國立虎尾科技大學 |

Statistical Methods for Genomics Data With Clustered Structures and Missing Values.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Statistical Methods for Genomics Data With Clustered Structures and Missing Values./
作者:	Wang, Jiebiao.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Biostatistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355234237

Statistical Methods for Genomics Data With Clustered Structures and Missing Values.
Wang, Jiebiao.

Statistical Methods for Genomics Data With Clustered Structures and Missing Values. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--The University of Chicago, 2017.

Includes bibliographical references

In the era of big data, with the rapidly evolving high-throughput technology in genomics, massive amounts of genomic data have been generated to measure a variety of genomic features. Each of these data types has unique characteristics while sharing some commonalities. Some arising issues in these data types pose new challenges to traditional statistical methods in data analyses.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355234237Subjects--Topical Terms:

783654
Biostatistics.
Index Terms--Genre/Form:

554714
Electronic books.

Statistical Methods for Genomics Data With Clustered Structures and Missing Values.
LDR:03299ntm a2200349Ki 4500 001 918809
005 20181106104111.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355234237
035 $a (MiAaPQ)AAI10603713
035 $a (MiAaPQ)uchicago:13919
035 $a AAI10603713
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wang, Jiebiao. $3 1193234
245 1 0 $a Statistical Methods for Genomics Data With Clustered Structures and Missing Values.
264 0 $c 2017
300 $a 1 online resource (123 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Advisers: Lin S. Chen; Robert D. Gibbons.
502 $a Thesis (Ph.D.)--The University of Chicago, 2017.
504 $a Includes bibliographical references
520 $a In the era of big data, with the rapidly evolving high-throughput technology in genomics, massive amounts of genomic data have been generated to measure a variety of genomic features. Each of these data types has unique characteristics while sharing some commonalities. Some arising issues in these data types pose new challenges to traditional statistical methods in data analyses.
520 $a In this dissertation, I developed tailored statistical methods and extended these methods to more general frameworks for related statistical problems. The methods developed in this work was motivated by three related but distinct areas of genomics research: multi-tissue gene expression and expression-quantitative-trait-loci studies, quantitative proteomics studies, and gene-environment interaction in genetic association studies. The proposed statistical methods accounted for the unique data structures in each data type. In particular, some samples or measurements were naturally or experimentally clustered and may have ignorable or non-ignorable missing values. It has been shown that failing to account for these data characteristics may result in unfaithful conclusions or biased/inefficient estimation. Furthermore, with the goal of detecting individually weak but collectively strong effects of interest, I proposed multivariate analysis methods that jointly analyze multiple functionally related genomic features, as complementary approaches to standard univariate analyses.
520 $a All of the methods developed here are computationally efficient and can be scaled up for high-dimensional data analysis. I developed R packages for each method. I conducted extensive simulation studies to examine the performance of the proposed methods and compared each with existing relevant approaches. I also applied the proposed statistical methods to each of the motivating data sets and obtained new biological results. In the end of each of the three methods chapters, I discussed general applicability of these methods and potential future directions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 783654
655 7 $a Electronic books. $2 local $3 554714
690 $a 0308
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Chicago. $b Public Health Sciences. $3 1193235
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603713 $z click for full text (PQDT)