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