---
title: "Reconstructing mult-omics networks with coglasso"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Reconstructing mult-omics networks with coglasso}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Introduction

This vignette illustrates the basic usage of the coglasso package to reconstruct a multi-omics network. The package implements and R interface to *collaborative graphical lasso*, a network estimation algorithm based on *graphical lasso* ([Friedman, Hastie and Tibshirani, 2008](#ref)) and *collaborative regression* ([Gross and Tibshirani, 2015](#ref))

Let us first attach coglasso.

```{r setup}
library(coglasso)
```

We then choose the multi-omics data set to use. The coglasso package offers three alternative version of a transcriptomics and metabolomics data set. We will use `multi_omics_sd_small`. For further explanation about all the multi-omics data sets see `help(multi_omics_sd)`.

```{r}
colnames(multi_omics_sd_small)
nrow(multi_omics_sd_small)
```

This smaller version of `multi_omics_sd` has 19 variables, 14 genes and 5 metabolites, and 30 samples. We can directly proceed with network reconstruction.

## Multi-omics network reconstruction

Our objective is to reconstruct a network from this data set using *collaborative graphical lasso*. To do so with the coglasso package, we mainly call two functions. First, we use `coglasso()` to estimate a network for every combination of hyperparameters we want to explore. Following this, we call `stars_coglasso()` to select the best combination of hyperparameters using stability selection.

The usual application of `coglasso()` requires to give an input data set to the argument `data`, the number of variables of the the first type to `pX`, and hyperparameter settings. Collaborative graphical lasso has three hyperparameters: $λ_w$, penalizing "within" same-type interactions, $λ_b$ penalizing "between" different-type interactions, and $c$, the weight of the collaborative term. In this vignette we choose to explore 15 possible penalty values for both "within" and "between" penalties, and three possible collaboration values. We do so by setting both `nlambda_w` and `nlambda_b` to 15, and by setting `nc` to 3. We also decide to restrict our search to a less extreme penalization, by setting a fixed maximum value for both penalties to 0.85, while by default it is often estimated to a higher value with a data-driven approach. Nevertheless, we also decide to focus our search to the sparse side of possible networks. We achieve this by setting to a fixed value the ratios between the smallest (least penalizing) and the largest (most penalizing) penalty explored. While the default value of these parameters is 0.1, we decide to set the minimum ratio to 0.6 for $λ_w$ and to 0.4 for $λ_b$. For further explanation on other arguments of `coglasso()` and how to use them, please see `help(coglasso)`.

```{r}
cg <- coglasso(multi_omics_sd_small,
  pX = 14,
  nlambda_w = 15,
  nlambda_b = 15,
  nc = 3,
  lambda_w_max = 0.85,
  lambda_b_max = 0.85,
  lambda_w_min_ratio = 0.6,
  lambda_b_min_ratio = 0.4,
  verbose = FALSE
)

# To see the explored paramaters:
cg$lambda_w
cg$lambda_b
cg$c
```

To select the best combination of hyperparameters, we will use `stars_coglasso()`. This function implements a coglasso-adapted version of *StARS*, the stability selection method developed by Liu, Roeder and Wasserman ([2010](#ref)). The usual application of `stars_coglasso()` uses the default options of the function, whose only required input is the object resulting from `coglasso()`.

```{r}
sel_cg <- stars_coglasso(cg, verbose = FALSE)

# To see the selected parameters:
sel_cg$sel_lambda_w
sel_cg$sel_lambda_b
sel_cg$sel_c
```

With this we have selected the combination of hyperparameters yielding the most stable, yet sparse coglasso network. The adjacency matrix of the selected network is stored in the object `sel_cg$sel_adj`. We can use this matrix to display the selected network with the R package `igraph`.

```{r}
# To create the igraph object from the selected adjacency matrix:
sel_graph <- igraph::graph.adjacency(sel_cg$sel_adj, mode = "undirected")

# Setting some graphical parameters and removing disconnected nodes from the graph
igraph::V(sel_graph)$label <- colnames(multi_omics_sd_small)
igraph::V(sel_graph)$color <- c(rep("#00ccff", 14), rep("#ff9999", 5))
igraph::V(sel_graph)$frame.color <- c(rep("#002060", 14), rep("#800000", 5))
igraph::V(sel_graph)$frame.width <- 2
igraph::V(sel_graph)$size <- c(30)
igraph::E(sel_graph)$width <- 2

lo <- igraph::layout_with_fr(sel_graph)
diconnected <- which(igraph::degree(sel_graph) == 0)
sel_graph2 <- igraph::delete.vertices(sel_graph, diconnected)
lo2 <- lo[-diconnected, ]

# Plotting
plot(sel_graph2, layout = lo2)
```

## References {#ref}

  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