Abstract
Background
Multi-view -omics datasets offer rich opportunities for integrative analysis across genomic, transcriptomic, and epigenetic data platforms. Statistical methods are needed to rigorously implement current research on functional biology, matching the complex dynamics of systems genomic datasets.
Methods
We apply imputation for missing data and a structural, graph-theoretic pathway model to a dataset of 22 cancers across 173 signaling pathways. Our pathway model integrates multiple data platforms, and we test for differential activation between cancerous tumor and healthy tissue populations.
Results
Our pathway analysis reveals significant disturbance in signaling pathways that are known to relate to oncogenesis. We identify several pathways that suggest new research directions, including the Trk signaling and focal adhesion kinase activation pathways in sarcoma.
Conclusions
Our integrative analysis confirms contemporary research findings, which supports the validity of our findings. We implement an interactive data visualization for exploration of the pathway analyses, which is available online for public access.
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The authors Henry Linder and Yu** Zhang declare that they have no conflict of interests.
This article does not contain any studies with human or animal subjects performed by any of the authors.
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Author summary: Genomic Big Data is now collected across multiple experimental platforms as a matter of course. These data offer multiple unique perspectives of the human genome and its processes, with great potential to improve our understanding of complex diseases such as cancer. In this paper, we apply a statistical model of the biological structure of genetic signaling pathways, which we use to explore functional differences between healthy and tumorous tissue. Our analysis, applied across multiple cancers and hundreds of signaling pathways, is accompanied by an interactive web application for exploratory visualization of our findings.
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Linder, H., Zhang, Y. A pan-cancer integrative pathway analysis of multi-omics data. Quant Biol 8, 130–142 (2020). https://doi.org/10.1007/s40484-019-0185-6
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DOI: https://doi.org/10.1007/s40484-019-0185-6