### Install SCpubr from GitHub (Development Version)

Source: https://enblacar.github.io/SCpubr-book/00_getting_started/01_installation.html

Install the latest stable development version of SCpubr directly from GitHub. Ensure the 'devtools' package is installed first.

```r
# If not installed.
if(!requireNamespace("devtools", quietly = TRUE)){
  install.packages("devtools")
}

devtools::install_github("enblacar/SCpubr", ref = "v3.0.0")
```

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

### Check SCpubr Dependencies and Function Availability

Source: https://enblacar.github.io/SCpubr-book/00_getting_started/01_installation.html

Run this function to get a detailed report on your installed dependencies, their status, and which SCpubr functions are ready to use. It also flags outdated packages.

```r
SCpubr::do_PackageReport(extended = TRUE)
```

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

### Compute TF activities with decoupleR

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Before generating the heatmap, compute TF activities using decoupleR. This example shows how to get the DoRothEA prior knowledge for humans and run the weighted mean activity calculation.

```r
# Run decoupleR with DoRothEA prior knowledge
net <- decoupleR::get_dorothea(organism = "human", levels = c("A", "B", "C"))
activities <- decoupleR::run_wmean(mat = as.matrix(sample["RNA"]$data), 
                                   net = net)
```

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

### Install SCpubr from CRAN

Source: https://enblacar.github.io/SCpubr-book/00_getting_started/01_installation.html

Use this command to install the latest official release of SCpubr from the Comprehensive R Archive Network (CRAN).

```r
install.packages("SCpubr")
```

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

### Select Specific PCs for Loadings Heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This example demonstrates how to select specific principal components (PCs) to display in the loadings heatmap using the 'dims' argument.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample,
                                dims = 1:2)
p
```

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

### Basic Ligand-Receptor Plot

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/09_ligandreceptorplot.html

Generates a basic dot plot of ligand-receptor interactions after running liana. Ensure liana is installed and the output is aggregated.

```r
liana_output <- liana::liana_wrap(sample) %>%
                liana::liana_aggregate()

p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output)
p
```

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

### Group Loadings Heatmap by Cell Type

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This example demonstrates grouping the loadings heatmap by a specific metadata column, such as 'subtype', using the 'group.by' argument.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample,
                                group.by = "subtype")
p
```

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

### Get SCpubr Package Version and Citation in R

Source: https://enblacar.github.io/SCpubr-book/07_appendix/01_citation.html

Use these R commands to retrieve the package version and citation information programmatically.

```r
citation("SCpubr")
packageVersion("SCpubr")
```

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

### Basic Package Report

Source: https://enblacar.github.io/SCpubr-book/06_accessories/05_packagereport.html

Generates a basic status report for SCpubr and its dependencies. Use this for a quick overview of installed packages and available functions.

```r
SCpubr::do_PackageReport()
```

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

### Adjust Color Scales for Loadings and Expression

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This example demonstrates how to adjust the color scales for both the loadings scores and gene expression levels using cutoff parameters.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample,
                                min.cutoff.loadings = -0.1,
                                max.cutoff.loadings = 0.1,
                                min.cutoff.expression = 0,
                                max.cutoff.expression = 3)
p
```

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

### do_ColorPalette() - Color Palette Generation

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

This utility function generates harmonious color palettes based on color theory principles. Provide a base color and get complementary, triadic, tetradic, or other color schemes.

```APIDOC
## do_ColorPalette()

### Description
Generates harmonious color palettes based on color theory principles. This function can produce various color schemes like complementary, triadic, tetradic, square, and split complementary, or evenly spaced colors around a color wheel.

### Method
`SCpubr::do_ColorPalette()`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Examples

#### Color Wheel
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", n = 8)
print(colors)
```

#### Opposite (Complementary)
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", opposite = TRUE)
print(colors)
```

#### Adjacent Colors
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", adjacent = TRUE)
print(colors)
```

#### Triadic Scheme
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", triadic = TRUE)
print(colors)
```

#### Tetradic Scheme
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", tetradic = TRUE)
print(colors)
```

#### Square Scheme
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", square = TRUE)
print(colors)
```

#### Split Complementary
```R
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946", split_complementary = TRUE)
print(colors)
```

