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GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis

View ORCID ProfileSilu Huang, View ORCID ProfileCharles Blatti, Saurabh Sinha, View ORCID ProfileAditya Parameswaran
doi: https://doi.org/10.1101/2020.02.05.935411
Silu Huang
1Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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Charles Blatti
2Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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Saurabh Sinha
1Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
2Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
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Aditya Parameswaran
1Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
2Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
3School of Information and Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley CA, 94704, USA
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Abstract

Motivation A common but critical task in genomic data analysis is finding features that separate and thereby help explain differences between two classes of biological objects, e.g., genes that explain the differences between healthy and diseased patients. As lower-cost, high-throughput experimental methods greatly increase the number of samples that are assayed as objects for analysis, computational methods are needed to quickly provide insights into high-dimensional datasets with tens of thousands of objects and features.

Results We develop an interactive exploration tool called Genvisage that rapidly discovers the most discriminative feature pairs that best separate two classes in a dataset, and displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially when the numbers of objects and features are large, we propose a suite of optimizations to make Genvisage more responsive and demonstrate that our optimizations lead to a 400X speedup over competitive baselines for multiple biological data sets. With this speedup, Genvisage enables the exploration of more large-scale datasets and alternate hypotheses in an interactive and interpretable fashion. We apply Genvisage to uncover pairs of genes whose transcriptomic responses significantly discriminate treatments of several chemotherapy drugs.

Availability Free webserver at http://genvisage.knoweng.org:443/ with source code at https://github.com/KnowEnG/Genvisage

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 February 05, 2020.
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GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis
Silu Huang, Charles Blatti, Saurabh Sinha, Aditya Parameswaran
bioRxiv 2020.02.05.935411; doi: https://doi.org/10.1101/2020.02.05.935411
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GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis
Silu Huang, Charles Blatti, Saurabh Sinha, Aditya Parameswaran
bioRxiv 2020.02.05.935411; doi: https://doi.org/10.1101/2020.02.05.935411

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