Applications of a new subspace clustering algorithm (COSA) in medical systems biology

Doris Damian, Matej Oresic (Corresponding Author), Elwin Verheij, Jacqueline Meulman, Jerome Friedman, Aram Adourian, Nicole Morel, Age Smilde, Jan van der Greef

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.
Original languageEnglish
Pages (from-to)69-77
JournalMetabolomics
Volume3
Issue number1
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

Fingerprint

Systems Biology
Clustering algorithms
Cluster Analysis
Pathology
Principal component analysis
Rats
Animals
Animal Diseases
Metabolomics
Principal Component Analysis

Keywords

  • COSA
  • subspace clustering
  • metabolomics
  • lipidomics
  • biomarkers
  • translational research
  • metabolic syndrome
  • Zucker rats
  • ZDF rats

Cite this

Damian, D., Oresic, M., Verheij, E., Meulman, J., Friedman, J., Adourian, A., ... van der Greef, J. (2007). Applications of a new subspace clustering algorithm (COSA) in medical systems biology. Metabolomics, 3(1), 69-77. https://doi.org/10.1007/s11306-006-0045-z
Damian, Doris ; Oresic, Matej ; Verheij, Elwin ; Meulman, Jacqueline ; Friedman, Jerome ; Adourian, Aram ; Morel, Nicole ; Smilde, Age ; van der Greef, Jan. / Applications of a new subspace clustering algorithm (COSA) in medical systems biology. In: Metabolomics. 2007 ; Vol. 3, No. 1. pp. 69-77.
@article{ccd07efa4e004578ac52eeeaac45d24c,
title = "Applications of a new subspace clustering algorithm (COSA) in medical systems biology",
abstract = "A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.",
keywords = "COSA, subspace clustering, metabolomics, lipidomics, biomarkers, translational research, metabolic syndrome, Zucker rats, ZDF rats",
author = "Doris Damian and Matej Oresic and Elwin Verheij and Jacqueline Meulman and Jerome Friedman and Aram Adourian and Nicole Morel and Age Smilde and {van der Greef}, Jan",
year = "2007",
doi = "10.1007/s11306-006-0045-z",
language = "English",
volume = "3",
pages = "69--77",
journal = "Metabolomics",
issn = "1573-3882",
publisher = "Springer",
number = "1",

}

Damian, D, Oresic, M, Verheij, E, Meulman, J, Friedman, J, Adourian, A, Morel, N, Smilde, A & van der Greef, J 2007, 'Applications of a new subspace clustering algorithm (COSA) in medical systems biology', Metabolomics, vol. 3, no. 1, pp. 69-77. https://doi.org/10.1007/s11306-006-0045-z

Applications of a new subspace clustering algorithm (COSA) in medical systems biology. / Damian, Doris; Oresic, Matej (Corresponding Author); Verheij, Elwin; Meulman, Jacqueline; Friedman, Jerome; Adourian, Aram; Morel, Nicole; Smilde, Age; van der Greef, Jan.

In: Metabolomics, Vol. 3, No. 1, 2007, p. 69-77.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Applications of a new subspace clustering algorithm (COSA) in medical systems biology

AU - Damian, Doris

AU - Oresic, Matej

AU - Verheij, Elwin

AU - Meulman, Jacqueline

AU - Friedman, Jerome

AU - Adourian, Aram

AU - Morel, Nicole

AU - Smilde, Age

AU - van der Greef, Jan

PY - 2007

Y1 - 2007

N2 - A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.

AB - A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.

KW - COSA

KW - subspace clustering

KW - metabolomics

KW - lipidomics

KW - biomarkers

KW - translational research

KW - metabolic syndrome

KW - Zucker rats

KW - ZDF rats

U2 - 10.1007/s11306-006-0045-z

DO - 10.1007/s11306-006-0045-z

M3 - Article

VL - 3

SP - 69

EP - 77

JO - Metabolomics

JF - Metabolomics

SN - 1573-3882

IS - 1

ER -

Damian D, Oresic M, Verheij E, Meulman J, Friedman J, Adourian A et al. Applications of a new subspace clustering algorithm (COSA) in medical systems biology. Metabolomics. 2007;3(1):69-77. https://doi.org/10.1007/s11306-006-0045-z