Gene clustering by structural prior based local factor analysis model under Bayesian Ying-Yang harmony learning

Abstract

We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments on the diagnostic research dataset show that BYY-spLFA outperforms the k-means clustering and single-link hierarchical clustering. The experiments on a lymphoma cancer datset further indicate the BYY-spLFA is able to uncover the number of phenotypes correctly and cluster the phenotypes more accurately. In addition, we modify BYY-spLFA to implement supervised learning and preliminarily demonstrate its effectiveness on a Leukemia data for classification.