cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.
Carmen Bravo Gonzalez-BlasLiesbeth MinnoyeDafni PapasokratiSara AibarGert HulselmansValerie ChristiaensKristofer DavieJasper WoutersStein AertsPublished in: Nature methods (2019)
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.
Keyphrases
- single cell
- rna seq
- transcription factor
- high throughput
- electronic health record
- dna binding
- induced apoptosis
- magnetic resonance imaging
- multiple sclerosis
- bone marrow
- stem cells
- big data
- dna methylation
- oxidative stress
- mesenchymal stem cells
- blood brain barrier
- cell therapy
- magnetic resonance
- cell cycle arrest
- brain injury
- genome wide
- contrast enhanced
- subarachnoid hemorrhage
- deep learning
- pi k akt