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GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data

Harsh Shrivastava, View ORCID ProfileXiuwei Zhang, Srinivas Aluru, Le Song
doi: https://doi.org/10.1101/2020.04.23.058149
Harsh Shrivastava
1Department of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
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  • For correspondence: hshrivastava3@gatech.edu
Xiuwei Zhang
1Department of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
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Srinivas Aluru
1Department of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
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Le Song
1Department of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
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Abstract

Motivation Gene regulatory networks (GRNs) are graphs that specify the interactions between transcription factors (TFs) and their target genes. Understanding these interactions is crucial for studying the mechanisms in cell differentiation, growth and development. Computational methods are needed to infer these networks from measured data. Although the availability of single cell RNA-Sequencing (scRNA-Seq) data provides unprecedented scale and resolution of gene-expression data, the inference of GRNs remains a challenge, mainly due to the complexity of the regulatory relationships and the noise in the data.

Results We propose GRNUlar, a novel deep learning architecture based on the unrolled algorithms idea for GRN inference from scRNA-Seq data. Like some existing methods which use prior information of which genes are TFs, GRNUlar also incorporates this TF information using a sparse multi-task deep learning architecture. We also demonstrate the application of a recently developed unrolled architecture GLAD to recover undirected GRNs in the absence of TF information. These unrolled architectures require supervision to train, for which we leverage the existing synthetic data simulators which generate scRNA-Seq data guided by a GRN. We show that unrolled algorithms outperform the state-of-the-art methods on synthetic data as well as real datasets in both the settings of TF information being absent or available.

Availability Github link to GRNUlar - https://github.com/Harshs27/GRNUlar

Contact hshrivastava3{at}gatech.edu

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 April 25, 2020.
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GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data
Harsh Shrivastava, Xiuwei Zhang, Srinivas Aluru, Le Song
bioRxiv 2020.04.23.058149; doi: https://doi.org/10.1101/2020.04.23.058149
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GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data
Harsh Shrivastava, Xiuwei Zhang, Srinivas Aluru, Le Song
bioRxiv 2020.04.23.058149; doi: https://doi.org/10.1101/2020.04.23.058149

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