2021
DOI: 10.1038/s41598-021-84049-4
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Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring

Abstract: Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Nowadays, MSI is expanding into new domains such as clinical pathology. In order to increase the value of MSI data, software for visual analysis is required that is intuitive and technique independent. Here, we present QUIMBI (QUIck exploration tool for Multivariate BioImages) a new tool for the visual analysis of MSI data. QUIMBI is an interactive visual explo… Show more

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Cited by 3 publications
(1 citation statement)
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“…The MALDI-MSI data featured, respectively, 3,108 features in positive and 2,133 in negative ionization modes with 4 animals per Treatment x Development condition (i.e., PND 7, 14, 21, and 50 days for both CTL and PL). Individual data were (1) log-transformed to account for log-normal distribution by peak, (2) standardized to z-scores to give same weight to all peaks independently of abundance and (3) 5% winsorized to minimize the influence of individual outliers ( 58 ). Principal component analysis (PCA) of these data was performed to simultaneously visualize the correlation structure of features (biplot loadings) and the multivariate discrimination between conditions (biplot scores, average of 4 animals) ( 59 ).…”
Section: Methodsmentioning
confidence: 99%
“…The MALDI-MSI data featured, respectively, 3,108 features in positive and 2,133 in negative ionization modes with 4 animals per Treatment x Development condition (i.e., PND 7, 14, 21, and 50 days for both CTL and PL). Individual data were (1) log-transformed to account for log-normal distribution by peak, (2) standardized to z-scores to give same weight to all peaks independently of abundance and (3) 5% winsorized to minimize the influence of individual outliers ( 58 ). Principal component analysis (PCA) of these data was performed to simultaneously visualize the correlation structure of features (biplot loadings) and the multivariate discrimination between conditions (biplot scores, average of 4 animals) ( 59 ).…”
Section: Methodsmentioning
confidence: 99%