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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# coglasso - Collaborative Graphical Lasso <a href="https://drquestion.github.io/coglasso/"><img src="man/figures/logo.png" align="right" height="138" alt="coglasso website" /></a>
<!-- badges: start -->
[](https://github.com/DrQuestion/coglasso/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/DrQuestion/coglasso)
[](https://cran.r-project.org/package=coglasso)
<!-- badges: end -->
Coglasso implements *collaborative graphical lasso*, an algorithm for network reconstruction from multi-omics data sets ([Albanese, Kohlen and Behrouzi, 2024](#references)). Our algorithm joins the principles of the *graphical lasso* by Friedman, Hastie and Tibshirani ([2008](#references)) and *collaborative regression* by Gross and Tibshirani ([2015](#references)).
## Installing coglasso
You can install the CRAN release of coglasso with:
``` r
install.packages("coglasso")
```
## Installing the development version
To install the development version of coglasso from [GitHub](https://github.com/) you need to make sure to install devtools with:
```r
if (!require("devtools")) {
install.packages("devtools")
}
```
You can then install the development version with:
``` r
devtools::install_github("DrQuestion/coglasso")
```
## Usage
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.
```r
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](#ref)). These are coglasso-adapted versions of the *StARS* selection algorithm ([Liu, Roeder and Wasserman, 2010](#references)) 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:
```r
plot(sel_cg)
```
## References
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