---
title: "Visualization Gallery in sciNOME"
author: "Shitao Zhou"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
    toc: true
vignette: >
  %\VignetteIndexEntry{Visualization Gallery in sciNOME}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = TRUE,     # CRITICAL: Ensures all plots are generated during BiocCheck
  warning = FALSE,
  message = FALSE,
  fig.width = 8,
  fig.height = 6
)
```

## Introduction

The `sciNOME` package provides an extensive suite of academic-grade plotting functions for Single-Cell RNA, Epigenetic, and Multi-Omics integrated data. 

In this vignette, we demonstrate the usage of these functions using minimal mock datasets to ensure rapid compilation without relying on external large data files.

```{r load-pkg}
library(sciNOME)
library(ggplot2)
# Ensure you have patchwork, ggridges, ggrepel, and ggpubr installed
```

## 1. Single-Cell RNA Visualization

We first create a tiny mock `RNA` object.

```{r rna-mock}
set.seed(42)
# Mock metadata
mock_meta <- data.frame(
  orig.ident = rep(c("Tumor", "Normal"), each = 20),
  nFeature_RNA = runif(40, 200, 2500),
  nCount_RNA = runif(40, 500, 8000),
  percent_mt = runif(40, 0, 10),
  Auto_Cluster = rep(c("C1", "C2", "C3", "C4"), each = 10)
)
rownames(mock_meta) <- paste0("Cell_", 1:40)

# Mock UMAP coordinates
mock_umap <- matrix(rnorm(80), ncol = 2)
rownames(mock_umap) <- rownames(mock_meta)
colnames(mock_umap) <- c("UMAP_1", "UMAP_2")

# Mock Pseudotime
mock_pt <- list(
  pseudotime = runif(40, 0, 1),
  dr_method = "umap",
  group_col = "Auto_Cluster",
  algorithm = "cluster",
  start_clus = "C1",
  curve_coords = data.frame(Dim1 = rnorm(40), Dim2 = rnorm(40))
)

# Assemble RNA object
mock_rna_obj <- list(
  meta.data = mock_meta,
  filter_meta.data = mock_meta,
  reductions = list(umap = mock_umap, pseudotime = mock_pt)
)
class(mock_rna_obj) <- "RNA"
```

### 1.1 Quality Control Plot
```{r rna-qc}
PlotQC_RNA(mock_rna_obj, group_col = "orig.ident")
```

### 1.2 Dimensionality Reduction Plot
Set `show_cluster = TRUE` to plot custom groups alongside unsupervised clusters.
```{r rna-dim}
if (requireNamespace("patchwork", quietly = TRUE)) {
  PlotDimRed_RNA(mock_rna_obj, reduction = "umap", group_col = "orig.ident", show_cluster = TRUE)
}
```

### 1.3 Volcano Plot for DEA
```{r rna-volcano}
mock_dea <- data.frame(
  gene = paste0("Gene_", 1:50),
  avg_log2FC = rnorm(50, 0, 1.5),
  p_val_adj = runif(50, 0, 0.1)
)
mock_dea$avg_log2FC[1:5] <- runif(5, 2, 4)
mock_dea$p_val_adj[1:5] <- runif(5, 1e-6, 1e-3)

if (requireNamespace("ggrepel", quietly = TRUE)) {
  PlotVolcano_RNA(mock_dea, fc_cut = 1, p_cut = 0.05, top_n = 5)
}
```

### 1.4 Trajectory & Pseudotime Plot
```{r rna-pseudo}
if (requireNamespace("patchwork", quietly = TRUE)) {
  PlotPseudo_RNA(mock_rna_obj)
}
```

## 2. Epigenetic Visualization

We simulate a small methylation `.level` matrix and its corresponding metadata.

```{r epi-mock}
set.seed(123)
mock_epi_mat <- matrix(runif(200, min = 0, max = 1), nrow = 10, ncol = 20)
colnames(mock_epi_mat) <- paste0("Sample_", 1:20)
rownames(mock_epi_mat) <- paste0("Region_", 1:10)

mock_epi_meta <- data.frame(
  SampleID = paste0("Sample_", 1:20),
  Condition = rep(c("Tumor", "Normal"), each = 10)
)
```

### 2.1 Epigenetic Landscape (Ridge Plot)
```{r epi-ridge}
if (requireNamespace("ggridges", quietly = TRUE)) {
  PlotLandscape_Epi(
    mat = mock_epi_mat, 
    meta = mock_epi_meta, 
    sample_col = "SampleID", 
    group_col = "Condition"
  )
}
```

### 2.2 Dimensionality Reduction for Epigenetics
```{r epi-dim}
mock_epi_dr <- data.frame(
  Dim1 = rnorm(20), 
  Dim2 = rnorm(20),
  Condition = rep(c("Tumor", "Normal"), each = 10)
)

PlotDimRed_Epi(mock_epi_dr, group_col = "Condition", method = "PCA")
```

### 2.3 Epigenetic Volcano Plot
```{r epi-volcano}
mock_epi_dea <- data.frame(
  chrdata = paste0("chr1:", 1001:1050),
  Diff = rnorm(50, mean = 0, sd = 1),
  P.Value = runif(50, min = 0, max = 0.5)
)
mock_epi_dea$Diff[1:3] <- c(1.5, 2.0, -1.8)
mock_epi_dea$P.Value[1:3] <- c(0.001, 0.0005, 0.002)

if (requireNamespace("ggrepel", quietly = TRUE)) {
  PlotVolcano_Epi(mock_epi_dea, metric_col = "Diff", th_effect = 1.0, top_n = 3)
}
```

## 3. Multi-Omics Integration Plots

Finally, we simulate an integrated dataframe containing RNA Expression, CpG Methylation, and GpC Accessibility.

```{r multi-mock}
set.seed(42)
mock_multi <- data.frame(
  RNA_Exp = rnorm(40, mean = 10, sd = 2),
  CpG_level = runif(40, 0, 1),
  GpC_level = runif(40, 0, 1)
)
# Add some artificial correlation
mock_multi$RNA_Exp <- mock_multi$RNA_Exp - (mock_multi$CpG_level * 3)
```

### 3.1 Omics Correlation Matrix
```{r multi-matrix, fig.width=10, fig.height=8}
if (requireNamespace("patchwork", quietly = TRUE)) {
  PlotOmicsMatrix(mock_multi, cor_method = "spearman")
}
```

### 3.2 Pairwise Scatter Plots
```{r multi-scatter, fig.width=12, fig.height=4}
if (requireNamespace("ggpubr", quietly = TRUE) && requireNamespace("patchwork", quietly = TRUE)) {
  PlotOmicsScatter(mock_multi)
}
```

## Session Information
```{r session-info}
sessionInfo()
```