Imperial College London
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DrRuiPinto

Faculty of Medicine, School of Public Health

Research Associate in Chemometrics/Metabolomics
 
 
 
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Contact

 

+44 (0)20 7594 9761r.pinto Website

 
 
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Location

 

155Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Pinto:2012:10.1021/ac301869p,
author = {Pinto, RC and Gerber, L and Eliasson, M and Sundberg, B and Trygg, J},
doi = {10.1021/ac301869p},
journal = {Anal Chem},
pages = {8675--8681},
title = {Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.},
url = {http://dx.doi.org/10.1021/ac301869p},
volume = {84},
year = {2012}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.
AU  - Pinto,RC
AU  - Gerber,L
AU  - Eliasson,M
AU  - Sundberg,B
AU  - Trygg,J
DO  - 10.1021/ac301869p
EP  - 8681
PY  - 2012///
SP  - 8675
TI  - Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.
T2  - Anal Chem
UR  - http://dx.doi.org/10.1021/ac301869p
UR  - https://www.ncbi.nlm.nih.gov/pubmed/22978754
VL  - 84
ER  -
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