### Install stable release of BadranSeq

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Installs the latest tagged version of the package using pak or devtools.

```r
# pak (recommended)
pak::pkg_install("wolf5996/BadranSeq@v1.3.1")

# devtools
devtools::install_github("wolf5996/BadranSeq", ref = "v1.3.1")
```

--------------------------------

### Install development version of BadranSeq

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Installs the latest changes from the main branch, which may include unreleased features.

```r
# pak
pak::pkg_install("wolf5996/BadranSeq")

# devtools
devtools::install_github("wolf5996/BadranSeq")
```

--------------------------------

### select_cells_interactive

Source: https://context7.com/wolf5996/badranseq/llms.txt

Launches a Shiny application for interactive cell selection.

```APIDOC
## select_cells_interactive

### Description
Launches a Shiny application for interactive cell selection with additive/subtractive brush selection.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **return_cells** (logical) - Optional - If TRUE, returns cell names; otherwise returns subsetted Seurat object.
- **reduction** (string) - Optional - Reduction to use for plotting.
- **color_by** (string) - Optional - Metadata column to color cells by.
```

--------------------------------

### Create Publication-Ready UMAP Plots

Source: https://context7.com/wolf5996/badranseq/llms.txt

Use do_UmapPlot for UMAP visualization with enhanced aesthetics. It supports split-by conditions with silhouette backgrounds, cell borders, boxed labels, and automatic rasterization for large datasets. Requires the BadranSeq library.

```r
library(BadranSeq)
data(pbmc3k)

# Basic UMAP plot with default styling
p1 <- do_UmapPlot(pbmc3k)

# Group by specific metadata column
p2 <- do_UmapPlot(pbmc3k, group.by = "seurat_annotations")

# Split by condition with silhouette background
p3 <- do_UmapPlot(pbmc3k, split.by = "condition")

# Customize appearance
p4 <- do_UmapPlot(
  pbmc3k,
  group.by = "seurat_clusters",
  label = TRUE,
  label.size = 5,
  pt.size = 1.5,
  pt.alpha = 0.8,
  plot_cell_borders = TRUE,
  border.size = 2,
  border.color = "black",
  show.legend = TRUE,
  ncol = 2  # For split panels
)

# Multiple grouping variables (returns patchwork)
p5 <- do_UmapPlot(pbmc3k, group.by = c("seurat_clusters", "seurat_annotations"))
```

--------------------------------

### Interactive Cell Selection Workflow

Source: https://context7.com/wolf5996/badranseq/llms.txt

Demonstrates a common workflow: interactively select cells, obtain their names, subset the Seurat object, and then perform downstream analysis like differential expression.

```r
# Workflow: select cells, then analyze
selected <- select_cells_interactive(pbmc3k, return_cells = TRUE)
if (!is.null(selected)) {
  # Create subset
  pbmc_subset <- subset(pbmc3k, cells = selected)
  # Run differential expression
  markers <- Seurat::FindMarkers(pbmc3k, ident.1 = selected)
}
```

--------------------------------

### Interactive Cell Selection - Color by Metadata

Source: https://context7.com/wolf5996/badranseq/llms.txt

Launches the interactive cell selection tool, coloring cells in the visualization by a specified metadata column (e.g., 'seurat_annotations'). Aids in selecting cells based on biological groups.

```r
# Color by specific metadata
seu_subset <- select_cells_interactive(
  pbmc3k,
  reduction = "umap",
  color_by = "seurat_annotations"
)
```

--------------------------------

### Create statistical violin plots

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Generates violin plots with integrated statistical testing using do_StatsViolinPlot.

```r
do_StatsViolinPlot(pbmc3k, features = "CD3D",
                   group.by = "seurat_annotations",
                   group.levels = c("Naive CD4 T", "Memory CD4 T", "CD8 T"))
```

--------------------------------

### Compare UMAP plots

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Demonstrates the visual difference between standard Seurat DimPlot and BadranSeq do_UmapPlot.

```r
library(BadranSeq)
library(Seurat)
library(ggplot2)
library(patchwork)
data(pbmc3k)

p1 <- DimPlot(pbmc3k, reduction = "umap") + ggtitle("Seurat::DimPlot()")
p2 <- do_UmapPlot(pbmc3k) + ggtitle("BadranSeq::do_UmapPlot()")
p1 + p2
```

--------------------------------

### Interactive Tools

Source: https://github.com/wolf5996/badranseq/blob/main/NEWS.md

Functions for interactive exploration of Seurat objects.

```APIDOC
## seurat_sleepwalk

### Description
Launches an interactive sleepwalk session for exploring cell-to-cell distances in embedding space.

## select_cells_interactive

### Description
Provides a Shiny app with brush selection for interactively selecting cells from embeddings.
```

