As current shotgun proteomics experiments can produce
gigabytes
of mass spectrometry data per hour, processing these massive data
volumes has become progressively more challenging. Spectral clustering
is an effective approach to speed up downstream data processing by
merging highly similar spectra to minimize data redundancy. However,
because state-of-the-art spectral clustering tools fail to achieve
optimal runtimes, this simply moves the processing bottleneck. In
this work, we present a fast spectral clustering tool, HyperSpec,
based on hyperdimensional computing (HDC). HDC shows promising clustering
capability while only requiring lightweight binary operations with
high parallelism that can be optimized using low-level hardware architectures,
making it possible to run HyperSpec on graphics processing units to
achieve extremely efficient spectral clustering performance. Additionally,
HyperSpec includes optimized data preprocessing modules to reduce
the spectrum preprocessing time, which is a critical bottleneck during
spectral clustering. Based on experiments using various mass spectrometry
data sets, HyperSpec produces results with comparable clustering quality
as state-of-the-art spectral clustering tools while achieving speedups
by orders of magnitude, shortening the clustering runtime of over
21 million spectra from 4 h to only 24 min.