Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Genome-scale gene expression analysis is crucial for biomarker discovery and understanding molecular mechanisms.
• Recent omics technology advances allow for more complex analyses beyond standard differential expression (DE) methods.
• Investigating gene expression regulation requires advanced approaches to capture its multifactorial nature.

Purpose of the Study:

• To review conceptual and methodological advances in transcriptomic and multimodal omic data analysis for gene expression.
• To provide an overview of study designs, statistical testing, and considerations for gene expression analysis.
• To highlight strategies for inferring gene co-expression and regulatory networks, including machine learning and context-specific models.

Main Methods:

• Overview of gene expression study designs and statistical testing.
• Description of strategies for inferring gene co-expression and regulatory networks.
• Emphasis on context-specific network models and machine learning for gene expression regulation.
• Discussion of emerging modalities like single-cell and spatial transcriptomics.

Main Results:

• Advanced network-based analyses offer deeper insights into gene expression regulation.
• Context-specific network models and machine learning capture multifactorial gene expression.
• Single-cell and spatial transcriptomics provide high-resolution mapping of regulatory complexity.

Conclusions:

• Advanced network-based analyses are essential for comprehensive gene expression studies.
• Emerging omics technologies enhance the resolution and scope of gene expression investigations.
• This review encourages the application of advanced network analyses for biomarker discovery and mechanistic understanding.