--------------------------------

### Generate split-panel UMAP plots

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Uses do_UmapPlot to render split-panel visualizations with grey silhouettes to preserve spatial context.

```r
do_UmapPlot(pbmc3k, split.by = "condition")
```

--------------------------------

### Fetch Cell Data with Metadata and PCA

Source: https://context7.com/wolf5996/badranseq/llms.txt

Fetches cell data including specified metadata columns and PCA reduction. Useful for integrating metadata with dimensionality reduction results.

```r
df4 <- fetch_cell_data(
  pbmc3k,
  reductions = "pca",
  dims = 1:10,
  metadata = c("seurat_clusters", "condition")
)
```

--------------------------------

### do_StatsViolinPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates statistical violin plots using ggstatsplot for between-group comparisons with built-in Kruskal-Wallis tests and pairwise comparisons.

```APIDOC
## do_StatsViolinPlot

### Description
Creates statistical violin plots for between-group comparisons with built-in Kruskal-Wallis tests and pairwise comparisons. Ideal for comparing gene expression across cell types or conditions.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **features** (vector) - Required - Features to plot.
- **group.by** (string) - Required - Metadata column to group by.
- **group.levels** (vector) - Optional - Specific levels to include.
- **ncol** (numeric) - Optional - Number of columns for multi-gene plots.
- **type** (string) - Optional - Statistical test type ('parametric', 'nonparametric', 'robust', 'bayes').
- **p.adjust.method** (string) - Optional - Method for p-value adjustment.
- **pairwise.display** (string) - Optional - Display mode for pairwise comparisons ('all', 'significant', 'none').
```

--------------------------------

### Interactive Cell Selection - Return Seurat Object

Source: https://context7.com/wolf5996/badranseq/llms.txt

Launches a Shiny application for interactive cell selection using brush tools. Returns a subsetted Seurat object based on the selected cells.

```r
library(BadranSeq)
data(pbmc3k)

# Interactive selection, returns subsetted Seurat object
seu_subset <- select_cells_interactive(pbmc3k)
```

--------------------------------

### Compare PCA plots with variance annotation

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Shows how BadranSeq automatically computes and formats PCA variance labels on axes.

```r
p1 <- DimPlot(pbmc3k, reduction = "pca") + ggtitle("Seurat::DimPlot(reduction = 'pca')")
p2 <- do_PcaPlot(pbmc3k) + ggtitle("BadranSeq::do_PcaPlot()")
p1 + p2
```

--------------------------------

### do_UmapPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates a UMAP dimensionality reduction plot with publication-ready styling, including options for split panels with silhouette backgrounds, cell borders, boxed cluster labels, vivid colors, and automatic rasterization for large datasets.

```APIDOC
## do_UmapPlot

### Description
Creates a UMAP dimensionality reduction plot with publication-ready styling. When `split.by` is used, shows each split category with a grey silhouette of all other cells in the background, preserving spatial context across panels. Features cell borders, boxed cluster labels, vivid colors, and automatic rasterization for large datasets.

### Method
Not specified (assumed to be a function call within R)

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
library(BadranSeq)
data(pbmc3k)

# Basic UMAP plot with default styling
p1 <- do_UmapPlot(pbmc3k)

# Group by specific metadata column
p2 <- do_UmapPlot(pbmc3k, group.by = "seurat_annotations")

# Split by condition with silhouette background
p3 <- do_UmapPlot(pbmc3k, split.by = "condition")

# Customize appearance
p4 <- do_UmapPlot(
  pbmc3k,
  group.by = "seurat_clusters",
  label = TRUE,
  label.size = 5,
  pt.size = 1.5,
  pt.alpha = 0.8,
  plot_cell_borders = TRUE,
  border.size = 2,
  border.color = "black",
  show.legend = TRUE,
  ncol = 2  # For split panels
)

# Multiple grouping variables (returns patchwork)
p5 <- do_UmapPlot(pbmc3k, group.by = c("seurat_clusters", "seurat_annotations"))
```

### Response
#### Success Response (200)
Returns a ggplot object representing the UMAP plot.

#### Response Example
(ggplot object, not directly representable as JSON)
```

--------------------------------

### Interactive Cell Selection - Different Reduction

Source: https://context7.com/wolf5996/badranseq/llms.txt

Performs interactive cell selection using a specified reduction (e.g., 'pca') instead of the default 'umap'. Allows selection based on different embedding spaces.

```r
# Use different reduction
seu_subset <- select_cells_interactive(pbmc3k, reduction = "pca")
```

--------------------------------

### Create Statistical Violin Plots with do_StatsViolinPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Performs between-group statistical comparisons using ggstatsplot, supporting various test types and pairwise annotations.

