Cell-specific prediction and application of drug-induced gene expression profiles
Name
9789813235533_0004.pdf
Description
Published version
Size
1.64 MB
Format
Adobe PDF
Checksum (MD5)
541d3248ba29f9b95e5c89301c6e267f
Author(s)
Hodos, Rachel
Zhang, Ping
Lee, Hao-Chih
Duan, Qiaonan
Wang, Zichen
Clark, Neil R.
Ma’ayan, Avi
Wang, Fei
Kidd, Brian
Hu, Jianying
Date Issued
January 2018
Journal
Pacific Symposium on Biocomputing
Publisher
World Scientific
Citation
Hodos, Rachel et al. "Cell-specific prediction and application of drug-induced gene expression profiles." Pacific Symposium on Biocomputing, January 2018, Kohala Coast, Hawaii, World Scientific, January 2018 © 2017 The Authors
Version
Final published version
Abstract
Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1142/9789813235533_0004
