Journal article
EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data
Bioinformatics, Vol.31(19), pp.3198-3206
2015
Abstract
Motivation: Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in ‘omics’ data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets.
Methods: The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups.
Results: We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization.
Details
- Title
- EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data
- Authors/Creators
- C.D. Wijetunge (Author/Creator) - Department of Mechanical Engineering.I. Saeed (Author/Creator) - Department of Mechanical Engineering.B.A. Boughton (Author/Creator) - The University of MelbourneJ.M. Spraggins (Author/Creator) - Bhabha Atomic Research CentreR.M. Caprioli (Author/Creator) - Vanderbilt University Medical CenterA. Bacic (Author/Creator) - The University of MelbourneU. Roessner (Author/Creator) - The University of MelbourneS.K. Halgamuge (Author/Creator) - Department of Mechanical Engineering.
- Publication Details
- Bioinformatics, Vol.31(19), pp.3198-3206
- Publisher
- Oxford University Press
- Identifiers
- 991005542691607891
- Copyright
- © The Author 2015
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
27 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 2 Chemistry
- 2.211 Mass Spectrometry
- 2.211.304 Mass Spectrometry Advances
- Web Of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability
- ESI research areas
- Biology & Biochemistry