```r
library(BadranSeq)
data(pbmc3k)

# Basic statistical violin plot
p1 <- do_StatsViolinPlot(
  pbmc3k,
  features = "CD3D",
  group.by = "seurat_annotations"
)

# Compare specific clusters only
p2 <- do_StatsViolinPlot(
  pbmc3k,
  features = "CD3D",
  group.by = "seurat_annotations",
  group.levels = c("Naive CD4 T", "Memory CD4 T", "CD8 T")
)

# Multiple genes with patchwork composition
p3 <- do_StatsViolinPlot(
  pbmc3k,
  features = c("CD3D", "CD8A"),
  group.by = "seurat_clusters",
  ncol = 2
)

# Customize statistical tests
p4 <- do_StatsViolinPlot(
  pbmc3k,
  features = "CD3D",
  group.by = "condition",
  type = "parametric",           # or "nonparametric", "robust", "bayes"
  p.adjust.method = "bonferroni",
  pairwise.display = "all"       # or "significant", "none"
)

# Hide pairwise brackets, keep omnibus test
p5 <- do_StatsViolinPlot(
  pbmc3k,
  features = "CD3D",
  group.by = "seurat_annotations",
  pairwise.display = "none"
)
```

--------------------------------

### Explore Embedding Distances with seurat_sleepwalk

Source: https://context7.com/wolf5996/badranseq/llms.txt

Launches the sleepwalk interactive explorer to visualize relationships between cell proximity in an embedding (default UMAP) and feature space (default PCA).

```r
library(BadranSeq)
data(pbmc3k)

# Default: UMAP embedding, PCA features
seurat_sleepwalk(pbmc3k)
```

--------------------------------

### Create Sankey Diagrams with do_SankeyPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Visualizes relationships between categorical metadata variables using alluvial flows.

```r
library(BadranSeq)
data(pbmc3k)

# Two-way relationship: clusters to annotations
p1 <- do_SankeyPlot(
  pbmc3k,
  columns = c("seurat_clusters", "seurat_annotations")
)

# Three-way relationship
p2 <- do_SankeyPlot(
  pbmc3k,
  columns = c("seurat_annotations", "condition", "Phase")
)

# Customize label size
p3 <- do_SankeyPlot(
  pbmc3k,
  columns = c("seurat_clusters", "seurat_annotations"),
  label.size = 4
)
```

--------------------------------

### do_ViolinPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates publication-ready violin plots with optional boxplot overlay, jittered raw data points, and centrality markers.

```APIDOC
## do_ViolinPlot

### Description
Creates publication-ready violin plots with optional boxplot overlay, jittered raw data points, and centrality markers (median/mean diamond with numeric label). Supports both gene expression and numeric metadata columns in the same plot.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **features** (vector) - Required - Features (genes or metadata) to plot.
- **group.by** (string) - Optional - Metadata column to group by.
- **pt.size** (numeric) - Optional - Size of raw data points.
- **pt.alpha** (numeric) - Optional - Alpha transparency of raw data points.
- **violin.alpha** (numeric) - Optional - Alpha transparency of the violin plot.
- **violin.width** (numeric) - Optional - Width of the violin plot.
- **boxplot** (boolean) - Optional - Whether to include a boxplot overlay.
- **boxplot.width** (numeric) - Optional - Width of the boxplot.
- **centrality** (boolean) - Optional - Whether to include centrality markers.
- **centrality.fn** (string) - Optional - Function to calculate centrality ('median' or 'mean').
```

--------------------------------

### Create Violin Plots with do_ViolinPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates violin plots for gene expression or metadata with optional boxplot overlays and centrality markers.

```r
library(BadranSeq)
data(pbmc3k)

# Single gene violin plot
p1 <- do_ViolinPlot(pbmc3k, features = "CD3D")

# Multiple genes
p2 <- do_ViolinPlot(pbmc3k, features = c("CD3D", "CD8A", "CD14"))

# Mix genes and metadata columns
p3 <- do_ViolinPlot(pbmc3k, features = c("CD3D", "nCount_RNA", "percent.mt"))

# Custom grouping and styling
p4 <- do_ViolinPlot(
  pbmc3k,
  features = "CD3D",
  group.by = "seurat_annotations",
  pt.size = 2,
  pt.alpha = 0.3,
  violin.alpha = 0.3,
  violin.width = 0.6,
  boxplot = TRUE,
  boxplot.width = 0.2,
  centrality = TRUE,
  centrality.fn = "median"  # or "mean"
)

# Without boxplot and centrality markers
p5 <- do_ViolinPlot(
  pbmc3k,
  features = "CD3D",
  boxplot = FALSE,
  centrality = FALSE
)
```

--------------------------------

### Interactive Cell Selection - Return Cell Names

Source: https://context7.com/wolf5996/badranseq/llms.txt

Launches an interactive cell selection tool that returns only the character vector of selected cell names, rather than a subsetted Seurat object.

```r
# Return cell names instead of Seurat object
cell_names <- select_cells_interactive(pbmc3k, return_cells = TRUE)
```

--------------------------------

### Custom ggplot2 Visualization with Fetched Cell Data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates a custom ggplot2 scatter plot using fetched cell data, coloring points by 'seurat_clusters'. Requires the ggplot2 library and a 'theme_badranseq' theme.

