期刊
BIOINFORMATICS
卷 34, 期 8, 页码 1321-1328出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx765
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资金
- NIH [R01CA190766]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2017R1C1B5017528]
- NATIONAL CANCER INSTITUTE [R01CA190766] Funding Source: NIH RePORTER
Motivation: With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the highdimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results: In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework.
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