---
title: "OmicNetR: Integrative Multi-Omics with Sparse CCA"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{OmicNetR: Integrative Multi-Omics with Sparse CCA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 7
)
set.seed(123)
```

## Overview

**OmicNetR** provides a minimal, reproducible workflow for integrating two omics layers
(e.g., gene expression and metabolomics) using sparse canonical correlation analysis (sCCA).

This vignette demonstrates:

1. generating (or loading) two omics matrices,
2. aligning samples,
3. fitting sCCA,
4. creating a bipartite network,
5. plotting key visual summaries.

## Load OmicNetR

```{r}
library(OmicNetR)
```

## 1. Generate example data

For demonstration we use simulated multi-omics data. Replace this section with your own matrices
in real analyses.

```{r}
omics_data <- generate_dummy_omics(
  n_samples = 60,
  n_genes = 800,
  n_metabolites = 150,
  n_linked = 20
)

X <- omics_data$X
Y <- omics_data$Y
```

## 2. Align samples

Alignment ensures that `X` and `Y` contain the same samples in the same order.

```{r}
aligned <- align_omics(X, Y)
X_aligned <- aligned$X
Y_aligned <- aligned$Y
```

## 3. Fit sparse CCA

The `penalty_X` and `penalty_Y` parameters control sparsity (higher = sparser).

```{r}
scca_model <- omic_scca(
  X = X_aligned,
  Y = Y_aligned,
  n_components = 2,
  penalty_X = 0.70,
  penalty_Y = 0.70
)
str(scca_model, max.level = 1)
```

## 4. Convert loadings to an interaction network

Edges are formed by pairing selected genes and metabolites and filtering by the absolute product
of weights (`weight_threshold`).

```{r}
net_data <- scca_to_network(
  scca_model,
  comp_select = 1,
  weight_threshold = 0.01
)

# Keep the strongest edges for readability
net_data <- net_data[order(abs(net_data$Weight_Product), decreasing = TRUE), ]
net_data <- head(net_data, 50)
head(net_data)
```

## 5. Visualize results

### Bipartite network

```{r}
plot_bipartite_network(net_data)
```

### Feature importance circle plot

```{r}
p <- plot_pathway_circle(scca_model, top_features = 30, pathway_db = "KEGG")
p
```

### Correlation heatmap

This heatmap summarizes correlations between the top sCCA-selected features from each omic layer.

```{r}
plot_correlation_heatmap(scca_model = scca_model, X = X_aligned, Y = Y_aligned, top_n = 25)
```

## Notes for CRAN

- This vignette uses simulated data and avoids writing files to the package directory.
- Plots are created in-memory and rendered by knitr.