Fast Parallel Construction of Correlation Similarity Matrices for Gene Co-Expression Networks on Multicore Clusters

Gene co-expression networks are gaining attention in the present days as useful representations of biologically interesting interactions among genes. The most computationally demanding step to generate these networks is the construction of the correlation similarity matrix, as all pairwise combinations must be analyzed and complexity increases quadratically with the number of genes. In this paper we present MPICorMat, a hybrid MPI/OpenMP parallel approach to construct similarity matrices based on Pearson's correlation. It is based on a previous tool (RMTGeneNet) that has been used on several biological studies and proved accurate. Our tool obtains the same results as RMTGeneNet but significantly reduces runtime on multicore clusters. For instance, MPICorMat generates the correlation matrix of a dataset with 61,170 genes and 160 samples in less than one minute using 16 nodes with two Intel Xeon Sandy-Bridge processors each (256 total cores), while the original tool needed almost 4.5 hours. The tool is also compared to another available approach to construct correlation matrices on multicore clusters, showing better scalability and performance. MPICorMat is an open-source software and it is publicly available at https://sourceforge.net/projects/mpicormat/.