Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas

2014 ◽  
Vol 86 (8) ◽  
pp. 3947-3954 ◽  
Author(s):  
Walid M. Abdelmoula ◽  
Ricardo J. Carreira ◽  
Reinald Shyti ◽  
Benjamin Balluff ◽  
René J. M. van Zeijl ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Brain Atlas ◽  
Data Sets ◽  
Automatic Registration ◽  
Imaging Data ◽  
Allen Brain Atlas ◽  
Allen Brain

scholarly journals Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

2019 ◽  
Author(s):  
Melanie Christine Föll ◽  
Lennart Moritz ◽  
Thomas Wollmann ◽  
Maren Nicole Stillger ◽  
Niklas Vockert ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Mass Spectrometry Imaging ◽  
Ease Of Use ◽  
Reproducible Research ◽  
Data Sets ◽  
Imaging Data ◽  
Available N ◽  
The Galaxy ◽  
Analysis Platform

AbstractBackgroundMass spectrometry imaging is increasingly used in biological and translational research as it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired data sets are large and complex and often analyzed with proprietary software or in-house scripts, which hinder reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many MSI researchers.FindingsWe have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Further, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research.ConclusionThe Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access together with high levels of reproducibility and transparency.

scholarly journals Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding

2020 ◽  
Author(s):  
hang hu ◽  
ruichuan yin ◽  
Hilary Brown ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Kidney Tissue ◽  
Data Sets ◽  
Imaging Data ◽  
Tissue Sections ◽  
Spatial Segmentation ◽  
High Quality Image ◽  
Segmentation Approach ◽  
Multivariate Clustering

<p>Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis are treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map is assembled from segment candidates generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.</p>

Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE

2021 ◽  
Vol 93 (40) ◽  
pp. 13421-13425
Author(s):  
Dušan Veličković ◽  
Tamara Bečejac ◽  
Sergii Mamedov ◽  
Kumar Sharma ◽  
Namasivayam Ambalavanan ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Data Sets ◽  
Imaging Data ◽  
Automated Annotation

scholarly journals Multiorder Correction Algorithms to Remove Image Distortions from Mass Spectrometry Imaging Data Sets

2013 ◽  
Vol 85 (21) ◽  
pp. 10249-10254 ◽  
Author(s):  
Florian Gerber ◽  
Florian Marty ◽  
Gert B. Eijkel ◽  
Konrad Basler ◽  
Erich Brunner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Data Sets ◽  
Imaging Data

scholarly journals Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding

2020 ◽  
Author(s):  
hang hu ◽  
ruichuan yin ◽  
Hilary Brown ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Kidney Tissue ◽  
Data Sets ◽  
Imaging Data ◽  
Tissue Sections ◽  
Spatial Segmentation ◽  
High Quality Image ◽  
Segmentation Approach ◽  
Multivariate Clustering

<p>Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis are treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map is assembled from segment candidates generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.</p>

Sign in / Sign up

Export Citation Format

Share Document