falcon-clust

falcon-clust clusters millions of MS/MS spectra by converting binned high-resolution spectra into low-dimensional vectors via feature hashing, building nearest neighbor indexes to compute sparse pairwise distance matrices within precursor mass tolerance, and applying density-based clustering to produce high-purity clusters for downstream proteomics analysis.

Key Features:

• High-resolution spectrum processing: Bins high-resolution MS/MS spectra and converts them into low-dimensional vectors using feature hashing to preserve essential spectral features while reducing dimensionality.
• Fast nearest neighbor searching: Constructs nearest neighbor indexes from spectrum vectors to enable rapid similarity searches and avoid exhaustive pairwise comparisons within the relevant precursor mass tolerance.
• Sparse pairwise distance matrix computation and clustering: Computes a sparse pairwise distance matrix from nearest neighbor results and applies density-based clustering to group similar spectra into high-purity clusters.

Scientific Applications:

• Proteomics: Supports protein identification and detection of post-translational modifications (PTMs) from large-scale MS/MS datasets.
• Data reduction for downstream analysis: Reduces MS/MS data volume while maintaining cluster purity to improve accuracy of protein identification and quantification.
• Large-scale MS/MS processing: Enables efficient processing and interpretation of millions of spectra from mass spectrometry experiments.

Methodology:

Feature hashing of binned high-resolution spectra into low-dimensional vectors; construction of nearest neighbor indexes to perform fast similarity searches and avoid exhaustive comparisons within precursor mass tolerance; calculation of a sparse pairwise distance matrix; and application of density-based clustering to form spectrum clusters.