Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multiomics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience.

To install and use the package, you may download the directory or follow the instructions below. ```{r, install-and-example} # Install package if (!require(“devtools”)) install.packages(“devtools”) devtools::install_github(“KechrisLab/SmCCNet”)

Load package

library(SmCCNet) ```

In the vignettes folder, users can find a documentation that illustrates how to implement SmCCNet with an example data set. The data file is included under the data folder. Details on all the functions included in the package are documented in the package manual under the package folder. Users may directly download the package tarball for all functions and example data, which is also under the package folder.

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