Multiview Graph Learning for Single-Cell RNA Sequencing Data

• Activity Number: 418 - Statistical Methods for Single Cell Genomics and Spatial Transcriptomics
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: International Indian Statistical Association
• Author(s): Satabdi Saha* and Abdullah Karaaslanli and Selin Aviyente and Tapabrata Maiti
• Companies: Michigan State University and Michigan State University and Michigan State University and Michigan State University
• Keywords: Single Cell; Gene Regulatory Network; Graph Learning; Graph Signal Processing; Multiple Graph Learning; Kernelized Graph Learning

Abstract

Characterizing the underlying topology of gene regulatory networks (GRNs) is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most algorithms aimed at single cell GRN reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell GRN under different but related conditions we propose the joint estimation of multiple networks using kernelized multi-view graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. In this talk, I will present mvGL and demonstrate its superior performance over state-of-the-art methods using synthetic and real data examples.