A graph theoretical approach to data fusion
File(s)
Published version
Author(s)
Zurauskiene, J
Kirk, PDW
Stumpf, MPH
Type
Journal Article
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.
Date Issued
2016-03-18
Date Acceptance
2016-03-18
Citation
Statistical Applications in Genetics and Molecular Biology, 2016, 15 (2), pp.107-122
ISSN
1544-6115
Publisher
De Gruyter
Start Page
107
End Page
122
Journal / Book Title
Statistical Applications in Genetics and Molecular Biology
Volume
15
Issue
2
Copyright Statement
© 2016, Michael P.H. Stumpf et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Sponsor
The Leverhulme Trust
Human Frontier Science Program
Biotechnology and Biological Sciences Research Council (BBSRC)
Grant Number
F/07 058/BP
RGP0061/2011
BB/K017284/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biochemistry & Molecular Biology
Mathematical & Computational Biology
Statistics & Probability
Mathematics
clustering
data integration
functional genomics
graph-theoretic methods
INCLUSION-BODY MYOSITIS
PROTEIN-INTERACTION NETWORKS
MITOTIC CELL-CYCLE
BIOLOGICAL NETWORKS
DATA INTEGRATION
GENOMIC DATA
COMPLEXES
INFERENCE
BREAST
VISUALIZATION
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
Publication Status
Published