Group-wise ANOVA simultaneous component analysis for designed omics experiments

Edoardo Saccenti, Age K. Smilde, José Camacho

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Introduction: Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation. Objectives: We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret. Methods: GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices. Results: The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes. Conclusions: GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

LanguageEnglish
Article number73
JournalMetabolomics
Volume14
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018

Fingerprint

Analysis of variance (ANOVA)
Analysis of Variance
Experiments
Principal Component Analysis
Principal component analysis
Biological Phenomena
Metabolomics
Set theory
Gene expression
Proteomics
Design of experiments
Research Design
Gene Expression

Keywords

  • Analysis of variance
  • Designed experiments
  • Principal component analysis
  • Sparsity

Cite this

@article{ced635e1570a4b2e8cf8da723022872e,
title = "Group-wise ANOVA simultaneous component analysis for designed omics experiments",
abstract = "Introduction: Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation. Objectives: We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret. Methods: GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices. Results: The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes. Conclusions: GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.",
keywords = "Analysis of variance, Designed experiments, Principal component analysis, Sparsity",
author = "Edoardo Saccenti and Smilde, {Age K.} and Jos{\'e} Camacho",
year = "2018",
month = "6",
day = "1",
doi = "10.1007/s11306-018-1369-1",
language = "English",
volume = "14",
journal = "Metabolomics",
issn = "1573-3882",
publisher = "Springer New York",
number = "6",

}

Group-wise ANOVA simultaneous component analysis for designed omics experiments. / Saccenti, Edoardo; Smilde, Age K.; Camacho, José.

In: Metabolomics, Vol. 14, No. 6, 73, 01.06.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Group-wise ANOVA simultaneous component analysis for designed omics experiments

AU - Saccenti, Edoardo

AU - Smilde, Age K.

AU - Camacho, José

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Introduction: Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation. Objectives: We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret. Methods: GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices. Results: The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes. Conclusions: GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

AB - Introduction: Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation. Objectives: We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret. Methods: GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices. Results: The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes. Conclusions: GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.

KW - Analysis of variance

KW - Designed experiments

KW - Principal component analysis

KW - Sparsity

U2 - 10.1007/s11306-018-1369-1

DO - 10.1007/s11306-018-1369-1

M3 - Article

VL - 14

JO - Metabolomics

T2 - Metabolomics

JF - Metabolomics

SN - 1573-3882

IS - 6

M1 - 73

ER -