Spectral preprocessing for clustering time-series gene expressions

Authors:
Wentao Zhao;Erchin Serpedin;Edward R. Dougherty
Affiliations:
Electrical and Computer Engineering Department, Texas A&M University, College Station, TX;Electrical and Computer Engineering Department, Texas A&M University, College Station, TX;Translational Genomics Research Institute, Phoenix, AZ
Venue:
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
Year:
2009

Citing 8
Cited 1

Comparing the similarity of time-series gene expression using signal processing metrics

Computers and Biomedical Research
Identifying periodically expressed transcripts in microarray time series data

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Clustering short time series gene expression data

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Detecting periodic patterns in unevenly spaced gene expression time series using Lomb--Scargle periodograms

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Inferring gene regulatory networks from time series data using the minimum description length principle

Bioinformatics
Spectral analysis of irregularly-sampled data: Paralleling the regularly-sampled data approaches

Digital Signal Processing
Comparing clusterings---an information based distance

Journal of Multivariate Analysis
Nonparametric spectral analysis with missing data via the EM algorithm

Digital Signal Processing

Weighted dynamic time warping for time series classification

Pattern Recognition

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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.