Structured Sparse PCA to Identify Mirna Co-Regulatory Modules
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This paper presents a new mathematical formulation and the corresponding algorithms for structured sparse principal component analysis (PCA). We introduce a new concept of support matrices with structured prior based on Markov Random Field (MRF). Both the support matrices and principal components are regularized by the L1 norm to be integrated in a coupled objective function to recover the structured sparsity from the given data. Block coordinate descent and subgradient-based optimization methods are utilized to search for proper local minima for the formulated non-convex optimization problem. We implement the proposed methods to jointly analyze micro-RNA (miRNA) and gene interaction data to identify miRNA-gene co-regulatory modules (co-modules). Our preliminary experiments demonstrate that our structured sparse PCA has the potential to identify meaningful co-regulatory modules with enriched cellular functionalities. © 2014 IEEE.
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