```r
library(ggplot2)
df <- fetch_cell_data(pbmc3k, reductions = "umap")
ggplot(df, aes(x = UMAP1, y = UMAP2, color = seurat_clusters)) +
  geom_point() +
  theme_badranseq()
```

--------------------------------

### Visualisation Functions

Source: https://github.com/wolf5996/badranseq/blob/main/NEWS.md

Functions for generating publication-ready plots from Seurat objects.

```APIDOC
## do_DimPlot

### Description
Provides a unified entry point for dimensionality reduction plots, routing to specialized handlers based on the reduction argument.

## do_UmapPlot

### Description
Produces publication-ready UMAP plots with cell borders, boxed cluster labels, and a clean theme.

## do_PcaPlot

### Description
Generates PCA plots with automatic variance-explained labels formatted as "PC1 (X.X%)".

## do_FeaturePlot

### Description
Overlays gene expression on embeddings using a viridis color scale with cells ordered by expression.

## do_StatsViolinPlot

### Description
Creates statistical violin plots using ggstatsplot::ggbetweenstats with Seurat-aware data extraction and nonparametric testing by default.

### Parameters
- **group.levels** (vector) - Optional - Subset of groups to compare.
- **pairwise.display** (boolean) - Optional - Controls bracket visibility.
```

--------------------------------

### Create PCA Plots with Automatic Variance Labels

Source: https://context7.com/wolf5996/badranseq/llms.txt

Use do_PcaPlot to generate PCA plots with automatically computed and formatted variance explained percentages on axis labels. Supports split panels and custom grouping. Requires the BadranSeq library.

```r
library(BadranSeq)
data(pbmc3k)

# Basic PCA plot with automatic variance labels
p1 <- do_PcaPlot(pbmc3k)
# Axes labeled as: "PC1 (12.5%)" and "PC2 (8.3%)"

# Plot different PC dimensions
p2 <- do_PcaPlot(pbmc3k, dims = c(2, 3))

# Group by metadata with custom variance precision
p3 <- do_PcaPlot(
  pbmc3k,
  group.by = "seurat_annotations",
  variance_digits = 2,
  label = TRUE,
  label.size = 4
)

# Split panels with silhouette background
p4 <- do_PcaPlot(pbmc3k, split.by = "condition", ncol = 2)

# Custom colors
custom_colors <- c("0" = "#E41A1C", "1" = "#377EB8", "2" = "#4DAF4A")
p5 <- do_PcaPlot(pbmc3k, group.by = "seurat_clusters", colors.use = custom_colors)
```

--------------------------------

### Combine theme_badranseq with Custom Colors

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies the BadranSeq theme and uses custom-defined colors for plot elements, ensuring both aesthetic consistency and specific color choices.

```r
# Combine with BadranSeq colors
colors <- generate_badranseq_colors(3)
p4 <- ggplot(mtcars, aes(mpg, wt, color = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_manual(values = colors) +
  theme_badranseq()
```

--------------------------------

### do_SankeyPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates a Sankey (alluvial) diagram showing relationships between 2-3 categorical metadata variables.

```APIDOC
## do_SankeyPlot

### Description
Creates a Sankey (alluvial) diagram showing relationships between 2-3 categorical metadata variables. Each stratum is labeled and colored; flows connect cells across variables.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **columns** (vector) - Required - Metadata columns to map (2-3 variables).
- **label.size** (numeric) - Optional - Size of the labels.
```

--------------------------------

### Generate BadranSeq Colors with Custom Override

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates a BadranSeq color palette, allowing for custom colors to be provided as an override. Useful for enforcing specific brand or experimental colors.

```r
# Override with custom colors
custom <- c("#E41A1C", "#377EB8", "#4DAF4A")
colors <- generate_badranseq_colors(3, custom.colors = custom)
```

--------------------------------

### Visualize metadata relationships with do_SankeyPlot

Source: https://github.com/wolf5996/badranseq/blob/main/README.md

Generates a Sankey diagram to visualize the distribution of cells across categorical metadata variables.

```r
do_SankeyPlot(pbmc3k, columns = c("seurat_clusters", "seurat_annotations"))
```

--------------------------------

### Create Feature Expression Plots

Source: https://context7.com/wolf5996/badranseq/llms.txt

Use do_FeaturePlot for visualizing gene expression on dimensionality reduction plots with viridis color scales and publication-ready styling. Supports multiple features, custom cutoffs, and split panels. Requires the BadranSeq library.

