Abstract
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.AvailabilityInstallation instructions, documentation, and tutorials are available at: https://satijalab.org/signac/
Publisher
Cold Spring Harbor Laboratory
Reference74 articles.
1. V. Agarwal and J. Shendure . Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. Cell Rep., 31(7), May 2020. URL http://www.cell.com/article/S2211124720306161/abstract.
2. Profiling chromatin states using single-cell itChIP-seq
3. The ENCODE Blacklist: Identification of Problematic Regions of the Genome
4. S. Arora , M. Morgan , M. Carlson , and H. Pagès. Genome-InfoDb: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style, 2020.
5. S. Arya , D. Mount , S. E. Kemp , and G. Jefferis . RANN: Fast nearest neighbour search (wraps ANN library) using L2 metric, 2019. URL “https://CRAN.R-project.org/package=RANN.