ClusterGVis: An Advanced Visualization and Clustering Tool for Gene Expression Analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Interpreting large-scale gene expression data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) is crucial for understanding biological systems.
• Effective analysis and visualization tools are essential for extracting meaningful insights from complex transcriptomic datasets.

Purpose of the Study:

• To introduce ClusterGVis, an advanced bioinformatics software designed to streamline the analysis and visualization of gene expression data.
• To provide researchers with a user-friendly platform for uncovering patterns and relationships within transcriptomic datasets.

Main Methods:

• Utilizes fuzzy c-means and k-means clustering algorithms for transcriptomic data analysis.
• Features integrated heatmap visualization for exploring co-expression networks and identifying differentially expressed genes.
• Offers a user-friendly interface for simplified data interpretation.

Main Results:

• Enables effective identification of patterns and relationships in complex gene expression profiles.
• Facilitates intuitive exploration of co-expression networks and identification of differentially expressed genes.
• Supports the identification of potential biomarkers and enhances understanding of gene function.

Conclusions:

• ClusterGVis simplifies complex gene expression data analysis and visualization.
• The software aids in biomarker discovery and elucidating gene regulatory mechanisms.
• Publicly available resources, including tutorials and a Shiny App, enhance accessibility and usability.