Identification (biology)

Cell

Archaeology

Pooling

Transcription factor

Biology

History

Gene

Enhancer

Epigenomics

Gene expression

Artificial intelligence

Cell type

DNA methylation

Single-cell analysis

Compendium

Computer science

Botany

Computational biology

Genetics

Nature Methods

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.

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data