Dataset Information

Common and distinct variation in data fusion of designed experimental data.

ABSTRACT:

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.

SUBMITTER: Alinaghi M

PROVIDER: S-EPMC6890597 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Common and distinct variation in data fusion of designed experimental data.

Alinaghi Masoumeh M Bertram Hanne Christine HC Brunse Anders A Smilde Age K AK Westerhuis Johan A JA

Metabolomics : Official journal of the Metabolomic Society 20191203 1

<h4>Introduction</h4>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.<h4>Objectives</h4>In the present work, we aim to incorporate the experiment ...[more]

PMID: 31797165

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Dataset Information

Common and distinct variation in data fusion of designed experimental data.

Introduction

Objectives

Methods

Results

Conclusions

Publications

Common and distinct variation in data fusion of designed experimental data.

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