Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics

Tesi per compendi de publicacions

Detalles Bibliográficos
Autor: Fernández Albert, Francesc
Tipo de recurso: tesis doctoral
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/95503
Acceso en línea:https://hdl.handle.net/2117/95503
https://dx.doi.org/10.5821/dissertation-2117-95503
Access Level:acceso abierto
Palabra clave:Aprenentatge automàtic
Cromatografia de líquids -- Informàtica
Metabolòmica -- Informàtica
Espectrometria de masses
Àrees temàtiques de la UPC::Informàtica
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oai_identifier_str oai:upcommons.upc.edu:2117/95503
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
title Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
spellingShingle Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
Fernández Albert, Francesc
Aprenentatge automàtic
Cromatografia de líquids -- Informàtica
Metabolòmica -- Informàtica
Espectrometria de masses
Àrees temàtiques de la UPC::Informàtica
title_short Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
title_full Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
title_fullStr Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
title_full_unstemmed Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
title_sort Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomics
dc.creator.none.fl_str_mv Fernández Albert, Francesc
author Fernández Albert, Francesc
author_facet Fernández Albert, Francesc
author_role author
dc.contributor.none.fl_str_mv Perera Lluna, Alexandre
Llorach Asunción, Rafael
dc.subject.none.fl_str_mv Aprenentatge automàtic
Cromatografia de líquids -- Informàtica
Metabolòmica -- Informàtica
Espectrometria de masses
Àrees temàtiques de la UPC::Informàtica
topic Aprenentatge automàtic
Cromatografia de líquids -- Informàtica
Metabolòmica -- Informàtica
Espectrometria de masses
Àrees temàtiques de la UPC::Informàtica
description Tesi per compendi de publicacions
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-10-30
2014
2014-11-17
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/95503
https://dx.doi.org/10.5821/dissertation-2117-95503
url https://hdl.handle.net/2117/95503
https://dx.doi.org/10.5821/dissertation-2117-95503
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406470386221056
spelling Machine learning methods for the analysis of liquid chromatography-mass spectrometry datasets in metabolomicsFernández Albert, FrancescAprenentatge automàticCromatografia de líquids -- InformàticaMetabolòmica -- InformàticaEspectrometria de massesÀrees temàtiques de la UPC::InformàticaTesi per compendi de publicacionsLiquid Chromatography-Mass Spectrometry (LC/MS) instruments are widely used in Metabolomics. To analyse their output, it is necessary to use computational tools and algorithms to extract meaningful biological information. The main goal of this thesis is to provide with new computational methods and tools to process and analyse LC/MS datasets in a metabolomic context. A total of 4 tools and methods were developed in the context of this thesis. First, it was developed a new method to correct possible non-linear drift effects in the retention time of the LC/MS data in Metabolomics, and it was coded as an R package called HCor. This method takes advantage of the retention time drift correlation found in typical LC/MS data, in which there are chromatographic regions in which their retention time drift is consistently different than other regions. Our method makes the hypothesis that this correlation structure is monotonous in the retention time and fits a non-linear model to remove the unwanted drift from the dataset. This method was found to perform especially well on datasets suffering from large drift effects when compared to other state-of-the art algorithms. Second, it was implemented and developed a new method to solve known issues of peak intensity drifts in metabolomics datasets. This method is based on a two-step approach in which are corrected possible intensity drift effects by modelling the drift and then the data is normalised using the median of the resulting dataset. The drift was modelled using a Common Principal Components Analysis decomposition on the Quality Control classes and taking one, two or three Common Principal Components to model the drift space. This method was compared to four other drift correction and normalisation methods. The two-step method was shown to perform a better intensity drift removal than all the other methods. All the tested methods including the two-step method were coded as an R package called intCor and it is publicly available. Third, a new processing step in the LC/MS data analysis workflow was proposed. In general, when LC/MS instruments are used in a metabolomic context, a metabolite may give a set of peaks as an output. However, the general approach is to consider each peak as a variable in the machine learning algorithms and statistical tests despite the important correlation structure found between those peaks coming from the same source metabolite. It was developed an strategy called peak aggregation techniques, that allow to extract a measure for each metabolite considering the intensity values of the peaks coming from this metabolite across the samples in study. If the peak aggregation techniques are applied on each metabolite, the result is a transformed dataset in which the variables are no longer the peaks but the metabolites. 4 different peak aggregation techniques were defined and, running a repeated random sub-sampling cross-validation stage, it was shown that the predictive power of the data was improved when the peak aggregation techniques were used regardless of the technique used. Fourth, a computational tool to perform end-to-end analysis called MAIT was developed and coded under the R environment. The MAIT package is highly modular and programmable which ease replacing existing modules for user-created modules and allow the users to perform their personalised LC/MS data analysis workflows. By default, MAIT takes the raw output files from an LC/MS instrument as an input and, by applying a set of functions, gives a metabolite identification table as a result. It also gives a set of figures and tables to allow for a detailed analysis of the metabolomic data. MAIT even accepts external peak data as an input. Therefore, the user can insert peak table obtained by any other available tool and MAIT can still perform all its other capabilities on this dataset like a classification or mining the Human Metabolome Dataset which is included in the package.Universitat Politècnica de CatalunyaPerera Lluna, AlexandreLlorach Asunción, Rafael20142014-10-3020142014-11-17doctoral thesishttp://purl.org/coar/resource_type/c_db06VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/2117/95503https://dx.doi.org/10.5821/dissertation-2117-95503reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/955032026-05-27T15:37:01Z
score 15,300719

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