Title:Five Years of Gene Networks Modeling in Single-cell RNA-sequencing
Studies: Current Approaches and Outstanding Challenges
Volume: 17
Issue: 10
Author(s): Samarendra Das*, Upendra Pradhan and Shesh N. Rai*
Affiliation:
- ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar 752050, Odisha, India
- International Centre for
Foot and Mouth Disease, Arugul, Bhubaneswar 752050, Odisha, India
- ICAR-Indian Agricultural Statistics Research
Institute, PUSA, New Delhi 110012, India
- Biostatistics and Bioinformatics Facility, Brown Cancer Center, University
of Louisville, Louisville, KY 40202, USA
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville,
KY 40202, USA
- School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY
40292, USA
- Biostatisitcs and Informatics Facility, Center for Integrative Environmental Health Sciences, University of
Louisville, Louisville, KY 40202, USA
- Data Analysis and Sample Management Facility, University of Louisville Super
Fund Center, University of Louisville, Louisville, KY 40202, USA
- Hepatobiology and Toxicology Center, University of
Louisville, Louisville, KY 40202, USA
- Christina Lee Brown Envirome Institute, University of Louisville, Louisville,
KY 40202, USA
Keywords:
Single-cell, scRNA-seq, gene networks, models, classification, statistical approach, challenges.
Abstract: Single-cell RNA-sequencing (scRNA-seq) is a rapidly growing field in transcriptomics,
which generates a tremendous amount of gene expression data at the single-cell level. Improved statistical
approaches and tools are required to extract informative knowledge from such data. Gene network
modeling and analysis is one such approach for downstream analysis of scRNA-seq data. Therefore,
newer and innovative methods have been introduced in the literature. These approaches greatly vary in
their utility, basic statistical concepts, models fitted to the data, etc. Therefore, we present a comprehensive
overview of the available approaches for gene network modeling and analysis in single-cell studies,
along with their limitations. We also classify the approaches based on different statistical principles and
present a class-wise review. We discuss the limitations that are specific to each class of approaches and
how they are addressed by subsequent classes of methods. We identify several biological and methodological
challenges that must be addressed to enable the development of novel and innovative single-cell
gene network inference approaches and tools. These new approaches will be able to analyze the singlecell
data efficiently and accurately to better understand the biological systems, increasing the specificity,
sensitivity, utility, and relevance of single-cell studies. Furthermore, this review will serve as a catalog
and provide guidelines to genome researchers and experimental biologists for objectively choosing
the better gene network modeling approach.
