Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene coexpression networks (GCNs) are crucial for inferring gene function and biological pathways.
• Bulk RNA-sequencing is increasingly replacing microarrays for gene expression quantification.
• New statistical methodologies are needed for robust GCN construction from RNA-Seq data.

Purpose of the Study:

• To explore and present popular methods for constructing GCNs using bulk RNA-Seq data.
• To provide insights into statistical approaches suitable for modern gene expression analysis.
• To demonstrate the application of these methods using the R programming language.

Main Methods:

• Review of established GCN construction algorithms.
• Discussion of distribution-based methods for network inference.
• Exploration of normalization techniques specific to RNA-Seq data.
• Implementation examples using the R statistical programming language.

Main Results:

• Identification of key statistical methods for GCN generation from RNA-Seq.
• Evaluation of different approaches for handling bulk RNA-Seq data characteristics.
• Demonstration of R package utility in constructing GCNs.

Conclusions:

• Effective GCN construction from bulk RNA-Seq data is feasible with appropriate statistical methods.
• The R programming language offers a versatile environment for implementing these analyses.
• Improved GCNs enhance the understanding of gene functions and biological systems.