chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
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Abstract
Single-cell ATAC-seq (scATAC) yields sparse data that make conventional analysis challenging. We developed chromVAR (http://www.github.com/GreenleafLab/chromVAR), an R package for analyzing sparse chromatin-accessibility data by estimating gain or loss of accessibility within peaks sharing the same motif or annotation while controlling for technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and characterization of known and de novo sequence motifs associated with variation in chromatin accessibility.
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Similar changes in TFBS accessibility were found using either ENCODE ChIP-seq data from K562 or predicted TF binding sites (JASPAR TF motifs and chromVAR).
“We found similar changes in TFBS accessibility using either ENCODE ChIP-seq data from K562 or predicted TF binding sites (JASPAR TF motifs13 and chromVAR14)”
From Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens (2021) · cites this paper as [14]
Prior work has shown that pooling five or more cells accurately reproduces chromatin accessibility profiles.
“previous reports have shown that aggregation of five or more cells is sufficient toaccurately reproduce chromatin accessibility profiles (Satpathy et al., 2018; Schep et al., 2017).”
From Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks (2019) · cites this paper as Schep et al., 2017
Single-cell chromatin accessibility data require specialized analysis methods that generate accessibility measurements either across groups of cells or across sets of genomic features.
“Therefore, these data require specialized analysis methods that generate accessibility measurements either across groups of cells [62] or across sets of genomic features (e.g. based on ChIP-seq data or TFs) [63].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [63]
chromVAR uses predefined cistromes or determines them ab initio based on motif enrichment to calculate average bias-corrected Z-scores.
“On the other hand, chromVAR uses predefined cistromes (defined as a set of cis-acting targets of a TF) or determines them ab initio based on motif enrichment to calculate average bias-corrected Z-scores [63].”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [63]
chromVAR calculates per-cell deviation of accessibility across all motif-containing peaks while correcting for Tn5 insertion bias.
“To obtain enrichment scores per cell, chromVAR can be used to calculate the deviation of accessibility across all motif-containing peaks per cell while correcting for the insertion bias of the Tn5 transposase”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [153]
dscATAC-seq reproduced known variation in transcription factor binding motif activity across single GM12878 cells.
“Notably, dscATAC-seq recapitulated known variation in the activity of TF binding motifs across single GM12878 cells, as previously reported18,26 (Supplementary Fig. 3d).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [26]
TF activity can be predicted by measuring differences in TF motif accessibility across cells using chromVAR.
“We can also predict TF activity by measuring differences in TF motif accessibility across cells using chromVAR39.”
From NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells (2022) · cites this paper as [39]
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A blueprint for tumor-infiltrating B cells across human cancers
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Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective
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Context specificity of the EMT transcriptional response
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Functional inference of gene regulation using single-cell multi-omics
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Self-citationEpigenomic State Transitions Characterize Tumor Progression in Mouse Lung Adenocarcinoma
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Gene regulation by gonadal hormone receptors underlies brain sex differences
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Epigenetic regulation during cancer transitions across 11 tumour types
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- Authors
- Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J.
- Affiliations
- Stanford Medicine (US)
- Broad Institute (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Buenrostro, Jason D
- Greenleaf, William J
- Venue
- Nature Methods
- Published
- 2017-08-21
- Volume
- 14
- Issue
- 10
- Pages
- 975-978
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5623146
- OpenAlex
- W2750049634
- Cited by
- 1,680
- References
- 29
- Citations by year
- 2026: 272 · 2025: 326 · 2024: 258 · 2023: 249 · 2022: 212 · 2021: 175 · 2020: 98 · 2019: 57 · 2018: 28 · 2017: 5
- FWCI
- 21.34
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Science Foundation ((DGE-114747, 114747)
- Broad Institute
- National Institutes of Health (DGE-114747)
- Society of Fellows, Harvard University
- National Human Genome Research Institute (UM1HG009436, P50HG007735, T32HG000044)
- National Institute of General Medical Sciences (R01GM111990)
- National Institute of Allergy and Infectious Diseases (U19AI057266)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- chromatin accessibility, transcription factors, single-cell ATAC-seq, cell clustering
- MeSH terms
- Algorithms, Animals, Cell Line, Gene Expression Regulation, Software, Transcription Factors, Sequence Analysis, DNA, Epigenomics
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