Delineation of complex gene expression patterns in single cell RNA-seq data with ICARUS v2.0

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Complex biological traits and diseases are influenced by gene expression patterns.
• Single-cell RNA sequencing (scRNA-seq) is a powerful technique for analyzing these patterns at a high resolution.
• Existing analysis tools may lack comprehensive integration for network and trait association studies.

Purpose of the Study:

• To present ICARUS v2.0, an enhanced web server for scRNA-seq data analysis.
• To integrate advanced tools for investigating gene networks, regulation, and biological traits.
• To provide a user-friendly platform for comprehensive scRNA-seq data exploration.

Main Methods:

• Integration of MEGENA for gene co-expression analysis.
• Inclusion of SCENIC for transcription factor network identification.
• Incorporation of Monocle3 for trajectory analysis and CellChat for cell-cell communication.
• Utilisation of MAGMA for Genome-Wide Association Studies (GWAS) trait association.
• Comparison of differentially expressed genes with the Drug-Gene Interaction database (DGIdb 4.0).

Main Results:

• ICARUS v2.0 offers a comprehensive suite of integrated tools for scRNA-seq analysis.
• The platform facilitates the investigation of gene networks and regulatory patterns.
• Users can explore associations between cell clusters and GWAS traits.
• Potential drug targets can be identified by comparing gene expression data with drug interaction databases.

Conclusions:

• ICARUS v2.0 provides an efficient and user-friendly web server for advanced scRNA-seq analysis.
• The integrated tools empower researchers to uncover complex biological insights.
• This updated platform supports biological discovery and accelerates drug development efforts.