ClusterFinder™ Intelligent Metabolomics Data Analysis
ClusterFinder™ transforms complex LC–MS data into accurate, reproducible biological insights with powerful tools for processing, visualization, normalization, and metabolite identification.
Powerful. Accurate. Effortless.
Traditional metabolomics workflows are often limited by ion suppression, batch effects, and inconsistent quantitation. IROA overcomes these challenges through isotope-based internal standards, automated data analysis, and confident metabolite identification, delivering reproducible and reliable results.
Powerful & Automated
Automate complex workflows including peak detection, alignment, normalization, and batch correction to save time and reduce errors.
Accurate & Confident
Leverage IROA technologies for suppression correction, isotopic pattern recognition, and metabolite identification.
Visualize & Explore
Interactive visualizations including chromatograms, PCA, heatmaps, and isotope envelopes for deeper biological insights.
Cloud-Based & Scalable
Process large datasets securely in the cloud with seamless updates and collaboration across your research teams.
Everything You Need in One Workspace
ClusterFinder™ provides a complete suite of tools to turn raw LC-MS data into meaningful biological insights.
- Chromatogram Viewer
- Peak Detection & Alignment
- Isotope Pattern Analysis
- Sample Comparison (PCA, Clustering)
- Batch Processing & Normalization
- Statistical Analysis & Visualization
- Reporting & Export
From Raw Data to Biological Insights
ClusterFinder™ streamlines every stage of metabolomics data analysis—from LC–MS data import and automated peak detection to normalization, metabolite identification, quantitation, visualization, and publication-ready results.
Raw LC-MS Data
Import data from various vendors and formats.
Import
Secure project creation and intelligent data import.
Peak Detection
Automatically detect and integrate peaks.
Normalization
Correct batch effects and normalize datasets.
Identification
Match metabolites using IROA libraries.
Quantitation
Accurate quantitation using internal standards.
Visualization
Create interactive plots and dashboards.
Export Results
Generate publication-ready reports and exports.
Precise Peak Detection from Raw LC-MS Data
ClusterFinder automatically detects, integrates, and validates chromatographic peaks using IROA's unique isotopic signatures. This allows genuine biological metabolites to be distinguished from noise, contaminants, and analytical artifacts. Every detected feature becomes a reliable starting point for identification, suppression correction, and quantitative analysis—reducing manual review while improving data quality and reproducibility.
Reveal Biological Patterns with Confidence
Interactive PCA and clustering tools help researchers visualize relationships between samples, identify outliers, and uncover meaningful biological trends across experiments. Since the data has already undergone suppression correction and Dual MSTUS normalization, clustering reflects biological variation rather than technical bias.
Visualize Expression Across Entire Datasets
Publication-ready heatmaps display metabolite abundance across every sample, making complex datasets easy to interpret. Hierarchical clustering reveals biomarkers, pathway activity, and metabolic relationships with suppression-corrected quantitative data.
Unique Molecular Signatures for Accurate Identification
IROA's patented isotopic labeling creates unique molecular signatures that simplify compound identification and molecular formula assignment. These predictable isotopic envelopes eliminate many false positives while providing multiple built-in quality checks during LC-MS analysis.
Quantitative Results You Can Trust
ClusterFinder combines an identity-verified informatics workflow with ion suppression correction and Dual MSTUS normalization to produce highly reproducible quantitative measurements. Raw, suppression-corrected, and normalized values provide a dependable foundation for statistical analysis and publication-quality metabolomics research.
Everything You Need to Know
Learn how ClusterFinder improves metabolomics workflows through isotope-based identification, suppression correction, normalization, and automated compound analysis.
ClusterFinder is designed specifically for IROA isotope-labeled workflows. Unlike traditional software that relies solely on peak intensity, it recognizes unique isotopic envelopes, automatically removes artifacts, corrects ion suppression, performs Dual MSTUS normalization, and identifies metabolites with built-in reference libraries.
The software analyzes characteristic IROA isotopic patterns together with authentic standards and the Long-Term Reference Standard (LTRS). This enables accurate molecular formula assignment, differentiates biological compounds from artifacts, and supports reliable metabolite identification even in complex datasets.
Ion suppression can significantly reduce signal intensity during LC-MS analysis. ClusterFinder compares the natural abundance and isotope-labeled internal standard signals to calculate suppression losses, allowing accurate correction before quantitation and downstream analysis.
Dual MSTUS is IROA's enhanced normalization strategy that uses both the natural abundance and isotope-labeled metabolite signals. This minimizes sample-to-sample variation, improves reproducibility, and enables direct comparison across experiments and batches.
Yes. ClusterFinder supports targeted and non-targeted metabolomics workflows, enabling compound identification, quantitation, visualization, suppression correction, normalization, and automated library matching within a single platform.
ClusterFinder supports IROA LC-MS datasets generated from biological samples including cells, tissues, plasma, urine, plant material, microbial cultures, and other metabolomics experiments using IROA internal standards.
Ready to Download ClusterFinder?
Access the latest version of ClusterFinder to perform accurate metabolomics data analysis with automated compound identification, ion suppression correction, Dual MSTUS normalization, and built-in IROA metabolite libraries.