Deep learning of gene relationships from single cell time-course expression data
Ye Yuan, Ziv Bar‐Joseph
Abstract
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offer several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, the data also raise new computational challenges. Using a novel encoding for scRNA-Seq expression data, we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as 3D tensor and train convolutional and recurrent neural networks for predicting interactions. We tested our time-course deep learning (TDL) models on five different time-series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time-series scRNA-Seq data.