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

Summary 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 a...

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Vydané v:Bioinformatics (Oxford, England) Ročník 40; číslo 3
Hlavní autori: Cordes, Jonas, Enzlein, Thomas, Hopf, Carsten, Wolf, Ivo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Oxford University Press 04.03.2024
Oxford Publishing Limited (England)
Predmet:
Applications Note
Availability
Data analysis
Data processing
Deep Learning
Image processing
Language
Mass spectrometry
Mass Spectrometry - methods
Mass spectroscopy
Metadata
Python
Scientific imaging
Signal generation
Signal processing
Software
Spatial data
ISSN:1367-4811, 1367-4803, 1367-4811
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Shrnutí:Summary 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. Availability and implementation Python package, code and examples are available at (https://m2aia.github.io/m2aia)
Bibliografia:SourceType-Scholarly Journals-1
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btae133