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

FindBook

Google Book

Amazon

博客來

Overcoming the Common Challenges in Differential Gene Expression Analysis Studies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Overcoming the Common Challenges in Differential Gene Expression Analysis Studies./
作者:	Huang, Yan.
面頁冊數:	1 online resource (113 pages)
附註:	Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Contained By:	Dissertations Abstracts International81-06B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22619997click for full text (PQDT)
ISBN:	9781392522028

Overcoming the Common Challenges in Differential Gene Expression Analysis Studies.
Huang, Yan.

Overcoming the Common Challenges in Differential Gene Expression Analysis Studies. - 1 online resource (113 pages)

Source: Dissertations Abstracts International, Volume: 81-06, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2019.

Includes bibliographical references

The ability to analyze gene expression data has had a fundamental impact in the biological sciences and on our understanding of the causes and mechanisms of disease. However, a significant statistical challenge is posed by the combination of the small number of replicates together with the large number of genes leading to an undesirable level of misclassified genes when identifying genes with differential expression levels. When multiple gene expression data sets are generated under the same set of experimental conditions, the ques- tion arises as to how to efficiently combine this information. Several methods in the literature have been suggested to aggregate ranked data from multiple sources. We introduce a new classifier, underpinned by Bayesian principles, called Peer Reinforced Ranker (PR-Ranker) which uses density estimation to approximate the probability that a gene is differentially expressed given a collection of ranked lists.Our classifier is amenable to theoretical analysis when the number of genes and lists is large using the theory of large deviations. Under modest technical assumptions we show that asymptotically PR-Ranker has the smallest loss of any rank aggregation procedure. Moreover, we prove that other more ad hoc methods, such as Borda, have a strictly higher asymptotic rate of loss.While the theoretical results are asymptotic, we perform a series of simulation studies that demonstrate that our classifier outperforms existing methods on datasets of realistic size for biological data. Furthermore, we show that the outperformance is even greater when the lists exhibit varying levels of noise or when some sources are corrupted. PR-Ranker automatically adapts to varying data quality and efficiently combines the data from different sources. Finally we apply PR-Ranker to a gene expression data set in a preeclampsia study. The top ranked genes identified were known to be biologically relevant to preeclampsia and our method achieved a substantially higher Consistency Index than other rank aggregation procedures.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781392522028Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Bayesian principlesIndex Terms--Genre/Form:

542853
Electronic books.

Overcoming the Common Challenges in Differential Gene Expression Analysis Studies.
LDR:03452nmm a2200385K 4500 001 2364529
005 20231130105827.5
006 m o d
007 cr mn ---uuuuu
008 241011s2019 xx obm 000 0 eng d
020 $a 9781392522028
035 $a (MiAaPQ)AAI22619997
035 $a AAI22619997
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Huang, Yan. $3 784621
245 1 0 $a Overcoming the Common Challenges in Differential Gene Expression Analysis Studies.
264 0 $c 2019
300 $a 1 online resource (113 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: Dissertations Abstracts International, Volume: 81-06, Section: B.
500 $a Advisor: Huang, Haiyan.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2019.
504 $a Includes bibliographical references
520 $a The ability to analyze gene expression data has had a fundamental impact in the biological sciences and on our understanding of the causes and mechanisms of disease. However, a significant statistical challenge is posed by the combination of the small number of replicates together with the large number of genes leading to an undesirable level of misclassified genes when identifying genes with differential expression levels. When multiple gene expression data sets are generated under the same set of experimental conditions, the ques- tion arises as to how to efficiently combine this information. Several methods in the literature have been suggested to aggregate ranked data from multiple sources. We introduce a new classifier, underpinned by Bayesian principles, called Peer Reinforced Ranker (PR-Ranker) which uses density estimation to approximate the probability that a gene is differentially expressed given a collection of ranked lists.Our classifier is amenable to theoretical analysis when the number of genes and lists is large using the theory of large deviations. Under modest technical assumptions we show that asymptotically PR-Ranker has the smallest loss of any rank aggregation procedure. Moreover, we prove that other more ad hoc methods, such as Borda, have a strictly higher asymptotic rate of loss.While the theoretical results are asymptotic, we perform a series of simulation studies that demonstrate that our classifier outperforms existing methods on datasets of realistic size for biological data. Furthermore, we show that the outperformance is even greater when the lists exhibit varying levels of noise or when some sources are corrupted. PR-Ranker automatically adapts to varying data quality and efficiently combines the data from different sources. Finally we apply PR-Ranker to a gene expression data set in a preeclampsia study. The top ranked genes identified were known to be biologically relevant to preeclampsia and our method achieved a substantially higher Consistency Index than other rank aggregation procedures.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
653 $a Bayesian principles
653 $a classifier
653 $a gene expression profiling
653 $a Peer Reinforced Ranker
653 $a rank aggregation methods
653 $a robust
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of California, Berkeley. $b Statistics. $3 1676555
773 0 $t Dissertations Abstracts International $g 81-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22619997 $z click for full text (PQDT)