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Exploratory Gene Ontology Analysis with Interactive Visualization

Junjie Zhu, Qian Zhao, Eugene Katsevich, Chiara Sabatti
doi: https://doi.org/10.1101/436741
Junjie Zhu
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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  • For correspondence: jjzhu@stanford.edu sabatti@stanford.edu
Qian Zhao
2Department of Statistics, Stanford University, Stanford, CA, USA
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Eugene Katsevich
2Department of Statistics, Stanford University, Stanford, CA, USA
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Chiara Sabatti
2Department of Statistics, Stanford University, Stanford, CA, USA
3Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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  • For correspondence: jjzhu@stanford.edu sabatti@stanford.edu
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Abstract

The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu.

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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. All rights reserved. No reuse allowed without permission.
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Posted October 05, 2018.
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Exploratory Gene Ontology Analysis with Interactive Visualization
Junjie Zhu, Qian Zhao, Eugene Katsevich, Chiara Sabatti
bioRxiv 436741; doi: https://doi.org/10.1101/436741
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Exploratory Gene Ontology Analysis with Interactive Visualization
Junjie Zhu, Qian Zhao, Eugene Katsevich, Chiara Sabatti
bioRxiv 436741; doi: https://doi.org/10.1101/436741

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