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首页> 外文期刊>Journal of applied statistics >Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components
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Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components

机译：使用稀疏主要成分鉴定与实体瘤对达沙替尼敏感性相关的基因组标记

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摘要

Differential analysis techniques are commonly used to offer scientists a dimension reduction procedure and an interpretable gateway to variable selection, especially when confronting high-dimensional genomic data. Huang etal. used a gene expression profile of breast cancer cell lines to identify genomic markers which are highly correlated with in vitro sensitivity of a drug Dasatinib. They considered three statistical methods to identify differentially expressed genes and finally used the results from the intersection. But the statistical methods that are used in the paper are not sufficient to select the genomic markers. In this paper we used three alternative statistical methods to select a combined list of genomic markers and compared the genes that were proposed by Huang etal. We then proposed to use sparse principal component analysis (Sparse PCA) to identify a final list of genomic markers. The Sparse PCA incorporates correlation into account among the genes and helps to draw a successful genomic markers discovery. We present a new and a small set of genomic markers to separate out the groups of patients effectively who are sensitive to the drug Dasatinib. The analysis procedure will also encourage scientists in identifying genomic markers that can help to separate out two groups.
机译：差异分析技术通常用于为科学家提供降维程序和变量选择的可解释途径，尤其是在面对高维基因组数据时。黄等。使用了乳腺癌细胞系的基因表达谱来鉴定与药物达沙替尼的体外敏感性高度相关的基因组标记。他们考虑了三种统计方法来鉴定差异表达的基因，并最终使用了交集的结果。但是，本文中使用的统计方法不足以选择基因组标记。在本文中，我们使用了三种替代的统计方法来选择基因组标记的组合列表，并比较了Huang等人提出的基因。然后，我们建议使用稀疏主成分分析（Sparse PCA）来确定基因组标记的最终列表。稀疏PCA将相关性纳入基因之中，并有助于得出成功的基因组标记发现。我们提出了一套新的和少量的基因组标记物，以有效地区分出对药物达沙替尼敏感的患者群体。该分析程序还将鼓励科学家确定有助于区分两组的基因组标记。

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