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Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in indivi...

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Autores principales: Kotliar, Dylan, Veres, Adrian, Nagy, M Aurel, Tabrizi, Shervin, Hodis, Eran, Melton, Douglas A, Sabeti, Pardis C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639075/
https://www.ncbi.nlm.nih.gov/pubmed/31282856
http://dx.doi.org/10.7554/eLife.43803
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author Kotliar, Dylan
Veres, Adrian
Nagy, M Aurel
Tabrizi, Shervin
Hodis, Eran
Melton, Douglas A
Sabeti, Pardis C
author_facet Kotliar, Dylan
Veres, Adrian
Nagy, M Aurel
Tabrizi, Shervin
Hodis, Eran
Melton, Douglas A
Sabeti, Pardis C
author_sort Kotliar, Dylan
collection PubMed
description Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.
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spelling pubmed-66390752019-07-19 Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq Kotliar, Dylan Veres, Adrian Nagy, M Aurel Tabrizi, Shervin Hodis, Eran Melton, Douglas A Sabeti, Pardis C eLife Computational and Systems Biology Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis. eLife Sciences Publications, Ltd 2019-07-08 /pmc/articles/PMC6639075/ /pubmed/31282856 http://dx.doi.org/10.7554/eLife.43803 Text en © 2019, Kotliar et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Kotliar, Dylan
Veres, Adrian
Nagy, M Aurel
Tabrizi, Shervin
Hodis, Eran
Melton, Douglas A
Sabeti, Pardis C
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title_full Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title_fullStr Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title_full_unstemmed Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title_short Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
title_sort identifying gene expression programs of cell-type identity and cellular activity with single-cell rna-seq
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639075/
https://www.ncbi.nlm.nih.gov/pubmed/31282856
http://dx.doi.org/10.7554/eLife.43803
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