FiT: fiber-based tensor completion for drug repurposing

Aysegul Bumin, Anna M. Ritz, Donna K. Slonim, Tamer Kahveci, Kejun Huang

Abstract: Drug repurposing aims to find new uses for existing drugs. One drug repurposing approach, called "Connectivity Mapping," links transcriptomic profiles of drugs to profiles characterizing disease states. However, experimentally evaluating the transcriptomic effects of drug exposure in particular cells is a costly process. Characterizing drug-cell combinations widely is further hindered because primary tissue samples may not be abundant, leading to many gaps in drug-cell databases. To best find drugs relevant for particular conditions, we may therefore want to impute the transcriptomic impact of a given drug on an unassayed cell type or types. This step deviates from classic data completion problems, however, because of the fundamental bottleneck that state of the art data imputation techniques for this problem do not consider the unique characteristics of the data. The missing values in the data are not randomly distributed, and the genes are not independent entities, but rather they interact with and affect the transcription rates of one another. Here, we address the first and one of the most fundamental parts of the connectivity map data imputation problem to enable drug repurposing. We develop a novel method, named FiT (Fiber-based Tensor Completion) to impute the transcription values for missing drug-cell line combinations in a highly sparse drug-cell line dataset accurately and efficiently, while exploiting the distribution of missing values as well as the interactions among genes. Our results demonstrate that even on a sparse dataset, where approximately 75% of the data is missing, FiT outperforms existing approaches and obtains more accurate results in a significantly shorter amount of time.