chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data

By Alicia N. Schep, Beijing Wu, Jason D. Buenrostro, W.J. Greenleaf. Nature Methods, 2017

1,401 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4b872c73eb009cb002f55f69a0d2c6007c40084e

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Citations in the corpus

Computational principles and challenges in single-cell data integration (2021, methodology): “More recent methods, including MMD-MA 78 , SCIM 67 and UnionCom 68 , have generalized MATCHER to account for complex multivariate trajectories.”
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays (2021, positive): “The transcription factor deviation score was calculated using ChromVAR (Schep et al., 2017).”
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “05) function in the chromVAR package (Schep et al., 2017).”
Assessment of computational methods for the analysis of single-cell ATAC-seq data (2019, methodology): “Several computational methods have been developed to address the inherent sparsity 162 and high dimensionality of single cell ATAC-seq data, including BROCKMAN[3], 163 chromVAR[2], Cicero[8], cisTopic[9], Cusanovich2018[1, 10, 11], Gene Scoring[12], 164 scABC[13], Scasat[14], SCRAT[4], and SnapATAC[”
The chromatin accessibility landscape of primary human cancers (2018, background): “ChromVAR (35), a similar genome-wide approach which assesses the ability of a TF to affect flanking accessibility, identified a highly overlapping list of TFs (fig.”
Comprehensive analysis of single cell ATAC-seq data with SnapATAC (2021, methodology): “Speci ﬁ cally, we monitor the running time for (1) TF-IDF transformation and Singular Value Decomposition (SVD) for LSA, (2) function “ runModels ” with topics = c(2, 5, 10, 15, 20, 25, 30, 35, 40) and “ selectModel ” function in cis -Topic.”
Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation (2018, methodology): “We determined significance using ‘‘direction z-scores,’’ whereby we repeated the analysis described above for PCs calculated using 50 background peak permutations matching GC and mean accessibility, peaks were determined using ChromVAR (Schep et al., 2017).”