Efficient pre-processing of Single-cell ATAC-seq data
- Creators
- Gao, Fan
- Pachter, Lior
Abstract
The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 18 times faster than Cell Ranger on human samples, and that uses 33% less RAM when 8 CPU threads are used. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signals and interaction traces for cell groups. We demonstrate the utility of scATAK in an exploration of the chromatin regulatory landscape of a healthy adult human brain and show that it can reveal cell-type-specific features. scATAK is available at https://pachterlab.github.io/scATAK/.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. We thank Xun Wang for helpful suggestions. The work was possible thanks to support by the Beckman Institute at Caltech for the Caltech Bioinformatics Resource Center. FG and LP were supported in part by NIH R01 DK126925-01. The authors have declared no competing interest.Attached Files
Submitted - 2021.12.08.471788v1.full.pdf
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Additional details
- Eprint ID
- 112356
- Resolver ID
- CaltechAUTHORS:20211210-240593000
- R01 DK126925-01
- NIH
- Created
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2021-12-10Created from EPrint's datestamp field
- Updated
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2022-02-01Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering