README.md
In coglasso: Collaborative Graphical Lasso - Multi-Omics Network Reconstruction

coglasso - Collaborative Graphical Lasso

Coglasso implements collaborative graphical lasso, an algorithm for network reconstruction from multi-omics data sets (Albanese, Kohlen and Behrouzi, 2024). Our algorithm joins the principles of the graphical lasso by Friedman, Hastie and Tibshirani (2008) and collaborative regression by Gross and Tibshirani (2015).

You will be able to install the CRAN release of coglasso with:

install.packages("coglasso")

To install the development version of coglasso from GitHub you need to make sure to install devtools with:

if (!require("devtools")) {
  install.packages("devtools")
}

You can then install the development version with:

devtools::install_github("DrQuestion/coglasso")

Here follows an example on how to reconstruct a multi-omics network with collaborative graphical lasso. For a more exhaustive example we refer the user to the vignette vignette("coglasso"). The package provides example multi-omics data sets of different dimensions, here we will use multi_omics_sd_small. Please notice that the current version of the coglasso package expects multi-omics data sets with two “omic” layers, where the single layers are grouped by column. For example, in multi_omics_sd_small the first 14 columns represent transcript abundances, and the other 5 columns represent metabolite abundances. To default usage of coglasso() only needs the input dataset and the dimension of the first “omic” layer.

library(coglasso)

cg <- coglasso(multi_omics_sd_small, pX = 14)

coglasso() explores several combinations of the hyperparameters characterizing collaborative graphical lasso. To select the combination yielding the most stable, yet sparse network, the package provides the function stars_coglasso(). This function implements a coglasso-adapted version of the StARS selection algorithm (Liu, Roeder and Wasserman, 2010).

sel_cg <- stars_coglasso(cg)

Albanese, A., Kohlen, W., & Behrouzi, P. (2024). Collaborative graphical lasso (arXiv:2403.18602). arXiv https://doi.org/10.48550/arXiv.2403.18602

Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441. https://doi.org/10.1093/biostatistics/kxm045

Gross, S. M., & Tibshirani, R. (2015). Collaborative regression. Biostatistics, 16(2), 326–338. https://doi.org/10.1093/biostatistics/kxu047

Liu, H., Roeder, K., & Wasserman, L. (2010). Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models (arXiv:1006.3316). arXiv https://doi.org/10.48550/arXiv.1006.3316