| Literature DB >> 34236659 |
Andrew Smith1, Isabella Piga2, Vanna Denti2, Clizia Chinello2, Fulvio Magni2.
Abstract
Matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF)-mass spectrometry imaging (MSI) enables the spatial localization of proteins to be mapped directly on tissue sections, simultaneously detecting hundreds in a single analysis. However, the large data size, as well as the complexity of MALDI-MSI proteomics datasets, requires the appropriate tools and statistical approaches in order to reduce the complexity and mine the dataset in a successful manner. Here, a pipeline for the management of MALDI-MSI data is described, starting with preprocessing of the raw data, followed by statistical analysis using both supervised and unsupervised statistical approaches and, finally, annotation of those discriminatory protein signals highlighted by the data mining procedure.Entities:
Keywords: Data processing; MALDI-MSI; Spatial proteomics; Statistical analysis; Supervised analysis; Unsupervised analysis
Mesh:
Year: 2021 PMID: 34236659 DOI: 10.1007/978-1-0716-1641-3_8
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745