Format

Send to

Choose Destination
Bioinformatics. 2016 Aug 15;32(16):2457-63. doi: 10.1093/bioinformatics/btw207. Epub 2016 Apr 19.

Sparse group factor analysis for biclustering of multiple data sources.

Author information

1
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland.

Abstract

MOTIVATION:

Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments. These biclustering techniques have focused on one data source, often gene expression data. We present a Bayesian approach for joint biclustering of multiple data sources, extending a recent method Group Factor Analysis to have a biclustering interpretation with additional sparsity assumptions. The resulting method enables data-driven detection of linear structure present in parts of the data sources.

RESULTS:

Our simulation studies show that the proposed method reliably infers biclusters from heterogeneous data sources. We tested the method on data from the NCI-DREAM drug sensitivity prediction challenge, resulting in an excellent prediction accuracy. Moreover, the predictions are based on several biclusters which provide insight into the data sources, in this case on gene expression, DNA methylation, protein abundance, exome sequence, functional connectivity fingerprints and drug sensitivity.

AVAILABILITY AND IMPLEMENTATION:

http://research.cs.aalto.fi/pml/software/GFAsparse/

CONTACTS:

: kerstin.bunte@googlemail.com or samuel.kaski@aalto.fi.

PMID:
27153643
DOI:
10.1093/bioinformatics/btw207
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center