Enhancing Deep Learning Inference of Gene Regulatory Networks via Construction of Image Representation of Cell-Cell Interactions From scRNA-Seq Data.

Abstract

Understanding gene regulatory networks (GRNs) holds paramount importance for deciphering the intricate interplay among genes and their influence on biological processes and disease pathogenesis. The emergence of single-cell RNA sequencing (scRNA-seq) techniques has heralded a new era in GRN inference by capturing the nuanced heterogeneity and dynamic nature of gene expression at the single-cell level. However, extracting meaningful patterns from scRNA-seq measurements to infer GRNs poses significant challenges to existing methodologies due to the sheer scale and inherent complexity of the data. Here we propose a highly accurate and computationally efficient strategy for scRNA-seq-based GRN inference. Our approach leverages the underlying interactive relationships among the cells using state-of-the-art deep learning strategy. Specifically, a spatially semantic image representation, termed CelloGraph, is first introduced to portray the expressions of each gene across cells. The allocation of a cell to a spatial grid point of the CelloGraph is dictated by its interactions with other cells within the system, as determined by the maximization of system entropy of cell-cell interactions. Subsequently, the CelloGraphs of all pertinent genes are analyzed by using a customarily designed convolutional neural network (CNN) to discern discriminant patterns in the data and infer GRNs. The efficacy of the proposed approach is demonstrated through diverse real-world biomedical datasets. By harnessing the distinctive attributes of spatially semantic CelloGraphs and leveraging the unique pattern discovery capabilities of CNNs, our methodology paves the way for a deeper comprehension of the underlying mechanisms that govern gene expression and regulation. The proposed strategy not only overcomes challenges in scRNA-seq-based GRN inference but also promises to provide a more comprehensive understanding of intricate biological processes.