```r
library(BadranSeq)
data(pbmc3k)

# Single gene expression
p1 <- do_FeaturePlot(pbmc3k, features = "CD3D")

# Multiple genes (returns patchwork)
p2 <- do_FeaturePlot(pbmc3k, features = c("CD3D", "CD8A", "CD14", "MS4A1"))

# Customize viridis palette and cutoffs
p3 <- do_FeaturePlot(
  pbmc3k,
  features = "CD3D",
  viridis.palette = "C",        # Options: A-H
  viridis.direction = -1,       # Reverse scale
  min.cutoff = "q10",           # 10th percentile
  max.cutoff = "q90",           # 90th percentile
  order = TRUE                  # High values on top
)

# PCA with variance labels
p4 <- do_FeaturePlot(pbmc3k, features = "CD3D", reduction = "pca")

# Split by condition
p5 <- do_FeaturePlot(
  pbmc3k,
  features = c("CD3D", "CD14"),
  split.by = "condition",
  pt.size = 0.8,
  plot_cell_borders = TRUE,
  ncol = 2
)
```

--------------------------------

### do_DimPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

A unified wrapper for dimensionality reduction plots that routes to specialized functions for PCA and UMAP, while handling other reductions (harmony, tsne) directly. Provides a single entry point for all reduction types with consistent parameter handling.

```APIDOC
## do_DimPlot

### Description
Unified wrapper for dimensionality reduction plots that routes to specialized functions for PCA and UMAP, while handling other reductions (harmony, tsne) directly. Provides a single entry point for all reduction types with consistent parameter handling.

### Method
Not specified (assumed to be a function call within R)

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
library(BadranSeq)
data(pbmc3k)

# UMAP (default)
p1 <- do_DimPlot(pbmc3k)

# PCA with automatic variance labels
p2 <- do_DimPlot(pbmc3k, reduction = "pca")

# Other reductions (harmony, tsne, etc.)
p3 <- do_DimPlot(pbmc3k, reduction = "harmony")

# Full customization
p4 <- do_DimPlot(
  pbmc3k,
  reduction = "umap",
  dims = c(1, 2),
  group.by = "seurat_annotations",
  split.by = "condition",
  label = TRUE,
  label.box = TRUE,
  pt.size = 1,
  shuffle = TRUE,
  plot_cell_borders = TRUE,
  legend.title = "Cell Type",
  show.legend = TRUE
)
```

### Response
#### Success Response (200)
Returns a ggplot object representing the dimensionality reduction plot.

#### Response Example
(ggplot object, not directly representable as JSON)
```

--------------------------------

### Customize theme_badranseq with Base Font Size

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies the BadranSeq theme with a customized base font size. Useful for adjusting text legibility in plots.

```r
# Customize base font size
p2 <- ggplot(mtcars, aes(mpg, wt, color = factor(cyl))) +
  geom_point() +
  theme_badranseq(base_size = 16)
```

--------------------------------

### Generate BadranSeq Color Palette

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates a categorical color palette matching SCpubr's default. Uses the 'Dark 3' colorspace with adjusted saturation and value for vivid colors.

```r
library(BadranSeq)
library(ggplot2)

# Generate 5 colors
colors <- generate_badranseq_colors(5)
```

--------------------------------

### do_FeaturePlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates feature expression plots overlaid on dimensionality reductions with viridis color scales and publication-ready styling. Supports multiple features with automatic patchwork composition, expression cutoffs, and cell borders.

```APIDOC
## do_FeaturePlot

### Description
Creates feature expression plots overlaid on dimensionality reductions with viridis color scales and publication-ready styling. Supports multiple features with automatic patchwork composition, expression cutoffs, and cell borders.

### Method
Not specified (assumed to be a function call within R)

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
library(BadranSeq)
data(pbmc3k)

# Single gene expression
p1 <- do_FeaturePlot(pbmc3k, features = "CD3D")

# Multiple genes (returns patchwork)
p2 <- do_FeaturePlot(pbmc3k, features = c("CD3D", "CD8A", "CD14", "MS4A1"))

# Customize viridis palette and cutoffs
p3 <- do_FeaturePlot(
  pbmc3k,
  features = "CD3D",
  viridis.palette = "C",        # Options: A-H
  viridis.direction = -1,       # Reverse scale
  min.cutoff = "q10",           # 10th percentile
  max.cutoff = "q90",           # 90th percentile
  order = TRUE                  # High values on top
)

# PCA with variance labels
p4 <- do_FeaturePlot(pbmc3k, features = "CD3D", reduction = "pca")

# Split by condition
p5 <- do_FeaturePlot(
  pbmc3k,
  features = c("CD3D", "CD14"),
  split.by = "condition",
  pt.size = 0.8,
  plot_cell_borders = TRUE,
  ncol = 2
)
```

### Response
#### Success Response (200)
Returns a ggplot object representing the feature expression plot.

#### Response Example
(ggplot object, not directly representable as JSON)
```

--------------------------------

### seurat_sleepwalk with UMAP Embedding and Harmony Features

Source: https://context7.com/wolf5996/badranseq/llms.txt

Visualizes cell relationships in a UMAP embedding based on distances in the 'harmony' feature space. Useful for exploring batch-corrected data structures.

