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Latent periodic process inference from single-cell RNA-seq data

Shaoheng Liang, Fang Wang, Jincheng Han, Ken Chen
doi: https://doi.org/10.1101/625566
Shaoheng Liang
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
2Department of Computer Science, Rice University, Houston, Texas, USA
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Fang Wang
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Jincheng Han
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Ken Chen
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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  • For correspondence: kchen3@mdanderson.org
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Abstract

Convoluted biological processes underlie the development of multicellular organisms and diseases. Advances in scRNA-seq make it possible to study these processes from cells at various developmental stages. Achieving accurate characterization is challenging, however, particularly for periodic processes, such as cell cycles. To address this, we developed Cyclum, a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Cyclum is available at https://github.com/KChen-lab/cyclum.

Footnotes

  • sliang3{at}mdanderson.org, fwang9{at}mdanderson.org, jhan6{at}mdanderson.org, kchen3{at}mdanderson.org.

Copyright 
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.
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Posted May 02, 2019.
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Latent periodic process inference from single-cell RNA-seq data
Shaoheng Liang, Fang Wang, Jincheng Han, Ken Chen
bioRxiv 625566; doi: https://doi.org/10.1101/625566
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Latent periodic process inference from single-cell RNA-seq data
Shaoheng Liang, Fang Wang, Jincheng Han, Ken Chen
bioRxiv 625566; doi: https://doi.org/10.1101/625566

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