Integrative sparse principal component analysis of gene expression data
In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance.
Source: Genetic Epidemiology - Category: Epidemiology Authors: Mengque Liu, Xinyan Fan, Kuangnan Fang, Qingzhao Zhang, Shuangge Ma Tags: RESEARCH ARTICLE Source Type: research
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