```r
# UMAP embedding with different feature reduction
seurat_sleepwalk(pbmc3k, embedding = "umap", features = "harmony")
```

--------------------------------

### theme_badranseq

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies a publication-ready ggplot2 theme to visualizations.

```APIDOC
## theme_badranseq

### Description
Publication-ready theme function providing consistent SCpubr-like aesthetics.

### Parameters
- **base_size** (numeric) - Optional - Base font size.
- **plot.axes** (logical) - Optional - Whether to display axes.
```

--------------------------------

### generate_badranseq_colors

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates a categorical color palette matching SCpubr's default palette.

```APIDOC
## generate_badranseq_colors

### Description
Generates a categorical color palette using colorspace "Dark 3" with adjusted saturation and value.

### Parameters
- **n** (numeric) - Required - Number of colors to generate.
- **custom.colors** (vector) - Optional - Custom colors to override default palette.
```

--------------------------------

### Unified Dimensionality Reduction Plotting

Source: https://context7.com/wolf5996/badranseq/llms.txt

Use do_DimPlot as a single entry point for UMAP, PCA, and other dimensionality reduction plots (harmony, tsne). It ensures consistent parameter handling across different reduction types. Requires the BadranSeq library.

```r
library(BadranSeq)
data(pbmc3k)

# UMAP (default)
p1 <- do_DimPlot(pbmc3k)

# PCA with automatic variance labels
p2 <- do_DimPlot(pbmc3k, reduction = "pca")

# Other reductions (harmony, tsne, etc.)
p3 <- do_DimPlot(pbmc3k, reduction = "harmony")

# Full customization
p4 <- do_DimPlot(
  pbmc3k,
  reduction = "umap",
  dims = c(1, 2),
  group.by = "seurat_annotations",
  split.by = "condition",
  label = TRUE,
  label.box = TRUE,
  pt.size = 1,
  shuffle = TRUE,
  plot_cell_borders = TRUE,
  legend.title = "Cell Type",
  show.legend = TRUE
)
```

--------------------------------

### seurat_sleepwalk with PCA Embedding and Features

Source: https://context7.com/wolf5996/badranseq/llms.txt

Explores cell relationships using PCA as both the embedding space and the feature space. Useful for understanding structure within the primary components of variation.

```r
# Explore PCA embedding with PCA features
seurat_sleepwalk(pbmc3k, embedding = "pca", features = "pca")
```

--------------------------------

### Custom ggplot2 Visualization with Fetched Feature Data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates a custom ggplot2 scatter plot using fetched feature data, mapping gene expression ('data') to color on a UMAP embedding. Requires ggplot2 and theme_badranseq.

```r
# Custom ggplot2 with fetched data
library(ggplot2)
df <- fetch_feature_data(pbmc3k, features = "CD3D", reductions = "umap")
ggplot(df, aes(x = UMAP1, y = UMAP2, color = data)) +
  geom_point() +
  scale_color_viridis_c() +
  theme_badranseq()
```

--------------------------------

### seurat_sleepwalk

Source: https://context7.com/wolf5996/badranseq/llms.txt

Wrapper for sleepwalk interactive embedding distance explorer.

```APIDOC
## seurat_sleepwalk

### Description
Allows exploration of how cells that are close in the embedding relate to their distances in feature space.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **embedding** (string) - Optional - Embedding to explore.
- **features** (string) - Optional - Feature reduction to compare against.
```

--------------------------------

### Fetch Feature Data - Normalized Expression

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts normalized expression data for specified features in a tidy, long format. Requires the BadranSeq library and loaded data.

```r
library(BadranSeq)
data(pbmc3k)

# Basic usage - normalized expression
df1 <- fetch_feature_data(pbmc3k, features = c("CD3D", "CD8A"))
```

--------------------------------

### Fetch Feature Data - Explicit Assay

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts feature data from a specific assay (e.g., 'RNA') and layer ('counts'). Ensures data is retrieved from the intended source.

```r
# Explicit assay
df4 <- fetch_feature_data(
  pbmc3k,
  features = "CD3D",
  assay = "RNA",
  layer = "counts"
)
```

--------------------------------

### do_PcaPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Creates a PCA dimensionality reduction plot with variance explained percentages automatically added to axis labels. Supports the same silhouette split approach as UMAP plots and allows for custom colors.

```APIDOC
## do_PcaPlot

### Description
Creates a PCA dimensionality reduction plot with variance explained percentages automatically added to axis labels. Computes and formats variance for each principal component without manual calculation. Supports the same silhouette split approach as UMAP plots.

