HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis

被引：4

作者：

Ren, Mingyang ^{[1
,2
]}

Zhang, Sanguo ^{[1
,2
]}

Zhang, Qingzhao ^{[3
,4
]}

Ma, Shuangge ^{[5
]}

机构：

[1] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China

[2] Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China

[3] Xiamen Univ, MOE Key Lab Econometr, Dept Stat, Sch Econ,Wang Yanan Inst Studies Econ, Xiamen 361005, Peoples R China

[4] Xiamen Univ, Fujian Key Lab Stat, Xiamen 361005, Peoples R China

[5] Yale Sch Publ Hlth, Dept Biostat, New Haven, CT 06520 USA

来源：

BIOINFORMATICS | 2021年 / 37卷 / 18期

基金：

北京市自然科学基金; 美国国家科学基金会; 中国国家自然科学基金; 美国国家卫生研究院;

关键词：

D O I：

10.1093/bioinformatics/btab134

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

A Summary: Heterogeneity is a hallmark of many complex human diseases, and unsupervised heterogeneity analysis has been extensively conducted using high-throughput molecular measurements and histopathological imaging features. 'Classic' heterogeneity analysis has been based on simple statistics such as mean, variance and correlation. Network-based analysis takes interconnections as well as individual variable properties into consideration and can be more informative. Several Gaussian graphical model (GGM)-based heterogeneity analysis techniques have been developed, but friendly and portable software is still lacking. To facilitate more extensive usage, we develop the R package HeteroGGM, which conducts GGM-based heterogeneity analysis using the advanced penaliztaion techniques, can provide informative summary and graphical presentation, and is efficient and friendly.

引用

页码：3073 / 3074

页数：2