ClusterChirp: A GPU-accelerated Web Server for Natural Language-Guided Interactive Visualization and Analysis of...

Area of Science:

• Bioinformatics
• Data Visualization
• Computational Biology

Background:

• Omics data matrices are growing, overwhelming current visualization tools.
• Existing tools often require downsampling or command-line expertise, hindering biological pattern discovery.
• Fragmented workflows impede downstream interpretation of omics data.

Purpose of the Study:

• To introduce ClusterChirp, a web-based platform for interactive exploration of large-scale data matrices.
• To enable real-time analysis and biological interpretation of high-dimensional omics data.
• To overcome limitations of existing tools by offering GPU acceleration and a natural language interface.

Main Methods:

• GPU-accelerated rendering and parallelized hierarchical clustering.
• Interactive features including on-the-fly clustering, multi-metric sorting, and feature search.
• A natural language interface powered by a Large Language Model for complex operations and workflow reproducibility.

Main Results:

• ClusterChimp supports real-time, interactive exploration of large omics data matrices.
• The platform facilitates on-the-fly clustering, sorting, and feature searching.
• Users can explore within-cluster correlation networks and perform functional enrichment analysis.

Conclusions:

• ClusterChirp empowers researchers to extract insights from high-dimensional omics data with unprecedented ease and speed.
• The platform adheres to FAIR4S principles, promoting data accessibility and reproducibility.
• ClusterChirp is freely available at clusterchirp.mssm.edu, requiring no login.