breast

Breast cancer is one of the most prevalent types of cancer which can be classified into four molecular subtypes, namely, luminal A, basal-like, HER2-enriched, and lu- minal B, based on their tumor expression profiles (Haque et al, 2012). In this study, we aim to construct a single gene regulatory network across the four subtypes to discover the overall gene regulation mechanism in breast cancer. It should be used in <code>BRGM(data,...)</code>.

datasets

The Gaussian graphical model is a widely used tool for learning gene regulatory networks with high-dimensional gene expression data. For many real problems, the data are heterogeneous, which may contain some subgroups or come from different resources. This package provide a Gaussian Graphical Mixture Model (GGMM) for the heterogeneous data. You can refer to Jia, B. and Liang, F. (2018) at <arXiv:1805.02547> for detail.

Bochao Jia

GGMM

Mixture Gaussian Graphical Models

Faming Liang

breast function

<dl class="dl-horizontal">
 <dt> breast </dt><dd>a nxp breast cancer gene expression data.</dd>
</dl>

Format

Example dataset for learning gene regulatory network. — breast

<dl class='dl-horizontal'>
 <dt> breast </dt><dd>a nxp breast cancer gene expression data.</dd>
</dl>

breast: Example dataset for learning gene regulatory network.

Description

Usage

Arguments

Format

References