A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets

Linder, Henry; Zhang, Yuping

doi:10.6339/21-JDS1019

Journal of Data Science

A Pan-Cancer Network Analysis with Integration of miRNA-Gene Targeting for Multiomics Datasets

Volume 19, Issue 4 (2021), pp. 555–568

Henry Linder Yuping Zhang

https://doi.org/10.6339/21-JDS1019

Pub. online: 16 August 2021 Type: Statistical Data Science

Received
12 March 2021

Accepted
17 July 2021

Published
16 August 2021

Abstract

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.

Supplementary material

Supplementary Material

Supplementary Materials include descriptions for data and software.

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Keywords

hypothesis testing integrative statistical learning large-scale inference multi-view data integration

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