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bioRxiv · 10.1101/370346

Cis-topic modelling of single cell epigenomes

Bravo Gonzalez-Blas, C.·Minnoye, L.·Papasokrati, D.·Aibar, S.·Hulselmans, G.·Christiaens, V.·Davie, K.·Wouters, J.·Aerts, S.
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Abstract

Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover \"cis-regulatory topics\" and stable cell states from sparse single-cell epigenomics data. After benchmarking cisTopic on single-cell ATAC-seq data, single-cell DNA methylation data, and semi-simulated single-cell ChIP-seq data, we use cisTopic to predict regulatory programs in the human brain and validate these by aligning them with co-expression networks derived from single-cell RNA-seq data. Next, we performed a time-series single-cell ATAC-seq experiment after SOX10 perturbations in melanoma cultures, where cisTopic revealed dynamic regulatory topics driven by SOX10 and AP-1. Finally, machine learning and enhancer modelling approaches allowed to predict cell type specific SOX10 and SOX9 binding sites based on topic specific co-regulatory motifs. cisTopic is available as an R/Bioconductor package at http://github.com/aertslab/cistopic.

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Bravo Gonzalez-Blas, C., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G., Christiaens, V., Davie, K., Wouters, J., Aerts, S.. 2018-07-16. Cis-topic modelling of single cell epigenomes. https://doi.org/10.1101/370346

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