Bioinformatics. 2021 Feb 26:btab134. doi: 10.1093/bioinformatics/btab134. Online ahead of print.
ABSTRACT
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.
AVAILABILITY: The package is available at https://CRAN.R-project.org/package=HeteroGGM.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:33638346 | DOI:10.1093/bioinformatics/btab134
