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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.3
- 수록면
- 455 - 468 (14page)
- DOI
- 10.7465/jkdi.2019.30.2.455
이용수
초록· 키워드
오류제보하기
We consider the problem to identify differentially expressed genes in microarray studies, which involves a proper multiple testing to high dimensional and low sample size problems in nonstandard data setups. In particular, we need to account for unknown dependence structures among genes in microarray data. A traditional multiple testing rate, family-wise error rate (FWER) is too conservative to control the type I error in these setups, whereas a less conservative multiple testing rate, false discovery rate (FDR) has gotten much attention in a lot of research areas such as genomic data, FMRI data and so on. We compare various FDR procedures in simulated data and real microarray data. For simulated data with dependency, we take hidden Markov model (HMM) into account in order to generate test statistics. For power consideration, we assess different FDR procedures by utilizing false negative rate (FNR). By calculating 1-FNR for each FDR procedure, we consider which FDR procedure is appropriate for identifying differentially expressed genes in microarray data. Numerical results show that the Sun and Cai FDR procedure (2009) is more appropriate for controlling the FDR while minimizing FNR in simulated data under various setups. It also controls the FDR in real microarray data.
목차
Abstract
1. Introduction
2. FDR procedures
3. Numerical analysis
4. Application to real data
5. Concluding remarks
References
참고문헌 (19)
참고문헌 신청
Benjamini, Y. / 1995 / Controlling the false discovery rate: A practical and powerful approach to multiple testing / Journal of the Royal Statistical Society: Series B 57:289~300
Benjamini, Y. / 1999 / A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence / Journal of Statistical Planning and Inference 82:163~170
Benjamini, Y. / 2001 / The control of the false discovery rate in multiple testingunder dependency / Annals of Statistics 29:1165~1188
Efron, B. / 2001 / Empirical Bayes analysis of a microarray experiment / Journal of the American Statistical Association 96:1151~1160
Efron, B. / 2003 / Robbins, empirical Bayes and microarrays / Annals of Statistics 31:366~378
Benjamini, Y. / 1999 / A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence / Journal of Statistical Planning and Inference 82:163~170
Benjamini, Y. / 2001 / The control of the false discovery rate in multiple testingunder dependency / Annals of Statistics 29:1165~1188
Efron, B. / 2001 / Empirical Bayes analysis of a microarray experiment / Journal of the American Statistical Association 96:1151~1160
Efron, B. / 2003 / Robbins, empirical Bayes and microarrays / Annals of Statistics 31:366~378
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UCI(KEPA) : I410-ECN-0101-2019-041-000582605