Spectral preprocessing for clustering time-series gene expressions

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.

Original languageEnglish
Article number713248
JournalEurasip Journal on Bioinformatics and Systems Biology
Volume2009
DOIs
Publication statusPublished - 2009
Externally publishedYes

Fingerprint

Gene expression
Gene Expression
Cluster Analysis
Preprocessing
Time series
Genes
Clustering
Cell Cycle
Gene
Cells
Circadian Rhythm
Spectral Estimation
Biological Phenomena
cdc Genes
Yeast
Gene Expression Profile
Transcriptome
Grouping
Yeasts
Strategy

ASJC Scopus subject areas

  • Medicine(all)
  • Computer Science(all)
  • Signal Processing
  • Statistics and Probability
  • General

Cite this

Spectral preprocessing for clustering time-series gene expressions. / Zhao, Wentao; Serpedin, Erchin; Dougherty, Edward R.

In: Eurasip Journal on Bioinformatics and Systems Biology, Vol. 2009, 713248, 2009.

Research output: Contribution to journalArticle

@article{18b68cbc3a4f425a9ca44ca1a159c0f3,
title = "Spectral preprocessing for clustering time-series gene expressions",
abstract = "Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.",
author = "Wentao Zhao and Erchin Serpedin and Dougherty, {Edward R.}",
year = "2009",
doi = "10.1155/2009/713248",
language = "English",
volume = "2009",
journal = "Eurasip Journal on Bioinformatics and Systems Biology",
issn = "1687-4145",
publisher = "Springer Publishing Company",

}

TY - JOUR

T1 - Spectral preprocessing for clustering time-series gene expressions

AU - Zhao, Wentao

AU - Serpedin, Erchin

AU - Dougherty, Edward R.

PY - 2009

Y1 - 2009

N2 - Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.

AB - Based on gene expression profiles, genes can be partitioned into clusters, which might be associated with biological processes or functions, for example, cell cycle, circadian rhythm, and so forth. This paper proposes a novel clustering preprocessing strategy which combines clustering with spectral estimation techniques so that the time information present in time series gene expressions is fully exploited. By comparing the clustering results with a set of biologically annotated yeast cell-cycle genes, the proposed clustering strategy is corroborated to yield significantly different clusters from those created by the traditional expression-based schemes. The proposed technique is especially helpful in grouping genes participating in time-regulated processes.

UR - http://www.scopus.com/inward/record.url?scp=65349172517&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=65349172517&partnerID=8YFLogxK

U2 - 10.1155/2009/713248

DO - 10.1155/2009/713248

M3 - Article

VL - 2009

JO - Eurasip Journal on Bioinformatics and Systems Biology

JF - Eurasip Journal on Bioinformatics and Systems Biology

SN - 1687-4145

M1 - 713248

ER -