GECO: gene expression clustering optimization app for non-linear data visualization of patterns.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Advances in sequencing technology generate vast amounts of omics data.
• Analyzing and visualizing this complex biological data remains a significant challenge.
• Existing code-based pipelines lack user-friendly interfaces for rapid data exploration.

Purpose of the Study:

• To develop an accessible application for rapid analysis and visualization of omics data.
• To provide a user-friendly, code-independent tool for biological data matrices.
• To enable quick evaluation of datasets and identification of genes of interest.

Main Methods:

• GECO (Gene Expression Clustering Optimization) is a GUI application.
• Utilizes non-linear reduction techniques (t-SNE, UMAP) for visualization.
• Accepts data matrices (e.g., RNA-seq, proteomics) with samples and expression levels.

Main Results:

• GECO rapidly visualizes expression trends and clusters genes/proteins by expression patterns.
• Interactive t-SNE or UMAP outputs enable visualization of expression trends.
• Customizable settings ensure adaptability for diverse biological data matrices.

Conclusions:

• GECO facilitates rapid, code-independent investigation of omics data matrices.
• Empowers researchers to uncover gene of interest and co-regulated gene programs.
• Supplements traditional statistical methods, particularly for visualizing gene trajectories across samples.