imspy-dia

DIA processing and clustering for timsTOF IMS data.

Overview

imspy-dia provides tools for Data-Independent Acquisition (DIA) data analysis from Bruker timsTOF instruments:

• Hierarchical DIA feature extraction - Extract MS1 precursor and MS2 fragment clusters
• Pseudo spectrum construction - Build pseudo spectra from correlated precursor-fragment pairs
• Isotopic feature grouping - Group isotope clusters into features with charge state assignment

Installation

pip install imspy-dia

Dependencies

• imspy-core>=0.4.0 - Core data structures
• imspy-connector>=0.4.0 - Rust bindings
• pandas>=2.0.0
• numpy>=1.24.0

Optional dependencies:

• matplotlib - For cluster reports (pip install imspy-dia[reporting])
• torch - For neural network-based extraction (pip install imspy-dia[torch])

Quick Start

from imspy_dia import TimsDatasetDIAClustering, build_simple_features_from_clusters

# Open a DIA dataset with clustering capabilities
ds = TimsDatasetDIAClustering("/path/to/data.d")

# Plan TOF-scan windows for MS1
plan = ds.plan_tof_scan_windows(tof_step=8, rt_window_sec=30.0, rt_hop_sec=15.0)

# Extract precursor clusters (requires peak detection)
# ... see full documentation for peak detection workflow

# Build isotopic features from precursor clusters
features = build_simple_features_from_clusters(precursor_clusters)

# Build pseudo spectra
pseudo_spectra = ds.build_pseudo_spectra_geom(
    ms1_clusters=precursor_clusters,
    ms2_clusters=fragment_clusters,
    features=features,
)

CLI Tools

Cluster Report

Generate a PDF report summarizing cluster statistics:

imspy-cluster-report --in clusters.parquet --out report.pdf --json summary.json

License

MIT License - see LICENSE file for details.