ASCA: analysis of multivariate data obtained from an experimental design

J.J. Jansen; H.C.J. Hoefsloot; J. Van der Greef; M.E. Timmerman; A.K Smilde

doi:10.1002/cem.952

ASCA: analysis of multivariate data obtained from an experimental design

J.J. Jansen, H.C.J. Hoefsloot, J. Van der Greef, M.E. Timmerman, A.K Smilde^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

207 Citations (Scopus)

Abstract

Recently analysis of variance (ANOVA)-simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043-3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright (c) 2006 John Wiley & Sons, Ltd.

Original language	English
Pages (from-to)	469-481
Number of pages	13
Journal	Journal of Chemometrics
Volume	19
Issue number	9
DOIs	https://doi.org/10.1002/cem.952
Publication status	Published - Sept-2005

Keywords

ANOVA
PCA
experimental design
component model
SIMULTANEOUS COMPONENT ANALYSIS
METABOLOMICS DATA
2-WAY ANALYSIS
VARIANCE
RESPONSES
TOOL

Access to Document

10.1002/cem.952

Handle.net

Persistent link

Embargoed Document

ASCA analysis of multivariate data obtained from an experimental design
Final publisher's version, 196 KB
Request copy

Cite this

@article{a1ff2c58d51d478aaff06cbb3b79c593,

title = "ASCA: analysis of multivariate data obtained from an experimental design",

abstract = "Recently analysis of variance (ANOVA)-simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043-3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright (c) 2006 John Wiley & Sons, Ltd.",

keywords = "ANOVA, PCA, experimental design, component model, SIMULTANEOUS COMPONENT ANALYSIS, METABOLOMICS DATA, 2-WAY ANALYSIS, VARIANCE, RESPONSES, TOOL",

author = "J.J. Jansen and H.C.J. Hoefsloot and {Van der Greef}, J. and M.E. Timmerman and A.K Smilde",

year = "2005",

month = sep,

doi = "10.1002/cem.952",

language = "English",

volume = "19",

pages = "469--481",

journal = "Journal of Chemometrics",

issn = "1099-128X",

publisher = "Wiley",

number = "9",

}

TY - JOUR

T1 - ASCA

T2 - analysis of multivariate data obtained from an experimental design

AU - Jansen, J.J.

AU - Hoefsloot, H.C.J.

AU - Van der Greef, J.

AU - Timmerman, M.E.

AU - Smilde, A.K

PY - 2005/9

Y1 - 2005/9

N2 - Recently analysis of variance (ANOVA)-simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043-3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright (c) 2006 John Wiley & Sons, Ltd.

AB - Recently analysis of variance (ANOVA)-simultaneous component analysis (ASCA) has been introduced as an explorative tool for the analysis of multivariate data with an underlying experimental design [Smilde et al. Bioinformatics 2005; 21: 3043-3048]. This paper focuses on the general methodological framework of ASCA. The drawbacks of other methods for the analysis of this type of data are discussed, as well as the advantages of ASCA above these other methods. Three case studies are used to illustrate the use of ASCA. The relationship between ASCA and several other multivariate data analysis techniques is demonstrated. Finally, possible extensions for ASCA are presented, including multiway analysis and multivariate regression. Copyright (c) 2006 John Wiley & Sons, Ltd.

KW - ANOVA

KW - PCA

KW - experimental design

KW - component model

KW - SIMULTANEOUS COMPONENT ANALYSIS

KW - METABOLOMICS DATA

KW - 2-WAY ANALYSIS

KW - VARIANCE

KW - RESPONSES

KW - TOOL

U2 - 10.1002/cem.952

DO - 10.1002/cem.952

M3 - Article

SN - 1099-128X

VL - 19

SP - 469

EP - 481

JO - Journal of Chemometrics

JF - Journal of Chemometrics

IS - 9

ER -

ASCA: analysis of multivariate data obtained from an experimental design

Abstract

Keywords

Access to Document

Handle.net

Embargoed Document

Fingerprint

Cite this