---
title: "Visualization Gallery"
author: "Zaoqu Liu"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Visualization Gallery}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 6,
  message = FALSE,
  warning = FALSE
)
```

## Overview

SecAct provides a comprehensive suite of visualization functions for exploring secreted protein activity results. This gallery showcases all available plotting functions with customization options.

```{r load}
library(SecAct)
library(ggplot2)

# Load and process data
data_path <- system.file("extdata/GSE100093.IFNG.expr.gz", package = "SecAct")
expr <- read.table(gzfile(data_path), sep = "\t", header = TRUE, row.names = 1)

# Run activity inference
result <- SecAct.activity.inference(expr[, 1:8], lambda = 5e5, nrand = 100)
```

## Activity Heatmaps

### Basic Heatmap

```{r heatmap-basic, fig.cap="Basic activity heatmap"}
# Select top 20 most variable secreted proteins
var_sp <- apply(result$zscore, 1, var)
top20 <- names(sort(var_sp, decreasing = TRUE))[1:20]

SecAct.heatmap.plot(result$zscore[top20, ])
```

### Customized Colors

```{r heatmap-colors, fig.cap="Heatmap with custom color palette"}
# Use different color scheme
SecAct.heatmap.plot(
  result$zscore[top20, ],
  title = "Secreted Protein Activity",
  colors = c("#2166AC", "#F7F7F7", "#B2182B")  # Blue-white-red
)
```

### Divergent Scale

```{r heatmap-divergent, fig.cap="Heatmap with divergent scale"}
# Center around zero for better visualization
SecAct.heatmap.plot(
  result$zscore[top20, ],
  title = "Activity Z-scores",
  colors = c("#4575B4", "#91BFDB", "#E0F3F8", "#FFFFBF", 
             "#FEE090", "#FC8D59", "#D73027")
)
```

## Bar Plots

### Basic Bar Plot

```{r bar-basic, fig.cap="Basic bar plot"}
# Get top up/down regulated secreted proteins
activities <- result$zscore[, 1]
n <- 10
top_up <- names(sort(activities, decreasing = TRUE))[1:n]
top_down <- names(sort(activities))[1:n]
selected <- c(top_up, top_down)

SecAct.bar.plot(activities[selected])
```

### With Title

```{r bar-titled, fig.cap="Bar plot with custom title"}
SecAct.bar.plot(
  activities[selected],
  title = "Sample 1: Top Active Secreted Proteins"
)
```

### Custom Colors

```{r bar-colors, fig.cap="Bar plot with custom colors"}
SecAct.bar.plot(
  activities[selected],
  title = "Activity Change",
  colors = c("#3288BD", "#D53E4F")  # Blue for down, red for up
)
```

## Lollipop Plots

### Basic Lollipop

```{r lollipop-basic, fig.cap="Basic lollipop plot"}
SecAct.lollipop.plot(activities[selected])
```

### With Title

```{r lollipop-titled, fig.cap="Lollipop plot with title"}
SecAct.lollipop.plot(
  activities[selected],
  title = "Secreted Protein Activity Rankings"
)
```

## Combining Plots

### Side-by-Side Comparison

```{r comparison, fig.width=12, fig.height=5, fig.cap="Comparing multiple samples"}
# Compare two samples
sample1_act <- result$zscore[selected, 1]
sample2_act <- result$zscore[selected, 2]

p1 <- SecAct.bar.plot(sample1_act, title = "Sample 1")
p2 <- SecAct.bar.plot(sample2_act, title = "Sample 2")

# Use patchwork to combine
if(requireNamespace("patchwork", quietly = TRUE)) {
  patchwork::wrap_plots(p1, p2, ncol = 2)
}
```

## Publication-Ready Figures

### Customized Theme

