High throughput spectral data processing

high-throughput-spectral-data-processing

Summary

Process and cluster millions of mass spectra using GPU-accelerated hyperdimensional computing to project spectra into binary hypervector space, enabling sub-15-minute runtimes on datasets that would otherwise require hours. This skill is essential when proteomics workflows must handle draft human proteome scales (25M+ spectra) with speed and clustering quality constraints.

When to use

When you have MGF-formatted mass spectrometry peak files (typically from proteomics experiments) numbering in the millions of spectra that require grouping into similar MS/MS clusters, and runtime must be kept under 15 minutes rather than hours. Specifically triggered by need to cluster large proteome datasets (e.g., PXD000561 or equivalent 25M-spectra draft human proteome) on GPU-equipped infrastructure.

When NOT to use

• Input spectra are already in a pre-computed distance matrix or feature table format; use direct clustering instead.
• Dataset is small (<1M spectra) or runtime is not a constraint; simpler CPU-based clustering may suffice.
• GPU hardware (NVIDIA with CUDA support) is unavailable; HyperSpec requires GPU for its speed advantage.

Inputs

• MGF peak files (mass spectrometry data in text format with m/z and intensity pairs)
• Directory path containing one or more MGF files
• Spectrum metadata (precursor m/z, charge state, retention time, scan identifier)

Outputs

• CSV or parquet file with one spectrum per row and assigned cluster label
• Parquet metadata table with columns: bucket, precursor_charge, precursor_mz, identifier, scan, retention_time, cluster, is_representative
• Optional: representative spectra MGF file (if --representative_mgf flag enabled)
• Optional: checkpoint file with encoded hypervectors for checkpointing

How to apply

Load MGF files from your input directory into HyperSpec with CUDA GPU acceleration enabled. Apply level-id encoding to project each preprocessed spectrum into a binary hypervector of ultra-high dimension (default 2048 dimensions, adjustable via --hd_dim). Configure preprocessing CPU cores (--cpu_core_preprocess, default 6) to handle spectrum intensity filtering, m/z range clipping (--min_mz, --max_mz), and peak count filtering (--min_peaks). Divide the encoded dataset into small spatial buckets and compute pairwise Hamming distance matrices on GPU. Finally, apply DBSCAN or hierarchical clustering (--cluster_alg, default hc_complete) with a distance threshold (--eps, typically 0.2–0.4) and optional post-clustering refinement (--refine flag). Monitor that end-to-end runtime remains <15 minutes and verify cluster labels are assigned to each spectrum in the output parquet file.

Related tools

• HyperSpec (GPU-accelerated hyperdimensional encoding and DBSCAN/hierarchical clustering engine for mass spectra) — https://github.com/wh-xu/Hyper-Spec
• Python 3.8+ (Runtime environment for HyperSpec)
• CUDA (GPU compute framework required for HyperSpec acceleration)

Examples

python src/main.py ~/dataset/ ./output.csv --cpu_core_preprocess=4 --cluster_alg dbscan --use_gpu_cluster --cluster_charges 2 3 --eps=0.2 --refine

Evaluation signals

• End-to-end runtime on 25M spectra dataset completes in <15 minutes (vs. baseline hours without GPU acceleration).
• Output parquet file contains one row per spectrum with valid cluster label (integer ≥ 0) and is_representative boolean flag.
• Clustering quality preserved: within-cluster Hamming distances should be lower than between-cluster distances; representative spectra should be marked correctly.
• Spectrum count in output matches input spectrum count; no spectra dropped or duplicated during encoding or clustering.
• Metadata columns (precursor_charge, precursor_mz, retention_time, scan identifier) are preserved and non-null in output.

Limitations

• Clustering for the largest datasets (PXD000561, 25M spectra) requires GTX 3090 or equivalent high-memory GPU; smaller GPUs (e.g., GTX 1080Ti) only support smaller datasets.
• Hypervector dimension (--hd_dim) and quantization level (--hd_Q) are fixed at encode time; altering them requires re-encoding, not reuse of cached hypervectors.
• DBSCAN threshold (--eps) and hierarchical clustering linkage method are user-tunable but lack automated parameter selection; suboptimal choices may yield poor clusters or over-fragmentation.
• Performance gains are heavily dependent on SSD storage speed; the README recommends high-performance SSDs for best I/O throughput.

Evidence

• [intro] HyperSpec shortens the runtime on the draft human proteome dataset with 25 million spectra from a few hours to <15 minutes.: "HyperSpec shortens the runtime on the draft human proteome dataset with 25 million spectra from a few hours to <15 minutes."
• [intro] HyperSpec adopts brain-inspired hyperdimensional computing to project spectra data into binary hyperdimensional space.: "HyperSpec adopts the brain-inspired hyperdimensional (HD) computing to project the spectra data into binary hyperdimensional space to obtain better clustering quality and faster clustering speed."
• [readme] HyperSpec requires Python 3.8+ with CUDA environment and GPU support.: "HyperSpec requires Python 3.8+ with CUDA environment. A GPU should be installed properly."
• [readme] Clustering for PXD000561 dataset requires GTX 3090 with larger memory.: "Clustering for PXD000561 dataset requires GTX 3090 with larger memory"
• [readme] HyperSpec takes MGF peak files as input and exports clustering result as CSV with spectrum and cluster label.: "HyperSpec supports running using the command line and takes MGF peak files as input and exports the clustering result as a CSV file with each MS/MS spectrum and its cluster label on a single line."
• [readme] HyperSpec encodes spectra into binary hypervectors, computes Hamming distance matrices per bucket, then clusters with DBSCAN.: "HyperSpec first encodes the processed spectra into binary hypervector (HV) with ultra-high dimension (>1000) based on level-id encoding method. The entire dataset is divided into small buckets and"
• [readme] Output format is compressed parquet with spectrum metadata and cluster assignment.: "The exported meta data for clustering results are compressed and stored in parquet file, which records bucket, precursor_charge, precursor_mz, identifier, scan, retention_time,"
• [readme] High-performance SSD is recommended for best performance.: "We recommend using high-performance SSD as the storage device for the best performance."