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One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data

Chloe Wang, Lin Zhang, Bo Wang
doi: https://doi.org/10.1101/2021.05.12.443814
Chloe Wang
1University Health Network;
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Lin Zhang
2University of Toronto
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Bo Wang
1University Health Network;
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  • For correspondence: bo.wang@uhnresearch.ca
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1 Abstract

The surge of single-cell RNA sequencing technologies enables the accessibility to large single-cell RNA-seq datasets at the scale of hundreds of thousands of single cells. Integrative analysis of large-scale scRNA-seq datasets has the potential of revealing de novo cell types as well as aggregating biological information. However, most existing methods fail to integrate multiple large-scale scRNA-seq datasets in a computational and memory efficient way. We hereby propose OCAT, One Cell At a Time, a graph-based method that sparsely encodes single-cell gene expressions to integrate data from multiple sources without most variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell-type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT facilitates a variety of downstream analyses, such as gene prioritization, trajectory inference, pseudotime inference and cell inference. OCAT is a unifying tool to simplify and expedite single-cell data analysis.

Competing Interest Statement

The authors have declared no competing interest.

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 May 13, 2021.
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One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data
Chloe Wang, Lin Zhang, Bo Wang
bioRxiv 2021.05.12.443814; doi: https://doi.org/10.1101/2021.05.12.443814
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One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data
Chloe Wang, Lin Zhang, Bo Wang
bioRxiv 2021.05.12.443814; doi: https://doi.org/10.1101/2021.05.12.443814

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