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pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.
Cordes, Jonas; Enzlein, Thomas; Hopf, Carsten; Wolf, Ivo.
Afiliação
  • Cordes J; Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim 68163, Germany.
  • Enzlein T; Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany.
  • Hopf C; Center for Mass Spectrometry and Optical Spectroscopy, Mannheim University of Applied Sciences, Mannheim 68163, Germany.
  • Wolf I; Medical Faculty Mannheim, Heidelberg University, Mannheim 68167, Germany.
Bioinformatics ; 40(3)2024 Mar 04.
Article em En | MEDLINE | ID: mdl-38445753
ABSTRACT

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).
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Aprendizado Profundo Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Aprendizado Profundo Idioma: En Ano de publicação: 2024 Tipo de documento: Article