A Graph Theoretical Approach to Data Fusion

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

Abstract

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. Therefore, it is increasingly being recognized that we can gain deeper understanding about underlying biology by combining the insights obtained from multiple, diverse datasets. Thus we propose a novel scalable computational approach to unsupervised data fusion. Our technique exploits network representations of the data to identify similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modeling techniques. An advantage of the proposed approach is that each dataset can initially be modeled independently (in parallel), before applying a fast post-processing step to perform data integration. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. We first demonstrate the applicability of our tool on artificial data, and then on examples from the literature, which include yeast cell cycle, breast cancer and sporadic inclusion body myositis datasets.

Details

Original languageEnglish
Pages (from-to)107-122
Number of pages16
JournalStatistical applications in genetics and molecular biology
Volume15
Issue number2
Publication statusPublished - 18 Mar 2016

Keywords

  • graph-theoretic methods, functional genomics, data integration, clustering

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