Single-cell chromatin state analysis with Signac

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 comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an e...

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Vydáno v:Nature methods Ročník 18; číslo 11; s. 1333 - 1341
Hlavní autoři: Stuart, Tim, Srivastava, Avi, Madad, Shaista, Lareau, Caleb A, Satija, Rahul
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Nature Publishing Group 01.11.2021
Témata:
Bone Marrow Cells - chemistry
Bone Marrow Cells - metabolism
Chromatin
Chromatin - chemistry
Chromatin - genetics
Chromatin - metabolism
Clustering
Computational Biology - methods
Computer applications
Datasets
Deoxyribonucleic acid
DNA
Experimental methods
Gene expression
Gene Expression Profiling
Genotypes
Humans
Interoperability
Leukocytes, Mononuclear - chemistry
Leukocytes, Mononuclear - metabolism
Measurement methods
Mitochondria
Mitochondria - genetics
Quality control
Sequence Analysis, DNA
Single-Cell Analysis - methods
Software
ISSN:1548-7091, 1548-7105, 1548-7105
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Shrnutí: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 comprehensive toolkit for the analysis of single-cell chromatin data. 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. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse 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 analyze datasets containing over 700,000 cells.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-021-01282-5