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 can 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 and select 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. The current version of the coglasso
package accepts multi-omics data sets with multiple “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. The function to perform both network estimation and network
selection is bs(). The suggested usage of bs()
only needs the input data set, the dimensions of the “omic” layers, and
the number of values to explore for each hyperparameter.
library(coglasso)
sel_cg <- bs(multi_omics_sd_small, pX = c(14, 5), nlambda_w = 15, nlambda_b = 15, nc = 5)
# To see information about the network estimation and selection
print(sel_cg)bs() explores several combinations of the
hyperparameters characterizing collaborative graphical lasso.
Then, it selects the combination yielding the best network according to
the chosen model selection method. Among others, this function
implements eXtended Efficient StARS (XEStARS), a
significantly faster and memory-efficient version of eXtended
StARS (XStARS, Albanese, Kohlen and
Behrouzi, 2024). These are coglasso-adapted versions of the
StARS selection algorithm (Liu, Roeder
and Wasserman, 2010) selecting the hyperparameter combination that
yields the most stable, yet sparse network. XEStARS is the
default option for the parameter method, so it is enough to
enjoy the comfort of the default behaviour and let the function do the
rest. To plot the selected network, use:
plot(sel_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