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Multimodal single-cell chromatin analysis with Signac

By Tim Stuart, Avi Srivastava, Caleb Lareau, Rahul Satija

Posted 10 Nov 2020
bioRxiv DOI: 10.1101/2020.11.09.373613

The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a framework for the analysis of single-cell chromatin data, as an extension of the Seurat R toolkit for single-cell multimodal analysis. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis, and interactive visualization. Furthermore, Signac facilitates the analysis of multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance, and mitochondrial genotype. We demonstrate scaling of the Signac framework to datasets containing over 700,000 cells. ### Competing Interest Statement In the past three years, RS has worked as a consultant for Bristol-Myers Squibb, Regeneron, and Kallyope, and served as an SAB member for ImmunAI and Apollo Life Sciences GmbH.

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