Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
Broad Institute · Harvard University · +5 more institutions
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…
Citation impact
- FWCI
- 15.89
- Percentile
- 100%
- References
- 69
Authors
7- DKDylan KotliarCorresponding
Broad Institute, Harvard University, Harvard–MIT Division of Health Sciences and Technology, Center for Systems Biology, Massachusetts Institute of Technology
- AVAdrian Veres
Harvard University, Harvard–MIT Division of Health Sciences and Technology, Center for Systems Biology, Harvard Stem Cell Institute, Massachusetts Institute of Technology
- MAM. Aurel Nagy
Harvard University, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology
- STShervin Tabrizi
Broad Institute
- EHEran Hodis
Harvard University, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology
Topics & keywords
- Cell type
- Biology
- RNA-Seq
- Gene expression
- Computational biology
- Cell
- RNA
- Gene
- Responsible consumption and production