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Statistical significance and biologi...

University of Cincinnati.

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Statistical significance and biological relevance of microarray data clustering.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical significance and biological relevance of microarray data clustering./
作者:	Guo, Junhai.
面頁冊數:	127 p.
附註:	Adviser: Mario Medvedovic.
Contained By:	Dissertation Abstracts International69-02B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3302790
ISBN:	9780549487937

Statistical significance and biological relevance of microarray data clustering.
Guo, Junhai.

Statistical significance and biological relevance of microarray data clustering. - 127 p.

Adviser: Mario Medvedovic.

Thesis (Ph.D.)--University of Cincinnati, 2008.

DNA microarray is a powerful tool for monitoring expression level of large number of genes simultaneously. Various clustering methods have been applied to identifying groups of co-regulated genes and clinically relevant groups of samples with similar expression profiles. Due to the high dimensionality of the data and the complexity of clustering results, the statistical significance of such findings has been difficult to assess with traditional statistical hypothesis testing methods. Such methods rely on constructing the "null distribution" and calculating p-values. We investigated several approaches for assessing statistical significance of the results from hierarchical and model-based clustering methods based of their ability to control the false positive rate (FPR), false negative rate (FNR) and false discovery rates (FDR). We demonstrate that bootstrap confidence level derived for pairwise co-clusterings generated by the hierarchical clustering procedure exhibit certain level of control of FPR and FNR. For finite mixture model, by adding bootstrap and summarizing results into pairwise measurements, FPR and FNR can be controlled under certain simulated and real life data. Bootstrap also improves the performance of finite mixture model across all the data sets investigated here. Across all simulated and real world data studied here, only posterior pairwise probability (PPP) derived from Gaussian infinite mixture model (GIMM) offers control on FPR and FDR.

ISBN: 9780549487937Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical significance and biological relevance of microarray data clustering.
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520 $a DNA microarray is a powerful tool for monitoring expression level of large number of genes simultaneously. Various clustering methods have been applied to identifying groups of co-regulated genes and clinically relevant groups of samples with similar expression profiles. Due to the high dimensionality of the data and the complexity of clustering results, the statistical significance of such findings has been difficult to assess with traditional statistical hypothesis testing methods. Such methods rely on constructing the "null distribution" and calculating p-values. We investigated several approaches for assessing statistical significance of the results from hierarchical and model-based clustering methods based of their ability to control the false positive rate (FPR), false negative rate (FNR) and false discovery rates (FDR). We demonstrate that bootstrap confidence level derived for pairwise co-clusterings generated by the hierarchical clustering procedure exhibit certain level of control of FPR and FNR. For finite mixture model, by adding bootstrap and summarizing results into pairwise measurements, FPR and FNR can be controlled under certain simulated and real life data. Bootstrap also improves the performance of finite mixture model across all the data sets investigated here. Across all simulated and real world data studied here, only posterior pairwise probability (PPP) derived from Gaussian infinite mixture model (GIMM) offers control on FPR and FDR.
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