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Visualization of descriptive multiblock analysis
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.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2018 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128XArticle in journal (Refereed) In press
Abstract [en]

Abstract Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018.
Keywords [en]
data fusion, descriptive analytics, multiblock analysis, OnPLS, visualization
National Category
Other Chemistry Topics
Identifiers
URN: urn:nbn:se:umu:diva-152512DOI: 10.1002/cem.3071Scopus ID: 2-s2.0-85051048496OAI: oai:DiVA.org:umu-152512DiVA, id: diva2:1254378
Conference
2018/10/09
Funder
eSSENCE - An eScience CollaborationSwedish Research Council, 2016‐04376Available from: 2018-10-09 Created: 2018-10-09 Last updated: 2019-04-25Bibliographically 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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Skotare, TomasSjögren, RickardSurowiec, IzabellaNilsson, DavidTrygg, Johan

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