Parallel Computation for Sparse Network Component Analysis

Abstract

The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network goal is determining the topological order of genes interactions. Moreover, the regulatory network is a vital for understanding genes influence on each other. However, the main challenge confronting gene regulatory network algorithms is the massive data size. Where, the algorithm runtime is relative to the data size. This paper presents a Parallel computation for Sparse Network Component Analysis (PSparseNCA) with application on gene regulatory network. PSparseNCA is a parallel version of SparseNCA. PSparseNCA enhanced the computation of SparseNCA using a distributed computing model. Where, the workload is distributed among P processing nodes, PSparseNCA is more efficient than SparseNCA. It achieved a better performance and its speedup reached 12.33. In addition, PsparseNCA complexity is O(NM2/P) instead of O(NM2) for SparseNCA.