#### Generate Plot
```R
p <- SCpubr::do_ColorPalette(colors.use = "#E63946", triadic = TRUE, plot = TRUE)
print(p)
```

### Parameter Reference
* `colors.use` (character or HEX) - Base color (name or HEX). Default is not specified, but examples use HEX.
* `n` (integer) - Number of colors to generate for the color wheel. Default is `12`.
* `opposite` (boolean) - If TRUE, generates the complementary color. Default is `FALSE`.
* `adjacent` (boolean) - If TRUE, generates adjacent colors. Default is `FALSE`.
* `triadic` (boolean) - If TRUE, generates a triadic color scheme. Default is `FALSE`.
* `split_complementary` (boolean) - If TRUE, generates a split complementary color scheme. Default is `FALSE`.
* `tetradic` (boolean) - If TRUE, generates a tetradic color scheme. Default is `FALSE`.
* `square` (boolean) - If TRUE, generates a square color scheme. Default is `FALSE`.
* `plot` (boolean) - If TRUE, returns a plot object. Default is `FALSE`.

### See Also
* `do_ColorBlindCheck()` — Test accessibility
* `Shared features` — Color scales
```

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

### Clamp Expression Values in DotPlot

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Set the minimum and maximum expression values to display using `min.cutoff` and `max.cutoff`. This example clamps expression values between 0 and 3.

```R
p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes,
                        min.cutoff = 0,
                        max.cutoff = 3)
p__
```

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

### Adjust Dot Size in DotPlot

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Control the maximum size of the dots using the `dot.scale` parameter. This example sets the maximum dot size to 12.

```R
p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes,
                        dot.scale = 12)
p__
```

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

### Chord Diagram Plot with Custom Arrow Type

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Sets a custom arrow type for the links in a chord diagram, using 'triangle' as an example.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype",
                                 link.arr.type = "triangle")
p__
```

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

### Load necessary packages and perform enrichment analysis

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/06_termenrichmentplot.html

This snippet demonstrates how to load required packages and perform GO enrichment analysis using `clusterProfiler`. The results are then passed to `do_TermEnrichmentPlot` for visualization.

```R
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect,
    is.element, setdiff, setequal, union


Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    anyDuplicated, aperm, append, as.data.frame,
    basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, is.unsorted, lapply, Map, mapply, match,
    mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, Position, rank, rbind, Reduce,
    rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view
    with 'browseVignettes()'. To cite Bioconductor, 
    see 'citation("Biobase")', and for packages 
    'citation("pkgname")'.

Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

The following objects are masked from 'package:base':

    expand.grid, I, unname


Attaching package: 'IRanges'

The following object is masked from 'package:sp':

    %over%


# Using clusterProfiler

enrichment_result <- clusterProfiler::enrichGO(
  gene = Seurat::VariableFeatures(sample)[1:100],
  OrgDb = org.Hs.eg.db,
  keyType = "SYMBOL",
  ont = "BP"
)

p <- SCpubr::do_TermEnrichmentPlot(mat = enrichment_result@result)
p
```

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

### Basic Usage of do_LoadingsHeatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This snippet shows the basic usage of the do_LoadingsHeatmap function with a sample object. It generates a heatmap of PCA loadings.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample)
p
```

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

### Generate basic TF activity heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Generate a basic TF activity heatmap using the computed activities. This requires the SCpubr package and the sample object.

```r
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
                                  activities = activities)
p
```

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

### Display R Session Information

Source: https://enblacar.github.io/SCpubr-book/07_appendix/04_session_info.html

Use the `sessionInfo()` function to capture the R version, platform, locale, time zone, attached base packages, other attached packages, and loaded namespaces. This is crucial for ensuring reproducibility of analyses and book builds.