```{r publication, fig.cap="Publication-quality visualization"}
# Create a polished visualization
activities_df <- data.frame(
  protein = factor(names(activities[selected]), levels = names(sort(activities[selected]))),
  zscore = activities[selected],
  direction = ifelse(activities[selected] > 0, "Upregulated", "Downregulated")
)

ggplot(activities_df, aes(x = protein, y = zscore, fill = direction)) +
  geom_bar(stat = "identity", width = 0.8) +
  coord_flip() +
  scale_fill_manual(values = c("Downregulated" = "#4575B4", "Upregulated" = "#D73027")) +
  labs(
    title = "Secreted Protein Activity Profile",
    subtitle = "Sample 1 vs Background",
    x = NULL,
    y = "Activity Z-score",
    fill = "Direction"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5, color = "gray50"),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    legend.position = "bottom"
  )
```

## Statistical Visualization

### P-value Distribution

```{r pvalue-dist, fig.cap="P-value distribution"}
pvals <- as.vector(result$pvalue)

par(mfrow = c(1, 2))

# Histogram
hist(pvals, breaks = 50, col = "steelblue", border = "white",
     main = "P-value Distribution", xlab = "P-value", 
     xlim = c(0, 1))
abline(v = 0.05, col = "red", lty = 2, lwd = 2)

# QQ plot
qqplot(qunif(ppoints(length(pvals))), pvals,
       main = "P-value QQ Plot",
       xlab = "Expected (Uniform)", ylab = "Observed")
abline(0, 1, col = "red", lty = 2)
```

### Volcano Plot

```{r volcano, fig.cap="Volcano plot of activity results"}
# Create volcano plot data
volcano_data <- data.frame(
  zscore = result$zscore[, 1],
  pvalue = result$pvalue[, 1],
  neg_log_p = -log10(result$pvalue[, 1])
)
volcano_data$significant <- abs(volcano_data$zscore) > 2 & volcano_data$pvalue < 0.05

ggplot(volcano_data, aes(x = zscore, y = neg_log_p, color = significant)) +
  geom_point(alpha = 0.6, size = 1.5) +
  scale_color_manual(values = c("FALSE" = "gray70", "TRUE" = "red")) +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "blue") +
  geom_vline(xintercept = c(-2, 2), linetype = "dashed", color = "blue") +
  labs(
    title = "Volcano Plot",
    x = "Activity Z-score",
    y = "-log10(P-value)",
    color = "Significant"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
```

## Sample Correlation

```{r sample-cor, fig.cap="Sample correlation heatmap"}
# Calculate sample correlation
sample_cor <- cor(result$zscore, method = "spearman")

# Simple heatmap using base R
heatmap(sample_cor, 
        main = "Sample Activity Correlation",
        col = colorRampPalette(c("#4575B4", "white", "#D73027"))(100),
        symm = TRUE)
```

## Tips for Better Visualizations

### 1. Choose Appropriate Color Palettes

```{r color-tips, fig.height=3}
# Demonstrate different palettes
par(mfrow = c(1, 3), mar = c(2, 2, 2, 1))

# Sequential (for magnitude)
image(1:100, 1, as.matrix(1:100), col = colorRampPalette(c("white", "#D73027"))(100),
      main = "Sequential", xaxt = "n", yaxt = "n", xlab = "", ylab = "")

# Divergent (for positive/negative)
image(1:100, 1, as.matrix(-50:49), col = colorRampPalette(c("#4575B4", "white", "#D73027"))(100),
      main = "Divergent", xaxt = "n", yaxt = "n", xlab = "", ylab = "")

# Qualitative (for categories)
image(1:8, 1, as.matrix(1:8), col = RColorBrewer::brewer.pal(8, "Set2"),
      main = "Qualitative", xaxt = "n", yaxt = "n", xlab = "", ylab = "")
```

### 2. Consider Your Audience

- **Presentations**: Use high contrast, large labels
- **Publications**: Use grayscale-friendly palettes
- **Interactive**: Consider plotly for web

### 3. Always Include Context

- Axis labels with units
- Meaningful titles
- Sample/condition annotations
- Statistical thresholds

## Session Info

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