Imperial College London
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DrPaoloInglese

Faculty of Medicine, Institute of Clinical Sciences

Research Associate in Data Science and Machine Learning
 
 
 
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Contact

 

p.inglese14 Website

 
 
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Location

 

Robert Steiner MR unitHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Inglese:2019:10.1021/acs.analchem.8b05598,
author = {Inglese, P and Correia, G and Pruski, P and Glen, RC and Takats, Z},
doi = {10.1021/acs.analchem.8b05598},
journal = {Analytical Chemistry},
pages = {6530--6540},
title = {Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging},
url = {http://dx.doi.org/10.1021/acs.analchem.8b05598},
volume = {91},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.
AU  - Inglese,P
AU  - Correia,G
AU  - Pruski,P
AU  - Glen,RC
AU  - Takats,Z
DO  - 10.1021/acs.analchem.8b05598
EP  - 6540
PY  - 2019///
SN  - 0003-2700
SP  - 6530
TI  - Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging
T2  - Analytical Chemistry
UR  - http://dx.doi.org/10.1021/acs.analchem.8b05598
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31013058
UR  - http://hdl.handle.net/10044/1/70357
VL  - 91
ER  -
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