```R
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.1
#> 
#> Matrix products: default
#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Berlin
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets 
#> [6] methods   base     
#> 
#> other attached packages:
#> [1] patchwork_1.3.2    ggplot2_4.0.1      SCpubr_3.0.1      
#> [4] Seurat_5.4.0       SeuratObject_5.3.0 sp_2.2-0          
#> 
#> loaded via a namespace (and not attached):
#>   [1] deldir_2.0-4           pbapply_1.7-4         
#>   [3] gridExtra_2.3          rlang_1.1.6           
#>   [5] magrittr_2.0.4         RcppAnnoy_0.0.22      
#>   [7] otel_0.2.0             spatstat.geom_3.6-1   
#>   [9] matrixStats_1.5.0      ggridges_0.5.7        
#>  [11] compiler_4.5.2         png_0.1-8             
#>  [13] vctrs_0.6.5            reshape2_1.4.5        
#>  [15] stringr_1.6.0          pkgconfig_2.0.3       
#>  [17] fastmap_1.2.0          promises_1.5.0        
#>  [19] rmarkdown_2.30         purrr_1.2.0           
#>  [21] xfun_0.55              jsonlite_2.0.0        
#>  [23] goftest_1.2-3          later_1.4.4           
#>  [25] spatstat.utils_3.2-0   irlba_2.3.5.1         
#>  [27] parallel_4.5.2         cluster_2.1.8.1       
#>  [29] R6_2.6.1               ica_1.0-3             
#>  [31] stringi_1.8.7          RColorBrewer_1.1-3    
#>  [33] spatstat.data_3.1-9    reticulate_1.44.1     
#>  [35] parallelly_1.46.0      spatstat.univar_3.1-5 
#>  [37] lmtest_0.9-40          scattermore_1.2       
#>  [39] Rcpp_1.1.0             knitr_1.51            
#>  [41] tensor_1.5.1           future.apply_1.20.1   
#>  [43] zoo_1.8-15             sctransform_0.4.2     
#>  [45] httpuv_1.6.16          Matrix_1.7-4          
#>  [47] splines_4.5.2          igraph_2.2.1          
#>  [49] tidyselect_1.2.1       rstudioapi_0.17.1     
#>  [51] abind_1.4-8            yaml_2.3.12           
#>  [53] spatstat.random_3.4-3  codetools_0.2-20      
#>  [55] miniUI_0.1.2           spatstat.explore_3.6-0
#>  [57] listenv_0.10.0         lattice_0.22-7        
#>  [59] tibble_3.3.0           plyr_1.8.9            
#>  [61] withr_3.0.2            shiny_1.12.1          
#>  [63] S7_0.2.1               ROCR_1.0-11           
#>  [65] evaluate_1.0.5         Rtsne_0.17            
#>  [67] future_1.68.0          fastDummies_1.7.5     
#>  [69] survival_3.8-3         polyclip_1.10-7       
#>  [71] fitdistrplus_1.2-4     pillar_1.11.1         
#>  [73] KernSmooth_2.23-26     plotly_4.11.0         
#>  [75] generics_0.1.4         RcppHNSW_0.6.0        
#>  [77] scales_1.4.0           globals_0.18.0        
#>  [79] xtable_1.8-4           glue_1.8.0            
#>  [81] lazyeval_0.2.2         tools_4.5.2           
#>  [83] data.table_1.18.0      RSpectra_0.16-2       
#>  [85] RANN_2.6.2             dotCall64_1.2         
#>  [87] cowplot_1.2.0          grid_4.5.2            
#>  [89] tidyr_1.3.2            nlme_3.1-168          
#>  [91] cli_3.6.5              spatstat.sparse_3.1-0 
#>  [93] spam_2.11-1            viridisLite_0.4.2     
#>  [95] dplyr_1.1.4            uwot_0.2.4            
#>  [97] gtable_0.3.6           digest_0.6.39         
#>  [99] progressr_0.18.0       ggrepel_0.9.6         
#> [101] htmlwidgets_1.6.4      farver_2.1.2          
#> [103] htmltools_0.5.9        lifecycle_1.0.4       
#> [105] httr_1.4.7             mime_0.13             
#> [107] MASS_7.3-65__
```

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

### Control X-axis Text Angle in DotPlot

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Adjust the rotation angle of the x-axis labels using the `axis.text.x.angle` parameter. This example rotates the labels by 90 degrees.

```R
p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes,
                        axis.text.x.angle = 90)
p__
```

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

### Metadata Heatmap with Sample Clustering

Source: https://enblacar.github.io/SCpubr-book/06_accessories/03_metadataheatmap.html

Creates a metadata heatmap with hierarchical clustering applied to samples. This helps in identifying patterns and relationships between samples based on their metadata.

