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Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments

BACKGROUND: Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes t...

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Autores principales: Liu, Hua, Tarima, Sergey, Borders, Aaron S, Getchell, Thomas V, Getchell, Marilyn L, Stromberg, Arnold J
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1127068/
https://www.ncbi.nlm.nih.gov/pubmed/15850479
http://dx.doi.org/10.1186/1471-2105-6-106
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author Liu, Hua
Tarima, Sergey
Borders, Aaron S
Getchell, Thomas V
Getchell, Marilyn L
Stromberg, Arnold J
author_facet Liu, Hua
Tarima, Sergey
Borders, Aaron S
Getchell, Thomas V
Getchell, Marilyn L
Stromberg, Arnold J
author_sort Liu, Hua
collection PubMed
description BACKGROUND: Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together. RESULTS: We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short time intervals following deafferentation of olfactory receptor neurons. Nine regression patterns have been identified and shown to fit gene expression profiles better than k-means clusters. EASE analysis identified over-represented functional groups in each regression pattern and each k-means cluster, which further demonstrated that the regression method provided more biologically meaningful classifications of gene expression profiles than the k-means clustering method. Comparison with Peddada et al.'s order-restricted inference method showed that our method provides a different perspective on the temporal gene profiles. Reliability study indicates that regression patterns have the highest reliabilities. CONCLUSION: Our results demonstrate that the proposed quadratic regression method improves gene discovery and pattern recognition for non-cyclic short time-course microarray data. With a freely accessible Excel macro, investigators can readily apply this method to their microarray data.
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spelling pubmed-11270682005-05-17 Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments Liu, Hua Tarima, Sergey Borders, Aaron S Getchell, Thomas V Getchell, Marilyn L Stromberg, Arnold J BMC Bioinformatics Methodology Article BACKGROUND: Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together. RESULTS: We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short time intervals following deafferentation of olfactory receptor neurons. Nine regression patterns have been identified and shown to fit gene expression profiles better than k-means clusters. EASE analysis identified over-represented functional groups in each regression pattern and each k-means cluster, which further demonstrated that the regression method provided more biologically meaningful classifications of gene expression profiles than the k-means clustering method. Comparison with Peddada et al.'s order-restricted inference method showed that our method provides a different perspective on the temporal gene profiles. Reliability study indicates that regression patterns have the highest reliabilities. CONCLUSION: Our results demonstrate that the proposed quadratic regression method improves gene discovery and pattern recognition for non-cyclic short time-course microarray data. With a freely accessible Excel macro, investigators can readily apply this method to their microarray data. BioMed Central 2005-04-25 /pmc/articles/PMC1127068/ /pubmed/15850479 http://dx.doi.org/10.1186/1471-2105-6-106 Text en Copyright © 2005 Liu et al; licensee BioMed Central Ltd.
spellingShingle Methodology Article
Liu, Hua
Tarima, Sergey
Borders, Aaron S
Getchell, Thomas V
Getchell, Marilyn L
Stromberg, Arnold J
Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title_full Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title_fullStr Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title_full_unstemmed Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title_short Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
title_sort quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1127068/
https://www.ncbi.nlm.nih.gov/pubmed/15850479
http://dx.doi.org/10.1186/1471-2105-6-106
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