International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 3 (2016)

Biclustering of Omics Data using Rectified Factor Networks

Published
2016-02-28

Abstract

Biclustering has effectively been employed in biological sciences and e-commerce for medication design and recommender systems, respectively, and has become a prominent technique for evaluating big datasets presented as a matrix of samples times attributes. One of the most successful biclustering methods, Factor Analysis for Bicluster Acquisition (FABIA), is a generative model in which each bicluster is represented by two sparse membership vectors: one for the samples and one for the features. Due to the high computational complexity of computing the posterior, FABIA is limited to approximately 20 code units. Additionally, code units are not always sufficiently decorrelated, making sample membership difficult to determine. To circumvent the limitations of existing biclustering approaches, we propose using the recently introduced unsupervised Deep Learning algorithm Rectified Factor Networks (RFNs). RFNs use their posterior means to efficiently build exceedingly sparse, non-linear, high-dimensional representations of the input. RFN learning is a generalized alternating minimization approach that ensures non-negative and normalized posterior means and is based on the posterior regularization method. Each code unit represents a bicluster, consisting of samples for which the coding unit is active and features for which the code unit has activating weights. RFN beat 13 other biclustering algorithms, including FABIA, on four hundred benchmark datasets and three gene expression datasets with identified clusters. RFN was able to detect DNA sequences that imply interbreeding with other hominins that began before modern humans' ancestors left Africa, based on data from the 1000 Genomes Project.

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