Cargando…

Common and distinct variation in data fusion of designed experimental data

INTRODUCTION: Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factor...

Descripción completa

Detalles Bibliográficos
Autores principales: Alinaghi, Masoumeh, Bertram, Hanne Christine, Brunse, Anders, Smilde, Age K., Westerhuis, Johan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890597/
https://www.ncbi.nlm.nih.gov/pubmed/31797165
http://dx.doi.org/10.1007/s11306-019-1622-2
_version_ 1783475639791124480
author Alinaghi, Masoumeh
Bertram, Hanne Christine
Brunse, Anders
Smilde, Age K.
Westerhuis, Johan A.
author_facet Alinaghi, Masoumeh
Bertram, Hanne Christine
Brunse, Anders
Smilde, Age K.
Westerhuis, Johan A.
author_sort Alinaghi, Masoumeh
collection PubMed
description INTRODUCTION: Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept. OBJECTIVES: In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets. METHODS: Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design. RESULTS: Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA. CONCLUSIONS: This method provides an improved understanding of the common and distinct variation in response to different experimental factors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-019-1622-2) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6890597
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-68905972019-12-19 Common and distinct variation in data fusion of designed experimental data Alinaghi, Masoumeh Bertram, Hanne Christine Brunse, Anders Smilde, Age K. Westerhuis, Johan A. Metabolomics Original Article INTRODUCTION: Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept. OBJECTIVES: In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets. METHODS: Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design. RESULTS: Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA. CONCLUSIONS: This method provides an improved understanding of the common and distinct variation in response to different experimental factors. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-019-1622-2) contains supplementary material, which is available to authorized users. Springer US 2019-12-03 2020 /pmc/articles/PMC6890597/ /pubmed/31797165 http://dx.doi.org/10.1007/s11306-019-1622-2 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Alinaghi, Masoumeh
Bertram, Hanne Christine
Brunse, Anders
Smilde, Age K.
Westerhuis, Johan A.
Common and distinct variation in data fusion of designed experimental data
title Common and distinct variation in data fusion of designed experimental data
title_full Common and distinct variation in data fusion of designed experimental data
title_fullStr Common and distinct variation in data fusion of designed experimental data
title_full_unstemmed Common and distinct variation in data fusion of designed experimental data
title_short Common and distinct variation in data fusion of designed experimental data
title_sort common and distinct variation in data fusion of designed experimental data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890597/
https://www.ncbi.nlm.nih.gov/pubmed/31797165
http://dx.doi.org/10.1007/s11306-019-1622-2
work_keys_str_mv AT alinaghimasoumeh commonanddistinctvariationindatafusionofdesignedexperimentaldata
AT bertramhannechristine commonanddistinctvariationindatafusionofdesignedexperimentaldata
AT brunseanders commonanddistinctvariationindatafusionofdesignedexperimentaldata
AT smildeagek commonanddistinctvariationindatafusionofdesignedexperimentaldata
AT westerhuisjohana commonanddistinctvariationindatafusionofdesignedexperimentaldata