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    政大機構典藏 > 商學院 > 統計學系 > 會議論文 >  Item 140.119/139857


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139857


    Title: A symbolic data analysis approach to regularized sliced inverse regression for gene expression data with multiple functional categories and a phenotypic response
    Authors: 吳漢銘
    Wu, Han-Ming
    Contributors: 統計系
    Keywords: data visualization;interval-valued data;symbolic data analysis;sufficient dimension reduction;gene expression;biological knowledge
    Date: 2021-07
    Issue Date: 2022-04-12
    Abstract: Gene expression data such as those obtained from the hybridization microarray, the serial analysis of gene expression (SAGE) and/or RNA-Seq is being used to study a phenotypic response of interest. It is often characterized by a large amount of genes but with limited samples. Also, a priori knowledge of genes such as the functional and/or curated annotations is accumulated and available over the years. This study intends to incorporate both the biological knowledge of genes and the information of a discrete phenotypic response of subjects into dimension reduction through the framework of symbolic data analysis (SDA). The proposed approach consists of two steps. Firstly, the concepts of the symbolic data analysis will be used to aggregate the expression levels into functional intervals according to their functional categories. For unknown genes, we perform the gene selection procedures to select fewer genes that differentiate subtypes of a phenotypic response. The selected unknown genes are further aggregated into the intervals. Secondly, the regularized sliced inverse regression for interval-valued data is applied where the information of a phenotypic response of subjects acts as the slices. We illustrate the proposed method using several public gene expression data sets for data visualization and the class prediction. The results are compared with those of the regularized PCA. The results show that the proposed method can achieve better performance in understanding biologically relevant processes of genes and subjects than purely data-driven models.
    Relation: Statistics Symposium in Memory of Wen-Chen Chen, 中研院
    Data Type: conference
    Appears in Collections:[統計學系] 會議論文

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