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Clustering time-course gene-expressi...

Rice University.

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Clustering time-course gene-expression array data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Clustering time-course gene-expression array data./
作者:	Gershman, Jason Andrew.
面頁冊數:	207 p.
附註:	Adviser: Rudy Guerra.
Contained By:	Dissertation Abstracts International69-04B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3309873
ISBN:	9780549579397

Clustering time-course gene-expression array data.
Gershman, Jason Andrew.

Clustering time-course gene-expression array data. - 207 p.

Adviser: Rudy Guerra.

Thesis (Ph.D.)--Rice University, 2008.

This thesis examines methods used to cluster time-course gene expression array data. In the past decade, various model-based methods have been published and advocated for clustering this type of data in place of classic non-parametric techniques like K-means and hierarchical clustering. On simulated data, where the variance between clusters is large, I show that the model-based MCLUST outperforms model-based SSClust and non-model-based K-means clustering. I also show that the number of genes or the number of clusters has no significant effect on the performance of these model-based clustering techniques. On two real data sets, where the variance between clusters is smaller, I show that model-based SSClust outperforms both MCLUST and K-means clustering. Since the "truth" is often not known for real data sets, I use the clustered data as "truth" and then perturb the data by adding pointwise noise to cluster this noisy data. Throughout my analysis of real and simulated expression data, I use the misclassification rate and the overall success rate as measures of success of the clustering algorithm. Overall, the model-based methods appear to cluster the data better than the non-model-based methods.

ISBN: 9780549579397Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Clustering time-course gene-expression array data.
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520 $a This thesis examines methods used to cluster time-course gene expression array data. In the past decade, various model-based methods have been published and advocated for clustering this type of data in place of classic non-parametric techniques like K-means and hierarchical clustering. On simulated data, where the variance between clusters is large, I show that the model-based MCLUST outperforms model-based SSClust and non-model-based K-means clustering. I also show that the number of genes or the number of clusters has no significant effect on the performance of these model-based clustering techniques. On two real data sets, where the variance between clusters is smaller, I show that model-based SSClust outperforms both MCLUST and K-means clustering. Since the "truth" is often not known for real data sets, I use the clustered data as "truth" and then perturb the data by adding pointwise noise to cluster this noisy data. Throughout my analysis of real and simulated expression data, I use the misclassification rate and the overall success rate as measures of success of the clustering algorithm. Overall, the model-based methods appear to cluster the data better than the non-model-based methods.
520 $a Later, I examine the role of gene ontology (GO) and using gene ontology data to cluster gene expression data. I find that clustering expression data, using a synthesis of gene expression and gene ontology not only provides clustering that has a biologic meaning but also clusters the data well. I also introduce an algorithm for clustering expression profiles on both gene expression and gene ontology data when some of the genes are missing the ontology data. Instead of some other methods which ignore the missing data or lump it all into a miscellaneous cluster, I use classification and inferential techniques to cluster using all of the available data and this method shows promising results. I also examine which ontology, among molecular function, biological process, and cellular component, is best in clustering expression data. This analysis shows that biological process is the preferred ontology for clustering expression data.
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