1. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data
- Author
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Jasper Wouters, Kristofer Davie, Stein Aerts, Carmen Bravo González-Blas, Valerie Christiaens, Sara Aibar, Gert Hulselmans, Dafni Papasokrati, and Liesbeth Minnoye
- Subjects
Epigenomics ,Topic model ,Biochemistry & Molecular Biology ,CHROMATIN ACCESSIBILITY ,Computer science ,ATAC-seq ,Computational biology ,Regulatory Sequences, Nucleic Acid ,MOUSE ,Biochemistry ,Article ,Biochemical Research Methods ,Workflow ,TOUCAN ,Mice ,03 medical and health sciences ,ELEMENTS ,Animals ,Cluster Analysis ,Humans ,Gene Regulatory Networks ,Enhancer ,Cluster analysis ,Molecular Biology ,Transcription factor ,Cells, Cultured ,030304 developmental biology ,0303 health sciences ,Blood Cells ,Science & Technology ,Sequence Analysis, RNA ,Gene Expression Profiling ,Brain ,Cell Biology ,Models, Theoretical ,Identification (information) ,Regulatory sequence ,INFERENCE ,Single-Cell Analysis ,CLUSTERS ,Life Sciences & Biomedicine ,PACKAGE ,Biotechnology - Abstract
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. As an unsupervised Bayesian framework, cisTopic classifies regions in scATAC-seq data into regulatory topics, which are used for clustering.
- Published
- 2019