Isotopic envelope identification by analysis of the spatial distribution of components in MALDI-MSI data

• URL: http://arxiv.org/abs/2302.06051v2
• Date: Tue, 14 Feb 2023 21:39:44 GMT
• Title: Isotopic envelope identification by analysis of the spatial distribution of components in MALDI-MSI data
• Authors: Anna Glodek, Joanna Pola\'nska, Marta Gawin
• Abstract summary: This paper presents a method for identifying isotope envelopes in MALDI-ToF molecular imaging data based on the Mamdani-Assilan fuzzy system. The algorithm was tested on eight datasets obtained from the MALDI-ToF experiment on samples from patients with cancer of the head and neck region.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the significant steps in the process leading to the identification of proteins is mass spectrometry, which allows for obtaining information about the structure of proteins. Removing isotope peaks from the mass spectrum is vital and it is done in a process called deisotoping. There are different algorithms for deisotoping, but they have their limitations, they are dedicated to different methods of mass spectrometry. Data from experiments performed with the MALDI-ToF technique are characterized by high dimensionality. This paper presents a method for identifying isotope envelopes in MALDI-ToF molecular imaging data based on the Mamdani-Assilan fuzzy system and spatial maps of the molecular distribution of peaks included in the isotopic envelope. Several image texture measures were used to evaluate spatial molecular distribution maps. The algorithm was tested on eight datasets obtained from the MALDI-ToF experiment on samples from the National Institute of Oncology in Gliwice from patients with cancer of the head and neck region. The data were subjected to pre-processing and feature extraction. The results were collected and compared with three existing deisotoping algorithms. The analysis of the obtained results showed that the method for identifying isotopic envelopes proposed in this paper enables the detection of overlapping envelopes by using the approach oriented to study peak pairs. Moreover, the proposed algorithm enables the analysis of large data sets.

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