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ChromVAR: Inferring transcription factor variation from single-cell epigenomic data

Alicia N. Schep, Beijing Wu, Jason D. Buenrostro, William J. Greenleaf
doi: https://doi.org/10.1101/110346
Alicia N. Schep
1Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
2Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305
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Beijing Wu
1Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
2Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305
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Jason D. Buenrostro
3Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
4Harvard Society of Fellows, Harvard University, Cambridge, MA 02138, USA
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  • For correspondence: jbuen@broadinstitute.org wjg@stanford.edu
William J. Greenleaf
1Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
2Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305
5Department of Applied Physics, Stanford University, Stanford, CA 94025, USA
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  • For correspondence: jbuen@broadinstitute.org wjg@stanford.edu
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Abstract

Single cell ATAC-seq (scATAC) yields sparse data that makes application of conventional computational approaches for data analysis challenging or impossible. We developed chromVAR, an R package for analyzing sparse chromatin accessibility data by estimating the gain or loss of accessibility within sets of peaks sharing the same motif or annotation while controlling for known technical biases. chromVAR enables accurate clustering of scATAC-seq profiles and enables characterization of known, or the de novo identification of novel, sequence motifs associated with variation in chromatin accessibility across single cells or other sparse epigenomic data sets.

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Posted February 21, 2017.
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ChromVAR: Inferring transcription factor variation from single-cell epigenomic data
Alicia N. Schep, Beijing Wu, Jason D. Buenrostro, William J. Greenleaf
bioRxiv 110346; doi: https://doi.org/10.1101/110346
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ChromVAR: Inferring transcription factor variation from single-cell epigenomic data
Alicia N. Schep, Beijing Wu, Jason D. Buenrostro, William J. Greenleaf
bioRxiv 110346; doi: https://doi.org/10.1101/110346

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