```r
p <- SCpubr::do_MetadataHeatmap(sample = sample,
                                group.by = "ID",
                                metadata = c("technology", "gender", "subtype"),
                                legend.ncol = 1,
                                flip = FALSE,
                                cluster = TRUE)
p__
```

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

### Filter Low Expression in DotPlot

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Use the `dot.min` parameter to remove dots where a gene is expressed in less than the specified percentage of cells. This example shows dots only where >= 50% of cells express the gene.

```R
# Only show dots where >= 50% of cells express the gene
p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes,
                        dot.min = 50)
p__
```

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

### Sequential Color Palettes

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Applies sequential color palettes from RColorBrewer. Use 'use_viridis = FALSE' and specify the palette name with 'sequential.palette' and direction with 'sequential.direction'.

```r
# RColorBrewer sequential palette
p1 <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            use_viridis = FALSE,
                            sequential.palette = "YlOrRd",
                            sequential.direction = 1)

p1
```

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

### Generate Chord Diagrams from Ligand-Receptor Data

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/09_ligandreceptorplot.html

Generates both a dot plot and chord diagrams to visualize cell-cell communication. Chord diagrams provide a circular flow visualization of interactions.

```r
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   compute_ChordDiagrams = TRUE)
p$DotPlot
p$ChordDiagram
```

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

### Basic Metadata Heatmap

Source: https://enblacar.github.io/SCpubr-book/06_accessories/03_metadataheatmap.html

Generates a basic heatmap of specified metadata columns across samples, grouped by a specified ID. Useful for an initial overview of sample annotations.

```r
p <- SCpubr::do_MetadataHeatmap(sample = sample,
                                group.by = "ID",
                                metadata = c("technology", "gender", "subtype"),
                                flip = FALSE,
                                legend.ncol = 1)
p__
```

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

### Split TF activity heatmap by a condition

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Create separate heatmaps for different conditions by specifying a metadata variable in the 'split.by' parameter. This is useful for analyzing TF activity differences between experimental groups.

```r
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
                                  activities = activities,
                                  split.by = "condition")
p
```

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

### Colorblind Check with Flipped Orientation

Source: https://enblacar.github.io/SCpubr-book/06_accessories/02_colorblindcheck.html

This snippet demonstrates how to flip the orientation of the colorblind check output by setting the `flip` argument to TRUE. This can be useful for different display preferences.

```r
p <- SCpubr::do_ColorBlindCheck(colors.use = colors,
                                flip = TRUE)
p__
```

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

### Basic Dot Plot for Marker Genes

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Generates a basic dot plot to visualize the expression of specified marker genes across cell clusters. Requires the 'sample' object and a vector of gene names.

```r
p <- SCpubr::do_DotPlot(sample = sample,
                        features = c("CDC25C", "KIF18B", "KIF14", "CENPE"))
p__
```

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

### Subsample Cells for Loadings Heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This snippet shows how to subsample a specified number of cells for generating the loadings heatmap using the 'subsample' argument, which can be useful for large datasets.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample,
                                subsample = 1000)
p
```

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

### Basic Chord Diagram Plot

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Generates a basic chord diagram to visualize transitions between two categorical variables. Requires the 'sample' object and specifies the 'from' and 'to' categorical columns.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype")
p__
```

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

### do_ColorBlindCheck()

Source: https://enblacar.github.io/SCpubr-book/06_accessories/02_colorblindcheck.html

Simulates colorblind vision to verify your color palette is accessible. Shows how your colors appear under different types of color vision deficiency.

```APIDOC
## do_ColorBlindCheck()

### Description
Simulates colorblind vision to verify your color palette is accessible. Shows how your colors appear under different types of color vision deficiency.

### Method
N/A (R function)

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```R
colors <- c("#E63946", "#F1FAEE", "#A8DADC", "#457B9D", "#1D3557")
p <- SCpubr::do_ColorBlindCheck(colors.use = colors)
p
```

### Response
#### Success Response
N/A (R function returns a plot object)

#### Response Example
N/A

### Simulated conditions
- **Normal** — Typical color vision
- **Protanopia** — Red-green (less common)
- **Deuteranopia** — Red-green (most common)
- **Tritanopia** — Blue-yellow

### Parameter reference
- `colors.use` (character vector) - Colors to check - 
- `flip` (logical) - Flip orientation - `FALSE`
```

