Background#R##N#High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation. With high-throughput sequencing technologies promising to produce even larger datasets per subject, we expect the performance of the state-of-the-art statistical algorithms to be further impacted unless efforts towards optimisation are carried out. MapReduce is a widely used high performance parallel framework that can solve the problem.
['Shicai Wang', 'Ioannis Pandis', 'David Johnson', 'Ibrahim Emam', 'Florian Guitton', 'Axel Oehmichen', 'Yike Guo']
Optimising parallel R correlation matrix calculations on gene expression data using MapReduce