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Identifying progressive gene network perturbation from single-cell RNA-seq data

View ORCID ProfileSumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee
doi: https://doi.org/10.1101/297275
Sumit Mukherjee
1Department of Electrical Engineering, University of Washington, Seattle.
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Alberto Carignano
1Department of Electrical Engineering, University of Washington, Seattle.
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Georg Seelig
1Department of Electrical Engineering, University of Washington, Seattle.
2Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.
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Su-In Lee
2Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.
3Department of Genome Sciences, University of Washington, Seattle.
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Abstract

Identifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.

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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 April 07, 2018.
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Identifying progressive gene network perturbation from single-cell RNA-seq data
Sumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee
bioRxiv 297275; doi: https://doi.org/10.1101/297275
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Identifying progressive gene network perturbation from single-cell RNA-seq data
Sumit Mukherjee, Alberto Carignano, Georg Seelig, Su-In Lee
bioRxiv 297275; doi: https://doi.org/10.1101/297275

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