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

### Basic Colorblind Check

Source: https://enblacar.github.io/SCpubr-book/06_accessories/02_colorblindcheck.html

Use this snippet to perform a basic check of your color palette. Provide a vector of colors to the `colors.use` argument.

```r
colors <- c("#E63946", "#F1FAEE", "#A8DADC", "#457B9D", "#1D3557")

p <- SCpubr::do_ColorBlindCheck(colors.use = colors)
p__
```

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

### Custom Colors for Multiple Metadata Variables in Heatmaps

Source: https://enblacar.github.io/SCpubr-book/00_getting_started/03_shared_features.html

Use a named list to define custom colors for multiple metadata variables, suitable for heatmaps like SCpubr::do_SCExpressionHeatmap.

```r
# Multiple metadata variables (for heatmaps)
colors <- list(
  "seurat_clusters" = c("0" = "#001219", "1" = "#005f73", "2" = "#0a9396"),
  "cell_type"       = c("A" = "#ca6702", "B" = "#0a9396")
)

SCpubr::do_SCExpressionHeatmap(sample, features = genes, 
                                metadata = names(colors),
                                metadata.colors = colors)
```

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

### Basic CNV Heatmap Generation

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/08_cnvheatmap.html

Generates a basic CNV heatmap using inferCNV output. Requires chromosome location data. Ensure inferCNV object and sample data are loaded.

```r
# Load chromosome locations
data("human_chr_locations", package = "SCpubr")

p <- SCpubr::do_CNVHeatmap(sample = sample,
                           infercnv_object = infercnv_obj,
                           chromosome_locations = human_chr_locations)
p
```

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

### Generate Split Complementary Color Scheme

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates a split complementary color scheme from a base color. This scheme uses the base color and the two colors adjacent to its complement, offering strong visual contrast with less tension than a direct complementary pair.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  split_complementary = TRUE)
colors
#> [1] "#E63946" "#39E683" "#399DE6"
```

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

### Customizing Color Scale Limits

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Controls the color scale limits by clamping outlier values using 'min.cutoff' and 'max.cutoff'. This helps in compressing the color range for better visualization.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            min.cutoff = -10,
                            max.cutoff = 10)
p
```

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

### Metadata Heatmap with Adjusted Row Gaps

Source: https://enblacar.github.io/SCpubr-book/06_accessories/03_metadataheatmap.html

Generates a metadata heatmap with custom spacing between the metadata rows. Adjusting `heatmap.gap` can improve readability for complex heatmaps.

```r
p <- SCpubr::do_MetadataHeatmap(sample = sample,
                                group.by = "ID",
                                metadata = c("technology", "gender", "subtype"),
                                flip = FALSE,
                                legend.ncol = 1,
                                heatmap.gap = 5)
p__
```

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

### Generate and Plot Triadic Color Palette

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates a triadic color scheme and returns it as a plot. This is useful for visualizing the generated palette directly.

```r
p <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                             triadic = TRUE,
                             plot = TRUE)
p
```

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

### Fixed Scale Limits

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Sets fixed color scale limits using 'scale.limits', which is useful for comparing scales across different samples. Cannot be used with 'min.cutoff' or 'max.cutoff'.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            scale.limits = c(-50, 50))
p
```

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

### Split Feature Plot by Variable

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Generates faceted FeaturePlots split by a specified metadata variable. Use the 'split.by' parameter with the column name. Assumes 'sample' is defined.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            split.by = "subtype")
```

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

### Chord Diagram Plot with Direction Visualization (Height Difference and Arrows)

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Configures the direction visualization in a chord diagram to use height difference and arrows, which is the default setting.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype",
                                 direction.type = c("diffHeight", "arrows"))

p__
```

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

### Generate Complementary Colors

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates the complementary color to a given base color. This is useful for creating high-contrast color pairs.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  opposite = TRUE)
colors
#> [1] "#E63946" "#39E6D9"
```

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

### Chord Diagram Plot with Directional Links (To -> From)

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Visualizes directional links from the 'to' category to the 'from' category in a chord diagram by setting directional to -1.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype",
                                 directional = -1)
p__
```

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

### Chord Diagram Plot with Directional Links (From -> To)

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Visualizes directional links from the 'from' category to the 'to' category in a chord diagram. This is the default behavior.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype",
                                 directional = 1)

p__
```

