, Philipp Stefanelli, Claire Saint, Malika Marguerat, Samuel Shahrezaei, Vahid |
author_facet | Tang, Wenhao Bertaux, François Thomas, Philipp Stefanelli, Claire Saint, Malika Marguerat, Samuel Shahrezaei, Vahid |
author_sort | Tang, Wenhao |
collection | PubMed |
description | MOTIVATION: Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. RESULTS: Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7703772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77037722020-12-07 bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data Tang, Wenhao Bertaux, François Thomas, Philipp Stefanelli, Claire Saint, Malika Marguerat, Samuel Shahrezaei, Vahid Bioinformatics Original Papers MOTIVATION: Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. RESULTS: Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-02-15 2019-10-04 /pmc/articles/PMC7703772/ /pubmed/31584606 http://dx.doi.org/10.1093/bioinformatics/btz726 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Tang, Wenhao Bertaux, François Thomas, Philipp Stefanelli, Claire Saint, Malika Marguerat, Samuel Shahrezaei, Vahid bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title | bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title_full | bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title_fullStr | bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title_full_unstemmed | bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title_short | bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data |
title_sort | baynorm: bayesian gene expression recovery, imputation and normalization for single-cell rna-sequencing data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703772/ https://www.ncbi.nlm.nih.gov/pubmed/31584606 http://dx.doi.org/10.1093/bioinformatics/btz726 |
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