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M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data
BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923906/ https://www.ncbi.nlm.nih.gov/pubmed/31861972 http://dx.doi.org/10.1186/s12859-019-3243-1 |
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author | Zhang, Yu Wan, Changlin Wang, Pengcheng Chang, Wennan Huo, Yan Chen, Jian Ma, Qin Cao, Sha Zhang, Chi |
author_facet | Zhang, Yu Wan, Changlin Wang, Pengcheng Chang, Wennan Huo, Yan Chen, Jian Ma, Qin Cao, Sha Zhang, Chi |
author_sort | Zhang, Yu |
collection | PubMed |
description | BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS: We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION: A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S. |
format | Online Article Text |
id | pubmed-6923906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69239062019-12-30 M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data Zhang, Yu Wan, Changlin Wang, Pengcheng Chang, Wennan Huo, Yan Chen, Jian Ma, Qin Cao, Sha Zhang, Chi BMC Bioinformatics Research BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS: We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION: A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S. BioMed Central 2019-12-20 /pmc/articles/PMC6923906/ /pubmed/31861972 http://dx.doi.org/10.1186/s12859-019-3243-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhang, Yu Wan, Changlin Wang, Pengcheng Chang, Wennan Huo, Yan Chen, Jian Ma, Qin Cao, Sha Zhang, Chi M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title | M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title_full | M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title_fullStr | M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title_full_unstemmed | M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title_short | M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data |
title_sort | m3s: a comprehensive model selection for multi-modal single-cell rna sequencing data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923906/ https://www.ncbi.nlm.nih.gov/pubmed/31861972 http://dx.doi.org/10.1186/s12859-019-3243-1 |
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