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

### Basic BeeSwarm Plot

Source: https://enblacar.github.io/SCpubr-book/04_distributions/05_beeswarmplot.html

Generates a basic beeswarm plot by ranking cells based on the 'nCount_RNA' feature. This is useful for visualizing the distribution of a specific metric across individual cells.

```r
p <- SCpubr::do_BeeSwarmPlot(sample = sample,
                             feature_to_rank = "nCount_RNA")
p__
```

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

### Metadata Heatmap from Data Frame

Source: https://enblacar.github.io/SCpubr-book/06_accessories/03_metadataheatmap.html

Generates a metadata heatmap directly from a data frame. This is useful when metadata is already organized in a tabular format.

```r
df <- data.frame(
  Patient1 = c("Healthy", "Male"),
  Patient2 = c("Disease", "Female"),
  row.names = c("Condition", "Sex")
)

p <- SCpubr::do_MetadataHeatmap(from_df = TRUE,
                                df = df)
p__
```

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

### SCpubr BibTeX Entry

Source: https://enblacar.github.io/SCpubr-book/07_appendix/01_citation.html

The BibTeX entry for citing SCpubr in LaTeX documents.

```bibtex
@article{blanco2022scpubr,
  title={Generating publication ready visualizations for Single Cell transcriptomics using SCpubr},
  author={Blanco-Carmona, Enrique},
  journal={bioRxiv},
  year={2022},
  doi={10.1101/2022.02.28.482303}
}
```

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

### Dot Plot with Multiple Marker Genes

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Visualizes expression for an extended list of marker genes. Ensure the 'sample' object and the 'genes' vector are correctly defined.

```r
genes <- c("CDC25C", "KIF18B", "KIF14", "CENPE",
           "DNAAF1", "ADGB", "CFAP61", "CFAP157",
           "KCNQ5", "MEOX2", "MBP", "SNTG1",
           "S100A1", "MGP", "TNNT1", "H2AFJ")

p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes)
p__
```

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

### Select specific TFs for heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Display only a specific set of transcription factors by providing a character vector to the 'tfs.use' parameter. This allows for targeted visualization of known TFs.

```r
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
                                  activities = activities,
                                  tfs.use = c("PAX5", "STAT3", "NF-kB"))
p
```

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

### Group Overlay Feature Plot with Dots

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Displays categorical group information by adding dots at cluster centers. Use 'group.by' to specify the metadata column. Assumes 'sample' is defined.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            group.by = "Annotation")
```

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

### Basic Strip Plot

Source: https://enblacar.github.io/SCpubr-book/04_distributions/04_stripplot.html

Generates a basic strip plot showing the distribution of a specified feature (e.g., nCount_RNA) across samples. This is the default usage.

```r
p <- SCpubr::do_StripPlot(sample = sample,
                          features = "nCount_RNA")
p__
```

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

### Display numeric values on TF activity heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Show the actual numeric TF activity scores on the heatmap cells by setting 'values.show' to TRUE. The size of the text can be adjusted with 'values.size'.

```r
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
                                  activities = activities,
                                  values.show = TRUE,
                                  values.size = 4)
p
```

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

### Group TF activity heatmap by a variable

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/03_tfactivityheatmap.html

Group the cells in the heatmap based on a specific metadata variable by using the 'group.by' parameter. This helps in comparing TF activity across different cell populations.

```r
p <- SCpubr::do_TFActivityHeatmap(sample = sample,
                                  activities = activities,
                                  group.by = "cell_type")
p
```

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

### Generate Square Color Scheme

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates a square color scheme from a base color. This scheme uses four colors evenly spaced around the color wheel, creating a balanced and harmonious palette.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  square = TRUE)
colors
#> [1] "#E63946" "#9DE639" "#39E6D9" "#8239E6"
```

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

### Control term text wrapping

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/06_termenrichmentplot.html

This snippet demonstrates how to adjust the line wrapping for term labels using the `n.chars` argument, allowing for shorter lines.

```R
p <- SCpubr::do_TermEnrichmentPlot(mat = enrichment_result@result,
                                   n.chars = 30)  # Shorter lines
p
```

