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

Chalini D. Wijetunge | Zendy; Isaam Saeed | Zendy; Berin A. Boughton | Zendy; Jeffrey M. Spraggins | Zendy; Richard M. Caprioli | Zendy; Antony Bacic | Zendy; Ute Roessner | Zendy; Saman Halgamuge | Zendy

Open Access

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

Author(s) -

Chalini D. Wijetunge,

Isaam Saeed,

Berin A. Boughton,

Jeffrey M. Spraggins,

Richard M. Caprioli,

Antony Bacic,

Ute Roessner,

Saman Halgamuge

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv356

Subject(s) - normalization (sociology) , computer science , cluster analysis , mass spectrometry imaging , data mining , pattern recognition (psychology) , pipeline (software) , spatial analysis , segmentation , artificial intelligence , mass spectrometry , mathematics , chemistry , statistics , chromatography , sociology , anthropology , programming language

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.

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