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