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

### Basic Groupwise DE Heatmap

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/05_groupwisedeheatmap.html

Generates a basic groupwise differential expression heatmap using Seurat's FindAllMarkers output. Ensure FindAllMarkers has been run prior to this function.

```r
de_genes <- Seurat::FindAllMarkers(sample, only.pos = TRUE)

p <- SCpubr::do_GroupwiseDEHeatmap(sample = sample,
                                   de_genes = de_genes)
p
```

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

### Chord Diagram Plot with Equally Scaled Nodes

Source: https://enblacar.github.io/SCpubr-book/03_proportions/04_chorddiagramplot.html

Ensures all groups in the chord diagram have the same width, regardless of their cell count, by setting scale to TRUE.

```R
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
                                 from = "Annotation",
                                 to = "subtype",
                                 scale = TRUE)

p__
```

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

### Generate 8 Colors Evenly Spaced Around Color Wheel

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates n colors evenly spaced around the color wheel using a base color. This is useful for creating a diverse set of related colors for visualizations.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  n = 8)
colors
#> [1] "#E63946" "#E6AE39" "#9DE639" "#39E657" "#39E6D9"
#> [6] "#3971E6" "#8239E6" "#E639C8"
```

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

### Three-Variable Cellular States Plot (Butterfly Plot)

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/03_cellularstatesplot.html

Creates a butterfly plot by adding a second X-axis variable, allowing for comparison of three gene signatures. The X-axis displays enrichment for x1 on the right and x2 on the left, while the Y-axis shows enrichment for y1. Requires defining three gene signatures and specifying x1, x2, and y1 parameters.

```R
gene_lists <- list(
  "IPC.like"      = c("CDC25C", "KIF18B", "KIF14", "CENPE"),
  "Cilia.like" = c("DNAAF1", "ADGB", "CFAP61", "CFAP157"),
  "OPC.like" = c("KCNQ5", "MEOX2", "DNER3", "SNTG1")
)

p <- SCpubr::do_CellularStatesPlot(
  sample          = sample,
  input_gene_list = gene_lists,
  x1              = "Cilia.like",
  x2              = "OPC.like",
  y1              = "IPC.like"
)
p
```

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

### Group Overlay Feature Plot with Cell Borders

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Adds a colored shadow around cells to indicate group membership. Set 'group.by.cell_borders' to TRUE and optionally adjust transparency with 'group.by.cell_borders.alpha'. Requires a pre-defined 'sample' object.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            group.by = "Annotation",
                            group.by.cell_borders = TRUE,
                            group.by.cell_borders.alpha = 0.1)
```

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

### Order Cells by Expression in Feature Plot

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Plots cells with higher expression values on top of the FeaturePlot. Use the 'order = TRUE' parameter. Assumes 'sample' is defined.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "KIF14",
                            order = TRUE)
```

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

### Highlight Specific Cells by Identity

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Highlights cells belonging to specified identities. Use the 'idents.highlight' parameter with a vector of identity names. Requires a pre-defined 'sample' object.

```r
# Highlight by identity
p2 <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            idents.highlight = c("IPC-like"))
```

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

### Basic Volcano Plot Generation

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/07_volcanoplot.html

Generates a basic volcano plot using differential expression results. Ensure differential expression analysis has been performed prior to using this function.

```r
de_genes <- Seurat::FindMarkers(sample, ident.1 = "IPC-like", ident.2 = "Cilia-like")

p <- SCpubr::do_VolcanoPlot(sample = sample,
                            de_genes = de_genes)
p
```

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

### Generate Adjacent Colors

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates adjacent colors to a given base color on the color wheel. This is useful for creating analogous color schemes.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  adjacent = TRUE)
colors
#> [1] "#E63946" "#E68239" "#E6399C"
```

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

### BeeSwarm Plot with Ordered Groups

Source: https://enblacar.github.io/SCpubr-book/04_distributions/05_beeswarmplot.html

Creates a beeswarm plot where the groups are ordered by their median value for the 'nCount_RNA' feature. This facilitates easier comparison of distributions across groups.

```r
p <- SCpubr::do_BeeSwarmPlot(sample = sample,
                             feature_to_rank = "nCount_RNA",
                             order = TRUE)
p__
```

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

### BeeSwarm Plot with Continuous Coloring

Source: https://enblacar.github.io/SCpubr-book/04_distributions/05_beeswarmplot.html

