Journal
NATURE METHODS
Volume 16, Issue 5, Pages 397-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-019-0367-1
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Funding
- ERC Consolidator Grant [724226_cis-CONTROL]
- KU Leuven [C14/18/092]
- Harry J. Lloyd Charitable Trust
- Foundation Against Cancer [2016-070]
- FWO [11F1519N, 1S03317N, 1S75219N, G0B5619N]
- Kom op tegen Kanker (Stand up to Cancer), the Flemish Cancer Society
- Hercules Foundation [AKUL/13/41]
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We present cisTopic, a probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data (http://github.com/aertslab/cistopic). Using a compendium of single-cell ATAC-seq datasets from differentiating hematopoietic cells, brain and transcription factor perturbations, we demonstrate that topic modeling can be exploited for robust identification of cell types, enhancers and relevant transcription factors. cisTopic provides insight into the mechanisms underlying regulatory heterogeneity in cell populations.
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