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 Accessible and reproducible mass spectrometry imaging data analysis in Galaxy

GigaScience ◽  
2019 ◽  
Vol 8 (12) ◽  
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 ◽  
Imaging Data ◽  
Available N ◽  
The Galaxy ◽  
Msi Analysis ◽  
Analysis Platform

Abstract Background Mass spectrometry imaging is increasingly used in biological and translational research because 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 datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. Findings We 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. Furthermore, 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. Conclusion The 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 Evaluation of Digital Image Recognition Methods for Mass Spectrometry Imaging Data Analysis

2018 ◽  
Vol 29 (12) ◽  
pp. 2467-2470 ◽  
Author(s):  
Måns Ekelöf ◽  
Kenneth P. Garrard ◽  
Rika Judd ◽  
Elias P. Rosen ◽  
De-Yu Xie ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Image Recognition ◽  
Digital Image ◽  
Mass Spectrometry Imaging ◽  
Imaging Data

scholarly journals Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework

2021 ◽  
Author(s):  
Melanie Christine Föll ◽  
Veronika Volkmann ◽  
Kathrin Enderle-Ammour ◽  
Konrad Wilhelm ◽  
Dan Guo ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Data Analysis ◽  
Mass Spectrometry Imaging ◽  
Urothelial Cancer ◽  
Type I ◽  
Raw Data ◽  
The Galaxy ◽  
Muscle Invasive ◽  
Reproducible Manner

Background: Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens. This allows for spatial characterization of molecular compositions of different tissue types and tumor subtypes, which bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of an urothelial carcinoma dataset. Methods: Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle invasive urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results: Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle invasive tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: Stromal tissue was characterized by collagen type I peptides and tumor tissue by histone and heat shock protein beta-1 peptides. Intermediate filaments such as cytokeratins and vimentin were discriminative between the tumors with different muscle-infiltration status. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion: Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

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 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>

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