We are working on methods and tools for extracting information from large matrix-valued data (think very large spreadsheets) that are common in modern biology. Examples include technologies such as transcriptomics, metabolomics, and high-throughput genetic screens. We are using a number of complementary approaches including bilinear models, penalized regression, multivariate kernel regression, matrix factorization, and gradient-based optimization techniques.

We have developed a number of software packages in the Julia programming language, a new promising language for scientific computing and data science: MatrixLM/MatrixLMnet (penalized matrix linear models), FlxQTL (multivariate linear mixed models for genetic mapping), BulkLMM/LiteQTL (real-time eQTL mapping), and GeneNetworkAPI/MetabolomicsWorkbenchAPI (interface to GeneNetwork and Metabolomics Workbench databases and computational tools).