Cargando…

An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phen...

Descripción completa

Detalles Bibliográficos
Autores principales: Matsumoto, Hirotaka, Hayashi, Tetsutaro, Ozaki, Haruka, Tsuyuzaki, Koki, Umeda, Mana, Iida, Tsuyoshi, Nakamura, Masaya, Okano, Hideyuki, Nikaido, Itoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8499053/
https://www.ncbi.nlm.nih.gov/pubmed/34632380
http://dx.doi.org/10.1093/nargab/lqz020
_version_ 1784580294587711488
author Matsumoto, Hirotaka
Hayashi, Tetsutaro
Ozaki, Haruka
Tsuyuzaki, Koki
Umeda, Mana
Iida, Tsuyoshi
Nakamura, Masaya
Okano, Hideyuki
Nikaido, Itoshi
author_facet Matsumoto, Hirotaka
Hayashi, Tetsutaro
Ozaki, Haruka
Tsuyuzaki, Koki
Umeda, Mana
Iida, Tsuyoshi
Nakamura, Masaya
Okano, Hideyuki
Nikaido, Itoshi
author_sort Matsumoto, Hirotaka
collection PubMed
description Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. Our algorithm decomposes mapped count data matrix for a gene region using non-negative matrix factorization, quantifies the differential expression level based on the decomposed matrix, and compares the differential expression level based on annotation-based approach to discover previously unannotated DE transcripts. We performed single-cell RNA sequencing for human neural stem cells and applied our algorithm to the dataset. We also applied our algorithm to two public single-cell RNA sequencing datasets correspond to mouse ES and primitive endoderm cells, and human preimplantation embryos. As a result, we discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.
format Online
Article
Text
id pubmed-8499053
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-84990532021-10-08 An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data Matsumoto, Hirotaka Hayashi, Tetsutaro Ozaki, Haruka Tsuyuzaki, Koki Umeda, Mana Iida, Tsuyoshi Nakamura, Masaya Okano, Hideyuki Nikaido, Itoshi NAR Genom Bioinform Methods Article Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. Our algorithm decomposes mapped count data matrix for a gene region using non-negative matrix factorization, quantifies the differential expression level based on the decomposed matrix, and compares the differential expression level based on annotation-based approach to discover previously unannotated DE transcripts. We performed single-cell RNA sequencing for human neural stem cells and applied our algorithm to the dataset. We also applied our algorithm to two public single-cell RNA sequencing datasets correspond to mouse ES and primitive endoderm cells, and human preimplantation embryos. As a result, we discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation. Oxford University Press 2019-12-16 /pmc/articles/PMC8499053/ /pubmed/34632380 http://dx.doi.org/10.1093/nargab/lqz020 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://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/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Article
Matsumoto, Hirotaka
Hayashi, Tetsutaro
Ozaki, Haruka
Tsuyuzaki, Koki
Umeda, Mana
Iida, Tsuyoshi
Nakamura, Masaya
Okano, Hideyuki
Nikaido, Itoshi
An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title_full An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title_fullStr An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title_full_unstemmed An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title_short An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data
title_sort nmf-based approach to discover overlooked differentially expressed gene regions from single-cell rna-seq data
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8499053/
https://www.ncbi.nlm.nih.gov/pubmed/34632380
http://dx.doi.org/10.1093/nargab/lqz020
work_keys_str_mv AT matsumotohirotaka annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT hayashitetsutaro annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT ozakiharuka annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT tsuyuzakikoki annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT umedamana annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT iidatsuyoshi annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT nakamuramasaya annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT okanohideyuki annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT nikaidoitoshi annmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT matsumotohirotaka nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT hayashitetsutaro nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT ozakiharuka nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT tsuyuzakikoki nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT umedamana nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT iidatsuyoshi nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT nakamuramasaya nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT okanohideyuki nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata
AT nikaidoitoshi nmfbasedapproachtodiscoveroverlookeddifferentiallyexpressedgeneregionsfromsinglecellrnaseqdata