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Tensor Decomposition Discriminates Tissues Using scATAC-seq

View ORCID ProfileY-h. Taguchi, View ORCID ProfileTurki Turki
doi: https://doi.org/10.1101/2022.08.04.502875
Y-h. Taguchi
1Department of Physics, Chuo University, Tokyo 112-8551, Japan
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  • For correspondence: tag@granular.com
Turki Turki
2Department of Computer Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Abstract

ATAC-seq is a powerful tool to measure the landscape structure of a chromosome. scATAC-seq is a recently updated version of ATAC-seq performed in a single cell.

The problem of scATAC-seq is data sparsity and most of the genomic sites are inaccessible.

Here, tensor decomposition (TD) was used to fill in missing values. In this study, TD was applied to massive scATAC-seq data and it generated averaging within 200 bp intervals and this number can be as high as 13,627,618. Currently, no other methods can deal with large sparse matrices.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Email: tag{at}granular.com

  • Email: tturki{at}kau.edu.sa, San Francisco, California 96678–2391

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 06, 2022.
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Tensor Decomposition Discriminates Tissues Using scATAC-seq
Y-h. Taguchi, Turki Turki
bioRxiv 2022.08.04.502875; doi: https://doi.org/10.1101/2022.08.04.502875
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Tensor Decomposition Discriminates Tissues Using scATAC-seq
Y-h. Taguchi, Turki Turki
bioRxiv 2022.08.04.502875; doi: https://doi.org/10.1101/2022.08.04.502875

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