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Rectified factor networks for biclustering of omics data
Djork-Arné Clevert1, Thomas Unterthiner2, Gundula Povysil2
1Bioinformatics Department, Bayer AG, Berlin, Germany.
Bioinformatics (Oxford, England)
|September 9, 2017
Summary
Rectified Factor Networks (RFNs) offer a novel deep learning approach to biclustering, overcoming limitations of traditional methods like FABIA. RFNs demonstrate superior performance in identifying complex patterns in large datasets, including gene expression and human genomic data.
Area of Science:
- Computational biology
- Bioinformatics
- Machine learning
Background:
- Biclustering is crucial for analyzing large sample-feature datasets in life sciences and e-commerce.
- Existing methods like FABIA have limitations in scalability and determining sample membership.
- FABIA's computational complexity restricts it to ~20 code units.
Purpose of the Study:
- To introduce Rectified Factor Networks (RFNs) as an unsupervised deep learning approach for biclustering.
- To overcome the limitations of existing biclustering methods, particularly FABIA.
- To leverage RFNs for efficient, sparse, and non-linear data representation.
Main Methods:
- Utilized Rectified Factor Networks (RFNs), an unsupervised deep learning model.
- RFNs construct sparse, non-linear, high-dimensional representations via posterior means.
- RFN learning employs a generalized alternating minimization algorithm with posterior regularization.
Main Results:
- RFN outperformed 13 other biclustering methods, including FABIA, on 400 benchmark and 3 gene expression datasets.
- RFN identified significant DNA segments in the 1000 Genomes Project data related to ancient hominin interbreeding.
- The method demonstrated superior performance in identifying known clusters in gene expression data.
Conclusions:
- RFNs provide an effective and scalable solution for biclustering complex biological and genomic data.
- The deep learning approach offers advantages over traditional biclustering algorithms.
- RFNs have potential applications in discovering novel biological insights from large-scale datasets.