### Method
Not specified (assumed to be a function call within R)

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
library(BadranSeq)
data(pbmc3k)

# Basic PCA plot with automatic variance labels
p1 <- do_PcaPlot(pbmc3k)
# Axes labeled as: "PC1 (12.5%)" and "PC2 (8.3%)"

# Plot different PC dimensions
p2 <- do_PcaPlot(pbmc3k, dims = c(2, 3))

# Group by metadata with custom variance precision
p3 <- do_PcaPlot(
  pbmc3k,
  group.by = "seurat_annotations",
  variance_digits = 2,
  label = TRUE,
  label.size = 4
)

# Split panels with silhouette background
p4 <- do_PcaPlot(pbmc3k, split.by = "condition", ncol = 2)

# Custom colors
custom_colors <- c("0" = "#E41A1C", "1" = "#377EB8", "2" = "#4DAF4A")
p5 <- do_PcaPlot(pbmc3k, group.by = "seurat_clusters", colors.use = custom_colors)
```

### Response
#### Success Response (200)
Returns a ggplot object representing the PCA plot.

#### Response Example
(ggplot object, not directly representable as JSON)
```

--------------------------------

### Apply theme_badranseq to ggplot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies the publication-ready BadranSeq theme to a standard ggplot2 plot. Ensures consistent aesthetics across visualizations.

```r
library(BadranSeq)
library(ggplot2)

# Apply to any ggplot
p1 <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  theme_badranseq()
```

--------------------------------

### Fetch Feature Data - Multiple Layers

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts feature data for a single feature across multiple layers (e.g., counts and normalized data). Useful for comparing different data representations.

```r
# Multiple layers (counts and normalized)
df2 <- fetch_feature_data(
  pbmc3k,
  features = "CD3D",
  layer = c("counts", "data")
)
```

--------------------------------

### Fetch Cell Data with fetch_cell_data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts cell metadata and dimensionality reduction embeddings into a tidy tibble format.

```r
library(BadranSeq)
data(pbmc3k)

# All metadata, no embeddings
df1 <- fetch_cell_data(pbmc3k)

# With UMAP coordinates
df2 <- fetch_cell_data(pbmc3k, reductions = "umap")

# With multiple reductions
df3 <- fetch_cell_data(pbmc3k, reductions = c("umap", "pca"))
```

--------------------------------

### Fetch Cell Data without Metadata

Source: https://context7.com/wolf5996/badranseq/llms.txt

Fetches cell data including only cell IDs and embeddings, with metadata explicitly excluded. Use when only positional or embedding information is needed.

```r
df5 <- fetch_cell_data(pbmc3k, reductions = "umap", metadata = FALSE)
```

--------------------------------

### fetch_cell_data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts cell metadata and embedding coordinates into a tidy tibble for visualization.

```APIDOC
## fetch_cell_data

### Description
Extracts cell metadata and embedding coordinates into a tidy tibble for visualization.

### Parameters
- **object** (Seurat) - Required - The Seurat object to extract data from.
- **reductions** (string) - Optional - The name of the reduction to include (e.g., "pca", "umap").
- **dims** (numeric) - Optional - Dimensions to include from the reduction.
- **metadata** (vector/logical) - Optional - Metadata columns to include or FALSE to exclude all.

### Request Example
fetch_cell_data(pbmc3k, reductions = "pca", dims = 1:10, metadata = c("seurat_clusters", "condition"))
```

--------------------------------

### Fetch Feature Data with Embeddings and Metadata

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts feature expression data along with UMAP embeddings and specified metadata. Useful for visualizing gene expression in the context of cell clusters and embeddings.

```r
# With embeddings and specific metadata
df3 <- fetch_feature_data(
  pbmc3k,
  features = c("CD3D", "CD14"),
  reductions = "umap",
  metadata = c("seurat_clusters", "condition")
)
```

--------------------------------

### Data Extraction Functions

Source: https://github.com/wolf5996/badranseq/blob/main/NEWS.md

Functions for extracting data from Seurat objects into tidy tibble formats.

```APIDOC
## fetch_cell_data

### Description
Extracts cell-level metadata and embeddings from Seurat objects as a tidy tibble.

## fetch_feature_data

### Description
Extracts feature expression data as a tidy tibble with one row per cell per feature.

### Parameters
- **assay** (string) - Optional - Explicit assay selection.
- **layer** (string) - Optional - Explicit layer selection.
```

--------------------------------

### Generate Enhanced Elbow Plots with EnhancedElbowPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

Provides an improved version of the Seurat ElbowPlot with additional aesthetic and variance-related customization options.

```r
library(BadranSeq)
data(pbmc3k)

# Basic elbow plot with variance explained
p1 <- EnhancedElbowPlot(pbmc3k)

# Mark cutoff at specific PC
p2 <- EnhancedElbowPlot(pbmc3k, cutoff_pc = 10)

# Plot standard deviation instead of variance
p3 <- EnhancedElbowPlot(pbmc3k, variance = FALSE)

# Full customization
p4 <- EnhancedElbowPlot(
  pbmc3k,
  ndims = 30,
  cutoff_pc = 15,
  variance = TRUE,
  title = "PCA Variance Explained",
  point_size = 4,
  point_color = "darkblue",
  line_color = "darkblue",
  cutoff_color = "red",
  base_size = 16
)
```

