Comparative Analysis of Gene Regulatory Network Reconstruction Approaches for Single-Cell RNA Sequencing Data

• Activity Number: 81 - Contributed Poster Presentations: Section on Statistics in Epidemiology
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Satabdi Saha* and Tapabrata (Taps) Maiti and Sudin Bhattacharya
• Companies: Michigan State University and Michigan State University and Michigan State University
• Keywords: single cell RNA-seq; Gene Regulatory network

Abstract

Conventional bulk transcriptome analysis methods rely on the average gene expression obtained from an ensemble of identical or different cell types. However, differences in gene expression between individual cells, even of the same type, can influence the resulting behavior of a biological system. Single-cell exploration of gene expression is therefore essential to interpret the causes and consequences of this heterogeneity. However single cell profiling of cells often leads to low amounts of RNA transcript reads and stochasticity in gene expression resulting in a high proportion of zeroes or dropouts. Several methods have been proposed for reconstruction of gene regulatory networks in bulk and single-cell RNA sequencing data. In this study, we perform a comparative statistical analysis of four bulk methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in-silico simulated data.