scholarly journals EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data

2015 ◽  
Vol 31 (19) ◽  
pp. 3198-3206 ◽  
Author(s):  
Chalini D. Wijetunge ◽  
Isaam Saeed ◽  
Berin A. Boughton ◽  
Jeffrey M. Spraggins ◽  
Richard M. Caprioli ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Peak Picking ◽  
Analysis Pipeline ◽  
Data Analysis Pipeline ◽  
Imaging Mass Spectrometry Data

scholarly journals Evaluation of Data Analysis Platforms and Compatibility with MALDI-TOF Imaging Mass Spectrometry Data Sets

2020 ◽  
Vol 31 (6) ◽  
pp. 1313-1320
Author(s):  
Gordon T. Luu ◽  
Alanna R. Condren ◽  
Lisa Juliane Kahl ◽  
Lars E.P. Dietrich ◽  
Laura M. Sanchez
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Data Sets ◽  
Maldi Tof ◽  
Evaluation Of Data ◽  
Imaging Mass Spectrometry Data

Super-resolution segmentation of imaging mass spectrometry data: Solving the issue of low lateral resolution

2011 ◽  
Vol 75 (1) ◽  
pp. 237-245 ◽  
Author(s):  
T. Alexandrov ◽  
S. Meding ◽  
D. Trede ◽  
J.H. Kobarg ◽  
B. Balluff ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Imaging Mass Spectrometry ◽  
Super Resolution ◽  
Mass Spectrometry Data ◽  
Lateral Resolution ◽  
Imaging Mass Spectrometry Data

scholarly journals Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

2020 ◽  
Author(s):  
Leonoor E.M. Tideman ◽  
Lukasz G. Migas ◽  
Katerina V. Djambazova ◽  
Nathan Heath Patterson ◽  
Richard M. Caprioli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Molecular Species ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Decision Making Process ◽  
Spatial Specificity ◽  
The Relationship ◽  
Insight Into ◽  
Imaging Mass Spectrometry Data

AbstractThe search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species’ biomarker potential, our workflow delivers spatially localized explanations of the classification model’s decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species’ potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.HighlightsOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class.A model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class.By providing spatially localized explanations for a classification model’s decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative.

scholarly journals Modeling of Imaging Mass Spectrometry Data and Testing by Permutation for Biomarkers Discovery in Tissues

2015 ◽  
Vol 51 ◽  
pp. 693-702 ◽  
Author(s):  
Michal Marczyk ◽  
Grzegorz Drazek ◽  
Monika Pietrowska ◽  
Piotr Widlak ◽  
Joanna Polanska ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Imaging Mass Spectrometry Data

Robust Simulation Of Imaging Mass Spectrometry Data

2021 ◽  
Author(s):  
Anastasia Sarycheva ◽  
Anton Grigoryev ◽  
Evgeny N. Nikolaev ◽  
Yury Kostyukevich
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Mass Spectrometry Imaging ◽  
Imaging Mass Spectrometry ◽  
Real Data ◽  
Sample Surface ◽  
Mass Spectrometry Data ◽  
Ion Composition ◽  
Simulation Parameters ◽  
Imaging Mass Spectrometry Data

Mass spectrometry imaging (MSI) with high resolution in mass and space is an analytical method that produces distributions of ions on a sample surface. The algorithms for preprocessing and analysis of the raw data acquired from a mass spectrometer should be evaluated. To do that, the ion composition at every point of the sample should be known. This is possible via the employment of a simulated MSI dataset. In this work, we suggest a pipeline for a robust simulation of MSI datasets that resemble real data with an option to simulate the spectra acquired from any mass spectrometry instrument through the use of the experimental MSI datasets to extract simulation parameters.

Abstract 3963: New software for imaging mass spectrometry data

2012 ◽  
Author(s):  
Masaaki Matsuura ◽  
Masaru Ushijima ◽  
Akiko Yuba-Kubo ◽  
Masatoshi Wakui ◽  
Mitsuyo Ohmura ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Imaging Mass Spectrometry Data

Robust Algorithm for Alignment of Liquid Chromatography−Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline

2006 ◽  
Vol 78 (21) ◽  
pp. 7397-7409 ◽  
Author(s):  
Navdeep Jaitly ◽  
Matthew E. Monroe ◽  
Vladislav A. Petyuk ◽  
Therese R. W. Clauss ◽  
Joshua N. Adkins ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Data Analysis ◽  
Accurate Mass ◽  
Liquid Chromatography Mass Spectrometry ◽  
Analysis Pipeline ◽  
Robust Algorithm ◽  
Chromatography Mass Spectrometry ◽  
Data Analysis Pipeline
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