Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq Article Swipe
Dylan Kotliar
,
Adrian Veres
,
M. Aurel Nagy
,
Shervin Tabrizi
,
Eran Hodis
,
Douglas A. Melton
,
Pardis C. Sabeti
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.7554/elife.43803
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.7554/elife.43803
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.
Related Topics
Concepts
Cell type
Biology
RNA-Seq
Gene expression
Computational biology
Cell
RNA
Gene
Phenotype
Transcriptome
Genetics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.43803
- OA Status
- gold
- Cited By
- 523
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2958554910
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2958554910Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.43803Digital Object Identifier
- Title
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Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-SeqWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-07-08Full publication date if available
- Authors
-
Dylan Kotliar, Adrian Veres, M. Aurel Nagy, Shervin Tabrizi, Eran Hodis, Douglas A. Melton, Pardis C. SabetiList of authors in order
- Landing page
-
https://doi.org/10.7554/elife.43803Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7554/elife.43803Direct OA link when available
- Concepts
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Cell type, Biology, RNA-Seq, Gene expression, Computational biology, Cell, RNA, Gene, Phenotype, Transcriptome, GeneticsTop concepts (fields/topics) attached by OpenAlex
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-
523Total citation count in OpenAlex
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2025: 105, 2024: 156, 2023: 90, 2022: 82, 2021: 41Per-year citation counts (last 5 years)
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69Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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