Open AccessEvaluation of Data Analysis Platforms and Compatibility with MALDI-TOF Imaging Mass Spectrometry Data Sets
Author(s) -
Gordon T Luu,
Alanna R. Condren,
Lisa Juliane Kahl,
Lars E. P. Dietrich,
Laura M. Sanchez
Publication year - 2020
Publication title -
journal of the american society for mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.961
H-Index - 127
eISSN - 1879-1123
pISSN - 1044-0305
DOI - 10.1021/jasms.0c00039
Subject(s) - mass spectrometry imaging , preprocessor , mass spectrometry , software , maldi imaging , chemistry , analyte , cluster analysis , data mining , matrix assisted laser desorption/ionization , pattern recognition (psychology) , biological system , computer science , computational biology , artificial intelligence , chromatography , organic chemistry , adsorption , desorption , biology , programming language
Imaging mass spectrometry (IMS) has proven to be a useful tool when investigating the spatial distributions of metabolites and proteins in a biological system. One of the biggest advantages of IMS is the ability to maintain the 3D chemical composition of a sample and analyze it in a label-free manner. However, acquiring the spatial information leads to an increase in data size. Due to the increased availability of commercial mass spectrometers capable of IMS, there has been an exciting development of different statistical tools that can help decipher the spatial relevance of an analyte in a biological sample. To address this need, software packages like SCiLS and the open source R package Cardinal have been designed to perform unbiased spectral grouping based on the similarity of spectra in an IMS data set. In this note, we evaluate SCiLS and Cardinal compatibility with MALDI-TOF IMS data sets of the Gram-negative pathogen Pseudomonas aeruginosa PA14. Both software were able to perform unsupervised segmentation with similar performance. There were a few notable differences which are discussed related to the identification of statistically significant features which required optimization of preprocessing steps, region of interest, and manual analysis.