Buchumschlag

pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning

Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and conve...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Cordes, Jonas (VerfasserIn) , Enzlein, Thomas (VerfasserIn) , Hopf, Carsten (VerfasserIn) , Wolf, Ivo (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: March 2024
In: Bioinformatics
Year: 2024, Jahrgang: 40, Heft: 3, Pages: 1-3
ISSN:1367-4811
DOI:10.1093/bioinformatics/btae133
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1093/bioinformatics/btae133
Volltext
Verfasserangaben:Jonas Cordes, Thomas Enzlein, Carsten Hopf, Ivo Wolf
Beschreibung
Zusammenfassung:Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with a focus on memory-efficient handling, processing and convenient data-access for DL applications. pyM2aia provides interfaces to its parent application M2aia, which offers interactive capabilities for exploring and annotating MSI data in imzML format. pyM2aia utilizes the image input and output routines, data formats, and processing functions of M2aia, ensures data interchangeability, and enables the writing of readable and easy-to-maintain DL pipelines by providing batch generators for typical MSI data access strategies. We showcase the package in several examples, including imzML metadata parsing, signal processing, ion-image generation, and, in particular, DL model training and inference for spectrum-wise approaches, ion-image-based approaches, and approaches that use spectral and spatial information simultaneously.Python package, code and examples are available at (https://m2aia.github.io/m2aia)
Beschreibung:Online verfügbar: 5. März 2024
Gesehen am 12.03.2025
Beschreibung:Online Resource
ISSN:1367-4811
DOI:10.1093/bioinformatics/btae133

Ähnliche Einträge