---
title: "one_vs_group"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{one_vs_group}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r eval=TRUE, echo=FALSE}
library(neuroSCC)
```

# Introduction

This vignette illustrates a minimal example of a **single patient vs. group** (1 vs Group) SCC analysis with the `neuroSCC` package.

Ensure you have executed the steps from the ["Getting Started"](landing_vignette.html) vignette, or follow the conditional steps below to create required data.

---

# Data Preparation (Conditional)

If you haven't executed the previous vignette, let's conditionally recreate necessary matrices.

```{r}
cat("Creating required matrices from sample data...\n")

databaseControls <- databaseCreator(pattern = "^syntheticControl.*\\.nii\\.gz$", control = TRUE, quiet = TRUE)
databasePathological <- databaseCreator(pattern = "^syntheticPathological1\\.nii\\.gz$", control = FALSE, quiet = TRUE)

matrixControls <- matrixCreator(databaseControls, paramZ = 35, quiet = TRUE)
matrixPathological <- matrixCreator(databasePathological, paramZ = 35, quiet = TRUE)

normalizedMatrix <- meanNormalization(matrixControls)
normalizedPathological <- meanNormalization(matrixPathological)

niftiPath <- system.file("extdata", "syntheticControl1.nii.gz", package = "neuroSCC")
contours <- neuroContour(niftiPath, paramZ = 35, plotResult = FALSE)

cat("Data prepared successfully.\n")
```

---

# Single Patient Data and Cloning

Prepare data for a single patient and generate synthetic Poisson clones to enable SCC computation:

```{r}
# Select the single pathological patient
SCC_AD <- normalizedPathological[1, , drop = FALSE]
SCC_CN <- normalizedMatrix

# Toy parameters for Poisson clone generation (modifiable by user)
numClones <- 2
factorLambda <- 0.1

# Generate synthetic clones
SCC_AD_clones <- generatePoissonClones(SCC_AD, numClones, factorLambda)

# Combine the patient with generated clones
SCC_AD_expanded <- rbind(SCC_AD, SCC_AD_clones)
SCC_AD_expanded <- meanNormalization(SCC_AD_expanded)
```

---

# SCC Estimation (Conditional Execution)

This step requires the external `ImageSCC` package, currently not on CRAN. Ensure you have this package installed. If not, install it using:

```{r}
if (requireNamespace("ImageSCC", quietly = TRUE)) {
  message("'ImageSCC' package is available.")
} else {
  message("This vignette requires the 'ImageSCC' package.")
  message("You can install it from GitHub with:")
  message("  remotes::install_github('FIRST-Data-Lab/ImageSCC')")
}
```


The SCC computation is typically computationally intensive. Here, we skip actual computation and load a precomputed result from the package (`SCCcomp.RData`):


```{r eval = requireNamespace("ImageSCC", quietly = TRUE), echo=TRUE}

# Check for Triangulation package
triangulation_available <- requireNamespace("Triangulation", quietly = TRUE)

if (triangulation_available) {
  message("'Triangulation' package is available.")
} else {
  message("'Triangulation' package not available.")
  message("Install with: remotes::install_github('FIRST-Data-Lab/Triangulation')")
}

# Proceed only if both packages are available
if (triangulation_available) {

  # Try loading precomputed SCC object from data/
  if (!exists("SCCcomp", inherits = FALSE) &&
      "SCCcomp" %in% data(package = "neuroSCC")$results[, "Item"]) {
    
    message("Loading precomputed SCC object from package data...")
    suppressMessages(data("SCCcomp", package = "neuroSCC"))

  } else if (!exists("SCCcomp", inherits = FALSE)) {
    
    message("Precomputed object not found. Running SCC estimation...")

