A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing
Hyeonkyu Kim1, Hwisoo Choi1, Daewon Lee2
1School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
Genes & Genomics
|November 30, 2023
Summary
Single-cell RNA sequencing (scRNA-seq) advances gene regulatory network (GRN) reconstruction by capturing dynamic cellular changes. Selecting the right GRN tool depends on whether cellular trajectory analysis is needed for specific research goals.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Gene regulatory networks (GRNs) are crucial for understanding cellular behavior.
- High-throughput transcriptome measurement technologies enable GRN reconstruction from gene expression data.
- Bulk RNA sequencing averages expression, limiting its ability to capture cell-specific dynamic changes.
Purpose of the Study:
- To review 15 gene regulatory network reconstruction tools.
- To discuss the strengths and limitations of these tools, especially with single-cell RNA sequencing (scRNA-seq).
Main Methods:
- Reviewing existing GRN reconstruction tools.
- Highlighting advancements in scRNA-seq for GRN analysis.
- Classifying tools based on their requirement for cellular trajectory data.
Main Results:
- scRNA-seq provides cell-specific snapshots, enhancing GRN reconstruction.
- Tools not requiring trajectory analysis excel at identifying regulator-target relationships.
- Tools utilizing trajectory analysis perform better in identifying key regulatory factors.
Conclusions:
- Researchers should choose GRN reconstruction tools aligned with their specific objectives.
- The choice depends on whether cellular trajectory analysis is necessary for the study.
- Advancements in scRNA-seq significantly improve the capabilities of GRN reconstruction.
