Enhancing Deep Learning Inference of Gene Regulatory Networks via Construction of Image Representation of Cell-Cell...

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.