    # 1. Prepare contour and triangulation
    Z <- as.matrix(contours[[1]][, c("x", "y")])
    VT <- Triangulation::TriMesh(contours[[1]], n = 15)
    V <- as.matrix(VT[[1]])
    Tr <- as.matrix(VT[[2]])

    # 2. Run SCC estimation
    SCCcomp <- ImageSCC::scc.image(
      Ya = SCC_AD_expanded,
      Yb = SCC_CN,
      Z = Z,
      d.est = 5,
      d.band = 2,
      r = 1,
      V.est.a = V,
      Tr.est.a = Tr,
      V.band.a = V,
      Tr.band.a = Tr,
      penalty = TRUE,
      lambda = 10^{seq(-6, 3, 0.5)},
      alpha.grid = c(0.10, 0.05, 0.01),
      adjust.sigma = TRUE
    )
  }
}


```

```{r eval=TRUE}
if (!exists("SCCcomp", inherits = FALSE)) {
  data("SCCcomp", package = "neuroSCC")
}
```

---

# Visualizing the SCC Result

Although the main value of a one-vs-group SCC analysis is inferential rather than visual, it can still be useful to inspect the estimated mean function and its SCC bands. The following plot provides a compact visual summary of the one-group SCC object included with the package.

```{r eval=TRUE, fig.alt="Single-group SCC panel for the control group"}
data("sccOneGroup", package = "neuroSCC")

plotSCCpanel(
  scc = sccOneGroup,
  title = "SCC FOR CONTROL GROUP"
)
```

---

# Visualizing Group Means and SCC Differences

Once the SCC result is available, it is often useful to visualize both group mean estimates together with the SCC-detected differences. The panel below provides a compact summary of the two-group comparison.

```{r eval=TRUE, fig.alt="Two-group SCC comparison panel"}
plotSCCcomparisonPanel(
  scc = SCCcomp,
  title = "SCC COMPARISON PANEL"
)
```

---

# Extracting Significant Points and Evaluating Metrics

Once the SCC computation results (`SCCcomp`) are available, we can extract significant points and compute relevant performance metrics:

```{r eval=TRUE}
# Extract significant points
significantPoints <- getPoints(SCCcomp)

# Load example true ROI data from the package
roi_path <- system.file("extdata", "ROIsample_Region2_18.nii.gz", package = "neuroSCC")
trueROI <- processROIs(roi_path, region = "Region2", number = "18", save = FALSE)

# Get dimensions for total coordinates
dimensions <- getDimensions(roi_path)
totalCoords <- expand.grid(x = 1:dimensions$xDim, y = 1:dimensions$yDim)

# Calculate performance metrics
metrics <- calculateMetrics(
  detectedPoints = significantPoints$positivePoints,
  truePoints = trueROI,
  totalCoords = dimensions,
  regionName = "SinglePatient_vs_Group"
)

# Display metrics
print(metrics)
```

---

# Visual Validation Against ROI and SPM

In addition to numerical metrics, it can be useful to visually compare the ground-truth ROI, SCC detections, and SPM detections over the same anatomical background. The following panel provides that comparison.

```{r eval=TRUE, fig.alt="Validation panel comparing ROI, SCC, and SPM detections"}
paramZ <- 35

controlPattern <- "^syntheticControl.*\\.nii\\.gz$"
databaseCN <- databaseCreator(pattern = controlPattern, control = TRUE, quiet = TRUE)
matrixCN <- matrixCreator(database = databaseCN, paramZ = paramZ, quiet = TRUE)
matrixCN <- meanNormalization(matrixCN)

roiFile <- system.file("extdata", "ROIsample_Region2_18.nii.gz", package = "neuroSCC")
truePoints <- processROIs(roiFile, region = "Region2", number = "18", save = FALSE)
roiPoints <- subset(truePoints, z == paramZ & pet == 1, select = c("x", "y"))

spmFile <- system.file("extdata", "binary.nii.gz", package = "neuroSCC")
spmPoints <- getSPMbinary(spmFile, paramZ = paramZ)

plotValidationPanel(
  template = roiFile,
  backgroundMatrix = matrixCN,
  roiPoints = roiPoints,
  sccPoints = list(
    positivePoints = getPoints(SCCcomp)$positivePoints,
    negativePoints = data.frame(x = numeric(0), y = numeric(0))
  ),
  spmPoints = spmPoints,
  title = "Performance Validation Panel",
  label1 = "Ground Truth (ROI)",
  label2 = "SCC Detected",
  label3 = "SPM Detected"
)
```

---

# Conclusion

This vignette provides a reproducible example of conducting a **single patient vs group SCC analysis** using `neuroSCC`. Due to computational constraints, actual SCC computations are skipped and precomputed data is loaded instead. Users are encouraged to adapt parameters and explore further on their datasets.