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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제32권 제1호
- 발행연도
- 2025.1
- 수록면
- 91 - 106 (16page)
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초록· 키워드
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Co-inertia analysis (CIA) is a multivariate analysis method that assesses relationships and trends in two sets of data. It has been effectively employed in the integrative analysis of high-dimensional multi-omics datasets. Recently, penalized CIA methods have been introduced to enhance the interpretability by inducing sparsity in the loading vectors. However, challenges persist in ensuring that non-zero elements in the estimated vector genuinely represent significant features. To address these challenges, we propose a penalized CIA method that controls the false discovery rate (FDR) using sorted l-1 penalized estimation (SLOPE). This approach allows for simultaneous FDR control and sparsity induction in the estimated vectors. Extensive simulation studies demonstrate the performance compared to the existing CIA method. Additionally, we apply our methods to the integrative analysis of NCI60 data to show its effectiveness in real-world scenarios.
목차
Abstract
1. Introduction
2. Methods
3. Simulation study
4. Real data analysis: NCI60
5. Discussion
References
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