IDEAS: individual level differential expression analysis for single-cell RNA-seq data

We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (in...

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Autores principales: Zhang, Mengqi, Liu, Si, Miao, Zhen, Han, Fang, Gottardo, Raphael, Sun, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784862/
https://www.ncbi.nlm.nih.gov/pubmed/35073995
http://dx.doi.org/10.1186/s13059-022-02605-1
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author Zhang, Mengqi
Liu, Si
Miao, Zhen
Han, Fang
Gottardo, Raphael
Sun, Wei
author_facet Zhang, Mengqi
Liu, Si
Miao, Zhen
Han, Fang
Gottardo, Raphael
Sun, Wei
author_sort Zhang, Mengqi
collection PubMed
description We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-022-02605-1).
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spelling pubmed-87848622022-01-24 IDEAS: individual level differential expression analysis for single-cell RNA-seq data Zhang, Mengqi Liu, Si Miao, Zhen Han, Fang Gottardo, Raphael Sun, Wei Genome Biol Method We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-022-02605-1). BioMed Central 2022-01-24 /pmc/articles/PMC8784862/ /pubmed/35073995 http://dx.doi.org/10.1186/s13059-022-02605-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Zhang, Mengqi
Liu, Si
Miao, Zhen
Han, Fang
Gottardo, Raphael
Sun, Wei
IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title_full IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title_fullStr IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title_full_unstemmed IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title_short IDEAS: individual level differential expression analysis for single-cell RNA-seq data
title_sort ideas: individual level differential expression analysis for single-cell rna-seq data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784862/
https://www.ncbi.nlm.nih.gov/pubmed/35073995
http://dx.doi.org/10.1186/s13059-022-02605-1
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