Interactive Visual
Exploration of 3D Mass Spectrometry
Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals
Spatiomolecular Structures at Full Data Resolution
Version 2 2018-02-19, 14:19
Version 1 2018-02-15, 21:04
Posted on 2018-02-19 - 14:19
Technological
advances in mass spectrometry imaging (MSI) have
contributed to growing interest in 3D MSI. However, the large size
of 3D MSI data sets has made their efficient analysis and visualization
and the identification of informative molecular patterns computationally
challenging. Hierarchical stochastic neighbor embedding (HSNE), a
nonlinear dimensionality reduction technique that aims at finding
hierarchical and multiscale representations of large data sets, is
a recent development that enables the analysis of millions of data
points, with manageable time and memory complexities. We demonstrate
that HSNE can be used to analyze large 3D MSI data sets at full mass
spectral and spatial resolution. To benchmark the technique as well
as demonstrate its broad applicability, we have analyzed a number
of publicly available 3D MSI data sets, recorded from various biological
systems and spanning different mass-spectrometry ionization techniques.
We demonstrate that HSNE is able to rapidly identify regions of interest
within these large high-dimensionality data sets as well as aid the
identification of molecular ions that characterize these regions of
interest; furthermore, through clearly separating measurement artifacts,
the HSNE analysis exhibits a degree of robustness to measurement batch
effects, spatially correlated noise, and mass spectral misalignment.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Abdelmoula, Walid
M.; Pezzotti, Nicola; Hölt, Thomas; Dijkstra, Jouke; Vilanova, Anna; McDonnell, Liam A.; et al. (2018). Interactive Visual
Exploration of 3D Mass Spectrometry
Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals
Spatiomolecular Structures at Full Data Resolution. ACS Publications. Collection. https://doi.org/10.1021/acs.jproteome.7b00725
or
Select your citation style and then place your mouse over the citation text to select it.
SHARE
Usage metrics
AUTHORS (7)
WA
Walid
M. Abdelmoula
NP
Nicola Pezzotti
TH
Thomas Hölt
JD
Jouke Dijkstra
AV
Anna Vilanova
LM
Liam A. McDonnell
BP
Boudewijn P. F. Lelieveldt
KEYWORDS
data setsmass spectrometry imagingmass-spectrometry ionization techniques3 D MSI data setsmeasurement batch effectshigh-dimensionality data setsFull Data Resolution Technological advancesSpatiomolecular Structures3 D Mass Spectrometry Imaging Dataneighbor embeddingHSNE analysis exhibitsnonlinear dimensionality reduction techniqueHierarchical Stochastic Neighbor Embeddingdata points3 D MSImeasurement artifactspatterns computationallymemory complexitiesmultiscale representations