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Joint and unique multiblock analysis of biological data: multiomics malaria study
Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0001-8445-0559
Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.ORCID iD: 0000-0001-7881-0968
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2019 (English)In: Faraday discussions (Online), ISSN 1359-6640, E-ISSN 1364-5498, Vol. 218, p. 268-283Article in journal (Refereed) Published
Abstract [en]

Modern profiling technologies enable obtaining large amounts of data which can be later used for comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be faced by bare analysis of separate datasets. Integrated approaches are necessary, because only data integration allows finding correlation trends common for all studied data sets and revealing hidden structures not known a priori. This improves understanding and interpretation of the complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variation shared with other connected matrices and variation that is unique for each single matrix. Mapping unique variation is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for integrated analysis of lipidomic, metabolomic and oxylipin datasets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, what makes development of the new diagnostic and prognostic tools, as well as better understanding of the disease, of utmost importance. In presented work JUMBA made it possible to detect already known trends related to disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced complexity of the analysis, facilitated detection of samples and variables corresponding to specific structures across multiple datasets and by doing this enabled fast interpretation of the studied system. All this makes JUMBA a perfect choice for multiblock analysis of systems biology data.

Place, publisher, year, edition, pages
Cambridge: Royal Society of Chemistry, 2019. Vol. 218, p. 268-283
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:umu:diva-156705DOI: 10.1039/C8FD00243FISI: 000481497900014OAI: oai:DiVA.org:umu-156705DiVA, id: diva2:1291399
Conference
Conference on Challenges in Analysis of Complex Natural Mixtures, Univ Edinburgh, Edinburgh, MAY 13-15, 2019
Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-11-14Bibliographically approved
In thesis
1. Multivariate integration and visualization of multiblock data in chemical and biological applications
Open this publication in new window or tab >>Multivariate integration and visualization of multiblock data in chemical and biological applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Multivariat integration och visualisering av multiblockdata i kemiska och biologiska applikationer
Abstract [en]

Thanks to improvements in technology more data than ever before is generated in almost all fields of science and industry.

The data is analyzed to hopefully provide valuable information and knowledge about a product or process, such as how to improve the quality of a manufactured product.

Analysis of collected data is often performed on a single dataset or data source at a time. In this thesis, I have focused on multiblock analysis, a concept that includes multiple sources or data blocks.  Analogous to how the human senses combine to let us experience the world around us, multiblock analysis integrates multiple data sources, providing a fuller examination of the product or process under study.

My thesis introduces Joint and Unique Multiblock Analysis, JUMBA, a complete analysis workflow for data integration. I describe each step of JUMBA, including data pre-treatment, model building and validation as well as model interpretation. Special focus is put on several newly developed visualizations for model validation and interpretation to make it as easy as possible to draw conclusions from the analysis.

 

By reading my thesis, the reader will gain a working understanding of the process of performing multiblock analysis, including solutions to common problems that are often encountered.

Abstract [sv]

Tack vare tekniska framsprång genereras det idag stora mängder data inom forskning och industri. Genom att analysera sådan data kan det i slutändan leda till att värdefull kunskap om en produkt eller process erhålls och kvaliteten på de studerade produkterna därmed kan ökas.

Analysen av data sker ofta på en enda datakälla, som då representeras av en matris, även kallat ett datablock. I denna avhandling har jag istället fokuserat på koncept som kan analysera flera datakällor samtidigt och integrera dessa. I likhet med hur människans sinnen låter oss uppleva världen runt omkring medför integrerandet av flera datakällor att undersökningen av en produkt eller process blir mer omfattande.

I min avhandling introduceras arbetsflödet JUMBA (Joint and Unique Multiblock Analysis, eng), som är ämnat för att utföra en fullständig integration av data. Jag beskriver varje enskilt steg av JUMBA, allt från förbehandling av data till byggande och validering av modeller samt deras tolkning. Jag har lagt särskild vikt vid att beskriva flera nyskapade typer av visualiseringar som underlättar att korrekta slutsatser kan dras från analysen.

Jag hoppas att läsaren av min avhandling kommer få förståelse för hur man utför analys av flera datablock och denne hittar även lösningar på problem man normalt sett kan ställas inför vid genomförandet.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2019. p. 62
Keywords
Multivariate analysis, PCA, PLS, OnPLS, JUMBA, Multiblock, calibration transfer
National Category
Analytical Chemistry
Identifiers
urn:nbn:se:umu:diva-158330 (URN)978-91-7855-069-2 (ISBN)
Public defence
2019-05-17, KB.E3.03, KBC - building, Linnaeus väg 6, 90736 Umeå, Umeå, 10:00 (English)
Opponent
Supervisors
Funder
eSSENCE - An eScience Collaboration
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-04-30Bibliographically approved

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Surowiec, IzabellaSkotare, TomasGouveia-Figueira, Sandra C.Bergström, SvenNormark, JohanTrygg, Johan

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