cisTopic: cis-Regulatory topic modelling on single-cell ATAC-seq data
By C. Bravo González-Blas, Liesbeth Minnoye, Dafni Papasokrati, S. Aibar, Gert J. Hulselmans, Valerie Christiaens, K. Davie, J. Wouters, S. Aerts. Nature Methods, 2019
407 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/87f17f939b79bb3e1d2746993a2c1cda48cb1b32
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Citations in the corpus
- Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Thus, treating chromatin accessibility data as binary might be detrimental for downstream analyses 160,161 , and methods that handle accessibility in a quantitative manner might improve GRN reconstruction 154,155,162,163 .”
- Best practices for single-cell analysis across modalities (2023, methodology): “TF-motif enrichment facilitates the characterization of cell identity and can be conducted on a cluster level using a hypergeometric test on cluster-specific DARs 140 .”