Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-04-15 DOI:10.1080/01621459.2024.2342639

Jingyi Jessica Li, Heather J. Zhou, Peter J. Bickel, Xin Tong

引用次数: 0

Abstract

Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences bet...

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剖析基因表达异质性：广义皮尔逊相关平方和 K 线聚类算法

由于迫切需要剖析基因表达数据中的异质性关系，我们在此对皮尔逊相关性平方进行了概括，以捕捉基因表达数据之间的线性相关混合物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.