東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Application of stellar photometry to...

Sabripour, Mahyar.

FindBook

Google Book

Amazon

博客來

Application of stellar photometry to the analysis of microarray images.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of stellar photometry to the analysis of microarray images./
作者:	Sabripour, Mahyar.
面頁冊數:	243 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-03, Section: B, page: 1246.
Contained By:	Dissertation Abstracts International66-03B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3168446
ISBN:	0542042185

Application of stellar photometry to the analysis of microarray images.
Sabripour, Mahyar.

Application of stellar photometry to the analysis of microarray images. - 243 p.

Source: Dissertation Abstracts International, Volume: 66-03, Section: B, page: 1246.

Thesis (Ph.D.)--The University of Texas Graduate School of Biomedical Sciences at Houston, 2005.

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots.

ISBN: 0542042185Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Application of stellar photometry to the analysis of microarray images.
LDR:03209nmm 2200289 4500 001 1812773
005 20060427132631.5
008 130610s2005 eng d
020 $a 0542042185
035 $a (UnM)AAI3168446
035 $a AAI3168446
040 $a UnM $c UnM
100 1 $a Sabripour, Mahyar. $3 1902313
245 1 0 $a Application of stellar photometry to the analysis of microarray images.
300 $a 243 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-03, Section: B, page: 1246.
500 $a Supervisor: Christopher I. Amos.
502 $a Thesis (Ph.D.)--The University of Texas Graduate School of Biomedical Sciences at Houston, 2005.
520 $a Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots.
590 $a School code: 2034.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Engineering, Biomedical. $3 1017684
690 $a 0308
690 $a 0369
690 $a 0541
710 2 0 $a The University of Texas Graduate School of Biomedical Sciences at Houston. $3 1019338
773 0 $t Dissertation Abstracts International $g 66-03B.
790 1 0 $a Amos, Christopher I., $e advisor
790 $a 2034
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3168446