--------------------------------

### Customize theme_badranseq to Hide Axes

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies the BadranSeq theme while hiding the plot axes. Ideal for visualizations like cell embeddings where axes are not informative.

```r
# Hide axes (useful for embeddings)
p3 <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  theme_badranseq(plot.axes = FALSE)
```

--------------------------------

### EnhancedElbowPlot

Source: https://context7.com/wolf5996/badranseq/llms.txt

An improved version of Seurat's ElbowPlot with enhanced aesthetics and customizable options.

```APIDOC
## EnhancedElbowPlot

### Description
An improved version of Seurat's ElbowPlot with enhanced aesthetics, customizable titles, optional cutoff lines, and variance explained options.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **ndims** (numeric) - Optional - Number of dimensions to plot.
- **cutoff_pc** (numeric) - Optional - PC number to draw a cutoff line.
- **variance** (boolean) - Optional - Whether to plot variance explained instead of standard deviation.
- **title** (string) - Optional - Plot title.
- **point_size** (numeric) - Optional - Size of points.
- **point_color** (string) - Optional - Color of points.
- **line_color** (string) - Optional - Color of lines.
- **cutoff_color** (string) - Optional - Color of the cutoff line.
- **base_size** (numeric) - Optional - Base font size.
```

--------------------------------

### Generate Named BadranSeq Color Vector

Source: https://context7.com/wolf5996/badranseq/llms.txt

Generates a BadranSeq color palette and assigns names to the colors, typically corresponding to cluster IDs. Essential for mapping specific colors to specific groups.

```r
# Named color vector for specific groups
n_clusters <- 8
cluster_colors <- generate_badranseq_colors(n_clusters)
names(cluster_colors) <- as.character(0:(n_clusters - 1))
```

--------------------------------

### fetch_cell_data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts a tidy tibble with one row per cell containing cell identifiers, embeddings, and metadata.

```APIDOC
## fetch_cell_data

### Description
Extracts a tidy tibble with one row per cell containing cell identifiers, optional dimensionality reduction embeddings, and optional metadata.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **reductions** (vector) - Optional - Dimensionality reduction names to include.
```

--------------------------------

### Extract PCA Variance with get_pca_variance

Source: https://context7.com/wolf5996/badranseq/llms.txt

Retrieves PCA variance metrics from a Seurat object as a data frame for programmatic analysis.

```r
library(BadranSeq)
data(pbmc3k)

# Get all PCs
variance_df <- get_pca_variance(pbmc3k)
# Returns: PC, variance_explained, cumulative_variance

# Get top 10 PCs
top10 <- get_pca_variance(pbmc3k, n_pcs = 10)

# Find PC where cumulative variance reaches 80%
variance_df <- get_pca_variance(pbmc3k)
pc_80 <- min(which(variance_df$cumulative_variance >= 80))
print(paste("80% variance at PC:", pc_80))
```

--------------------------------

### Use BadranSeq Colors with ggplot2

Source: https://context7.com/wolf5996/badranseq/llms.txt

Applies a BadranSeq-generated color palette to a ggplot2 plot using scale_color_manual. Ensures consistent and publication-ready colors.

```r
# Use with ggplot2
ggplot(mtcars, aes(mpg, wt, color = factor(cyl))) +
  geom_point(size = 3) +
  scale_color_manual(values = generate_badranseq_colors(3)) +
  theme_badranseq()
```

--------------------------------

### fetch_feature_data

Source: https://context7.com/wolf5996/badranseq/llms.txt

Extracts a tidy tibble with one row per cell per feature in long format, including expression values and optional metadata.

```APIDOC
## fetch_feature_data

### Description
Extracts a tidy tibble with one row per cell per feature in long format. Expression values are provided as one column per requested layer.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **features** (vector) - Required - Features (genes) to extract.
- **layer** (vector) - Optional - Layers to extract (e.g., "counts", "data").
- **reductions** (string) - Optional - Reduction to include.
- **metadata** (vector) - Optional - Metadata columns to include.
- **assay** (string) - Optional - Assay to use.
```

--------------------------------

### get_pca_variance

Source: https://context7.com/wolf5996/badranseq/llms.txt

Helper function to extract variance explained information from a Seurat object as a data.frame.

```APIDOC
## get_pca_variance

### Description
Extracts variance explained information from a Seurat object. Returns PC number, variance explained percentage, and cumulative variance.

### Parameters
- **object** (Seurat) - Required - The Seurat object.
- **n_pcs** (numeric) - Optional - Number of top PCs to return.
```

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