Kernelized multiview signed graph learning for single-cell RNA sequencing data

Abdullah Karaaslanli, Satabdi Saha, Tapabrata Maiti, Selin Aviyente

Abstract: Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions than bulk and microarray datasets. However, cellular heterogeneity and sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most GRN reconstruction approaches estimate a single network for the entire data. This could cause potential loss of information when single cell datasets are generated from multiple treatment conditions/disease states.

External IDs:dblp:journals/bmcbi/KaraaslanliSMA23