ParallelPC: An R Package for Efficient Causal Exploration in Genomic Data

Discovering causal relationships from genomic data is the ultimate goal in gene regulation research. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. However, their applications in bioinformatics are still limited due to their high computational complexity. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms and 12 different conditional independence tests for each. The parallelised algorithms help speed up the procedure of experimenting large biological datasets and reduce the memory used when running the algorithms. Our experiment results on a real gene expression dataset show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a personal multicore computer. We present some typical applications in bioinformatics using different algorithms in ParallelPC. ParallelPC is available in CRAN repository at https://cran.r-project.org/web/packages/ParallelPC/index.html Refereed/Peer-reviewed