---
title: "Getting Started with MOFSR"
author: "Zaoqu Liu"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{Getting Started with MOFSR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE,
  eval = FALSE
)
```

## Introduction

**MOFSR** (Multi-Omics Fusion for Subtype Recognition) is a comprehensive R package for integrative analysis of multi-omics data. The package provides:

- **15 multi-omics clustering algorithms** with internal implementations
- **17 classification methods** for subtype prediction
- **Comprehensive visualization** tools
- **Parallel computing** support for large-scale analyses

### Author

**Zaoqu Liu**

- Email: liuzaoqu@163.com
- GitHub: [https://github.com/Zaoqu-Liu](https://github.com/Zaoqu-Liu)
- ORCID: [0000-0002-0452-742X](https://orcid.org/0000-0002-0452-742X)

## Installation

```{r eval=FALSE}
# From R-Universe (Recommended)
install.packages("MOFSR", repos = "https://zaoqu-liu.r-universe.dev")

# From GitHub
remotes::install_github("Zaoqu-Liu/MOFSR")
```

## Quick Start

### Generate Simulated Multi-Omics Data

```{r generate-data}
library(MOFSR)
set.seed(42)

# Simulate three-omics data with 3 subtypes
n_samples <- 60
n_features <- c(500, 200, 100)  # mRNA, miRNA, methylation

# Generate cluster labels
true_clusters <- rep(1:3, each = 20)

# Generate data with cluster structure
generate_omics <- function(n, p, clusters) {
  n_clusters <- length(unique(clusters))
  centers <- matrix(rnorm(n_clusters * p, sd = 2), n_clusters, p)
  data <- t(sapply(clusters, function(k) {
    centers[k, ] + rnorm(p, sd = 1)
  }))
  colnames(data) <- paste0("Feature_", seq_len(p))
  rownames(data) <- paste0("Sample_", seq_len(n))
  return(t(data))  # Return features x samples
}

data_list <- list(
  mRNA = generate_omics(n_samples, n_features[1], true_clusters),
  miRNA = generate_omics(n_samples, n_features[2], true_clusters),
  methylation = generate_omics(n_samples, n_features[3], true_clusters)
)

cat("Data dimensions:\n")
sapply(data_list, dim)
```

### Run SNF Clustering

```{r snf-clustering}
# Run Similarity Network Fusion
result_snf <- run_snf(data_list, n_clusters = 3)

# View results
head(result_snf)

# Calculate Adjusted Rand Index
ari <- .adjusted_rand_index(result_snf$Cluster, true_clusters)
cat("\nAdjusted Rand Index (SNF):", round(ari, 4))
```

### UMAP Visualization

```{r umap-viz, fig.cap="UMAP visualization of multi-omics clustering results"}
# Compute UMAP coordinates
umap_coords <- compute_umap(data_list, n_epochs = 100, seed = 42)

# Plot with cluster colors
plot_umap(umap_coords, result_snf, title = "SNF Clustering Results")
```

## Compare Multiple Algorithms

```{r compare-algorithms}
# Run multiple algorithms
algorithms <- c("SNF", "RGCCA", "CPCA")

results <- lapply(algorithms, function(alg) {
  run_integration(data_list, algorithm = alg, n_clusters = 3)
})
names(results) <- algorithms

# Compare clustering agreement
ari_matrix <- compare_clusterings(results)
print(round(ari_matrix, 3))
```

```{r compare-viz, fig.cap="Algorithm agreement measured by Adjusted Rand Index"}
plot_algorithm_comparison(results, title = "Algorithm Agreement (ARI)")
```

## Consensus Clustering

```{r consensus, fig.cap="Consensus matrix heatmap showing clustering stability"}
# Run consensus clustering on mRNA data
cc_result <- consensus_cluster(data_list$mRNA, maxK = 5, reps = 50, seed = 42)

# Plot consensus heatmap for K=3
plot_consensus_heatmap(cc_result[[3]]$consensusMatrix, 
                       title = "Consensus Matrix (K=3)")
```

```{r pac, fig.cap="PAC scores for optimal cluster number selection"}
# Calculate PAC for optimal K selection
pac_values <- calc_pac(cc_result)
print(pac_values)

# Plot quality metrics
plot_cluster_quality(pac_values, title = "Cluster Quality (PAC)")
```

## Data Preprocessing

```{r preprocessing}
# Normalize data
data_norm <- normalize_omics(data_list, method = "zscore")

# Filter low-variance features
data_filtered <- filter_low_variance(data_list, min_var = 0.01)

# Quality control summary
qc <- qc_summary(data_list)
print(qc)
```

## Session Info

```{r session}
sessionInfo()
```