Genome binding/occupancy profiling by high throughput sequencing
Summary
We present cisTopic, a probabilistic framework to simultaneously discover co-accessible enhancers and stable cell states from sparse single-cell epigenomics data (http://github.com/aertslab/cistopic). On a compendium of single-cell ATAC-seq datasets from differentiating hematopoietic cells, brain, and transcription-factor perturbation dynamics, we demonstrate that topic modelling can be exploited for a robust identification of cell types, enhancers, and relevant transcription factors. cisTopic provides insight into the mechanisms underlying regulatory heterogeneity within cell populations.
Overall design
Time series scATAC-seq (Fluidigm C1) and bulk OmniATAC-seq of SOX10 knockdown-induced phenotype switching in two melanoma cell lines. ChIP-seq of H3K27Ac on four melanoma cell lines.
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