SCGRNs: Novel supervised inference of single-cell gene regulatory networks of complex diseases

Abstract

Single-cell gene regulatory network (SCGRN) inference refers to the process of inferring gene regulatory networks from single-cell data, which are generated via single-cell RNA-sequencing (scRNA-seq) technologies. Although scRNA-seq leads to the generation of data pertaining to cells of particular interest, the single-cell data are noisy and highly sparse, which makes the analysis of such data a challenging task. In this study, we model an SCGRN as a directed graph where an edge from a source node (also called transcription factor (TF)) to a target node (also called target gene) indicates that a TF regulates a target gene. Inferring the SCGRN via predicting TF-target gene regulations would help biologists better understand various diseases in terms of networks. Following the modeling step, we propose three machine learning approaches. The first approach considers feature vectors encoding regulatory relationships of expressed TFs-target genes as input. The resulting model is then used to predict unseen TF-target gene regulations. The second machine learning approach constructs new feature vectors via incorporating features obtained from stacked autoencoders, which are provided to a machine learning algorithm to induce a model and predict unseen regulations of TFs-target genes. The third approach extends the second approach via including topological features extracted from an SCGRN. We perform an experimental study comparing our approaches against adapted unsupervised approaches. Experimental results on SCGRNs pertaining to healthy and type 2 pancreatic diabetes demonstrate the clinical importance and the accurate prediction performance of the proposed approaches.