Wesley Tansey

Abstract Comprehensively studying metabolism requires metabolite measurements. Such measurements, however, are often unavailable in large cohorts of tissue samples. To address this basic barrier, we propose a Bayesian framework (‘UnitedMet’) that leverages RNA–metabolite covariation to impute otherwise unmeasured metabolite levels from widely available transcriptomic data. UnitedMet is equally capable of imputing whole pool sizes and outcomes of isotope tracing experiments.

The authors have declared no competing interest. This project was generously supported by the Cycle for Survival, the Marie-Josee and Henry R. Kravis Center for Molecular Oncology and the National Cancer Institute Cancer Center Core (grant P30-CA008748) supporting Memorial Sloan Kettering Cancer Center.