Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
- Harvard Medical School, United States
- Massachusetts Institute of Technology, United States
- Broad Institute of MIT and Harvard, United States
- Harvard University, United States
Abstract
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.
Data availability
All of the analyzed real datasets are publicly available and the relevant GEO accession codes are included in the manuscript. All of the simulated and real data can be accessed through Code Ocean at the following URL: https://doi.org/10.24433/CO.9044782e-cb96-4733-8a4f-bf42c21399e6
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Cell diversity and network dynamics in photosensitive human brain organoids.Gene Expression Omnibus, GSE86153.
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Adult mouse cortical cell taxonomy by single cell transcriptomicsGene Expression Omnibus, GSE71585.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (T32GM007753)
- Dylan Kotliar
- Adrian Veres
- M Aurel Nagy
- Eran Hodis
National Institute of Allergy and Infectious Diseases (R01AI099210)
- Pardis C Sabeti
U.S. Food and Drug Administration (HHSF223201810172C)
- Dylan Kotliar
- Pardis C Sabeti
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2019, Kotliar et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
eLife 8:e43803.
https://doi.org/10.7554/eLife.43803
Further reading
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- Computational and Systems Biology
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