基因组数据的两两非线性相关性分析。

IF 1.3 4区数学 Q2 STATISTICS & PROBABILITY

Annals of Applied Statistics Pub Date : 2023-12-01 Epub Date: 2023-10-30 DOI:10.1214/23-aoas1745

Siqi Xiang, Wan Zhang, Siyao Liu, Katherine A Hoadley, Charles M Perou, Kai Zhang, J S Marron

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引用次数: 0

摘要

在癌症基因组图谱(TCGA)数据集中，有许多有趣的非线性依赖关系的基因对揭示癌症的重要关系和亚型。这种基因组数据分析需要快速、强大和可解释的检测过程，特别是在高维环境中。我们使用一个强大的工具二进制展开测试来研究TCGA中基因对的非线性表达模式。我们发现了许多非线性模式，其中一些是由已知的癌症亚型驱动的，其中一些是新的。

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PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA.

In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.

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来源期刊

Annals of Applied Statistics 社会科学-统计学与概率论

CiteScore

3.10

自引率

5.60%

发文量

131

审稿时长

6-12 weeks

期刊介绍： Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.