Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset

Version 1 : Received: 9 July 2020 / Approved: 11 July 2020 / Online: 11 July 2020 (04:01:03 CEST)

A peer-reviewed article of this Preprint also exists.

Torell, F.; Skotare, T.; Trygg, J. Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset. Metabolites 2020, 10, 295. Torell, F.; Skotare, T.; Trygg, J. Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset. Metabolites 2020, 10, 295.

Journal reference: Metabolites 2020, 10, 295
DOI: 10.3390/metabo10070295

Abstract

Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has few number of samples, we studied a small metabolomic multiblock dataset containing six blocks (i.e. tissue types), only including common metabolites. We used a single model multiblock analysis method called Joint and unique multiblock analysis (JUMBA) and compare it to a commonly used method, concatenated PCA. These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.

Subject Areas

Data integration; Metabolomics; Multi-tissue; Multiblock; Joint and unique multiblock analysis (JUMBA), OnPLS; Multiblock Orthogonal Component Analysis (MOCA)

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