A Flexible Adaptive Stable Clustering Algorithm for Archive-Scale Online Mass Spectrometry

TL;DR

The paper introduces FASC, a scalable, stable clustering algorithm for large-scale mass spectrometry data, enabling detailed environmental analysis with linear runtime and high accuracy.

Contribution

FASC decouples similarity measures from optimization, ensuring deterministic convergence and scalability for massive environmental mass spectrometry datasets.

Findings

Achieved >99.5% cluster purity and 0.99 Adjusted Rand Index on ground truth data.

Demonstrated linear runtime scaling (O(N)) on 25 million spectra.

Successfully mapped atmospheric aerosol aging pathways and identified rare industrial tracers.

Abstract

Modern online mass spectrometry generates multi-terabyte data streams critical for understanding Earth's environmental systems. However, extracting actionable chemical insights from these repositories is impeded by a computational bottleneck: existing clustering methods force a compromise among scalability, metric flexibility, and algorithmic stability. Here, we introduce Flexible Adaptive Stable Clustering (FASC), a dynamical systems framework that resolves these constraints by architecturally decoupling the similarity kernel from rigorous optimization logic. Unlike legacy heuristics that suffer from stochastic drift and algorithmic blending, FASC employs a Density-Augmented Similarity Selection rule and geometric constraints to guarantee deterministic, order-independent convergence. After validating FASC on canonical machine-learning ground truths (achieving >99.5% cluster purity and 0.99 Adjusted Rand Index), we deployed the framework on 25 million mass spectra of atmospheric aerosols. Demonstrating strictly linear empirical runtime scaling (O(N)), FASC autonomously mapped atmospheric aging pathways of secondary inorganic aerosols while isolating ultra-rare industrial tracers (<0.2% abundance), providing a scalable infrastructure for mining environmental big data.