JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1019 10.6339/21-JDS1019 Statistical Data Science A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets LinderHenry1 https://orcid.org/0000-0001-8986-0354 ZhangYupingyuping.zhang@uconn.edu1∗ Department of Statistics, University of Connecticut, Storrs, Connecticut, USA Corresponding author. Email: yuping.zhang@uconn.edu. 20211682021194555568 Supplementary Material

Supplementary Materials include descriptions for data and software.

12320211772021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

Large-scale genomics studies provide researchers with access to extensive datasets with extensive detail and unprecedented scope that encompasses not only genes, but also more experimental functional units, including non-coding microRNAs (miRNAs). In order to analyze these high-fidelity data while remaining faithful to the underlying biology, statistical methods are necessary that can reflect the full range of understanding in contemporary molecular biology, while remaining flexible enough to analyze a wide range of data and complex phenomena. Leveraging multiple omics datasets, miRNA-gene targets as well as signaling pathway topology, we present an integrative linear model to analyze signaling pathways. Specifically, we use a mixed linear model to characterize tumor and healthy tissue, and execute statistical significance testing to identify pathway disturbances. In this paper, pan-cancer analysis is performed for a wide range of signaling pathways. We discuss specific findings from this analysis, as well as an interactive data visualization available for public consumption that contains the full range of our analytic findings.

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