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Data transformations for gene-expres...

Durbin, Blythe Pamela.

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Data transformations for gene-expression microarrays.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data transformations for gene-expression microarrays./
作者:	Durbin, Blythe Pamela.
面頁冊數:	92 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.
Contained By:	Dissertation Abstracts International64-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097958

Data transformations for gene-expression microarrays.
Durbin, Blythe Pamela.

Data transformations for gene-expression microarrays. - 92 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.

Thesis (Ph.D.)--University of California, Davis, 2003.

Data from gene-expression microarrays have proven difficult to analyze, in part because the data fail to satisfy the assumptions on which many standard statistical techniques are based. The right data transformation, however, can bring the data more closely in line with assumptions such as normality of errors and constancy of variance, which can greatly simplify downstream analysis.Subjects--Topical Terms:

517247
Statistics.

Data transformations for gene-expression microarrays.
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100 1 $a Durbin, Blythe Pamela. $3 1948134
245 1 0 $a Data transformations for gene-expression microarrays.
300 $a 92 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3352.
500 $a Adviser: David M. Roche.
502 $a Thesis (Ph.D.)--University of California, Davis, 2003.
520 $a Data from gene-expression microarrays have proven difficult to analyze, in part because the data fail to satisfy the assumptions on which many standard statistical techniques are based. The right data transformation, however, can bring the data more closely in line with assumptions such as normality of errors and constancy of variance, which can greatly simplify downstream analysis.
520 $a We introduce a family of data transformations, the generalized-log family, that stabilizes the variance of microarray data across the full range of expression values. We demonstrate that the generalized-log transformation provides superior variance stabilization for two-color microarray data in comparison to two other families of transformations. We present Box-Cox-like maximum-likelihood and robust methods for estimating the transformation parameter. We introduce the two-parameter generalized-log transformation, which extends the generalized-log family via a shift constant.
520 $a Finally, we perform a simulation study that suggests that generalized-log-transformed microarray data follow a distribution consisting of a mixture of normal distributions, rather than a heavy-tailed distribution. Based on this result, we propose that robust estimation of the transformation parameter may not be necessary.
590 $a School code: 0029.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
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690 $a 0308
710 2 0 $a University of California, Davis. $3 1018682
773 0 $t Dissertation Abstracts International $g 64-07B.
790 1 0 $a Roche, David M., $e advisor
790 $a 0029
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097958