[Publication Time] 2021-04-01

[Lead Author] Zhang, Sanguo

[Corresponding Author] 孙怡帆; Ma, Shuangge

[Journal] STATISTICS IN MEDICINE

[Abstract]

Heterogeneity is a hallmark of many complex diseases. There are multiple ways of defining heterogeneity, among which the heterogeneity in genetic regulations, for example GEs (gene expressions) by CNVs (copy number variations) and methylation, has been suggested but little investigated. The heterogeneity in genetic regulations can be linked with disease severity, progression, and other traits and is biologically highly important. However, the analysis can be very challenging with the high dimensionality of both sides of regulation and sparse and weak signals. In this article, we consider the scenario where subjects form unknown subgroups, and each subgroup has unique genetic regulation relationships. Further, such heterogeneity is “guided" by a known biomarker. We develop an MSF (Multivariate Sparse Fusion) approach, which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and regulation relationships within each subgroup. An effective computational algorithm is developed, and extensive simulations are conducted. The analysis of heterogeneity in the GE-CNV regulations in melanoma and GE-methylation regulations in stomach cancer using the TCGA (The Cancer Genome Atlas) data leads to interesting findings.

[Keywords]

biomarker; genetic regulations; heterogeneity analysis; multivariate sparse fusion