Creates a beeswarm plot where cells are ranked by 'nCount_RNA' and colored based on the same continuous feature. This helps in visualizing the distribution and variation of a continuous metric across cells.

```r
p <- SCpubr::do_BeeSwarmPlot(sample = sample,
                             feature_to_rank = "nCount_RNA",
                             continuous_feature = TRUE)
p__
```

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

### Adjust dot size for visualization

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/06_termenrichmentplot.html

This snippet illustrates how to modify the scale of the dots representing enriched terms using the `dot.scale` argument.

```R
p <- SCpubr::do_TermEnrichmentPlot(mat = enrichment_result@result,
                                   dot.scale = 6)
p
```

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

### Sorting Options for Ligand-Receptor Plot

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/09_ligandreceptorplot.html

Applies different sorting methods to the ligand-receptor interactions displayed in the plot, including sorting by specificity, magnitude, or a combination thereof.

```r
# Sort by specificity
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort.by = "A")

# Sort by magnitude
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort.by = "B")

# Sort by specificity then magnitude
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort.by = "C")

# Sort by magnitude then specificity
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort.by = "D")

# Equal weight (default)
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   sort.by = "E")
p
```

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

### Highlight Specific Cells by Barcode

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Highlights a subset of cells based on their barcodes. First, select a number of cells to use, then call do_FeaturePlot with the cells.use parameter. Assumes 'sample' is defined.

```r
# Highlight by cell barcodes
cells.use <- sample(colnames(sample), 500)
p1 <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            cells.highlight = cells.use)
```

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

### Dot Plot with Grouped Features

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Organizes and visualizes genes into distinct categories using a named list. Each category will be represented in a separate faceted panel.

```r
genes <- list(
  "IPC.like"      = c("CDC25C", "KIF18B", "KIF14", "CENPE"),
  "Cilia.like" = c("DNAAF1", "ADGB", "CFAP61", "CFAP157"),
  "OPC.like" = c("KCNQ5", "MEOX2", "MBP", "SNTG1"),
  "Mesenchymal.like" = c("S100A1", "MGP", "TNNT1", "H2AFJ")
)

p <- SCpubr::do_DotPlot(sample = sample,
                        features = genes)
p__
```

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

### Feature Plot with Density Contours

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/01_featureplot.html

Overlays density contours on the FeaturePlot. Set 'plot_density_contour' to TRUE and customize contour appearance with 'contour.color' and 'contour.position'. Requires a pre-defined 'sample' object.

```r
p <- SCpubr::do_FeaturePlot(sample = sample,
                            features = "PC_1",
                            plot_density_contour = TRUE,
                            contour.color = "grey50",
                            contour.position = "bottom")
```

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

### Adjust Number of Top Loadings

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/10_loadingsheatmap.html

This snippet shows how to adjust the number of top and bottom contributing genes displayed for each principal component using the 'top_loadings' argument.

```r
p <- SCpubr::do_LoadingsHeatmap(sample = sample,
                                top_loadings = 2)
p
```

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

### CNV Heatmap with Numeric Values Displayed

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/08_cnvheatmap.html

Generates a CNV heatmap with the numeric CNV scores displayed directly on the heatmap cells. Useful for precise value inspection. Requires chromosome location data.

```r
p <- SCpubr::do_CNVHeatmap(sample = sample,
                           infercnv_object = infercnv_obj,
                           chromosome_locations = human_chr_locations,
                           values.show = TRUE)
p
```

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

### Color Cellular States Plot by Metadata

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/03_cellularstatesplot.html

Color cells in the plot based on a specified metadata variable using the `group.by` parameter. This helps in visualizing the distribution of different cell populations or states.

```r
gene_lists <- list(
  "IPC.like"      = c("CDC25C", "KIF18B", "KIF14", "CENPE"),
  "Cilia.like" = c("DNAAF1", "ADGB", "CFAP61", "CFAP157"),
  "OPC.like" = c("KCNQ5", "MEOX2", "DNER3", "SNTG1"),
  "Mesenchymal.like" = c("S100A1", "MGP", "TNNT1", "H2AFJ")
)

p <- SCpubr::do_CellularStatesPlot(
  sample          = sample,
  input_gene_list = gene_lists,
  x1              = "IPC.like",
  x2              = "Mesenchymal.like",
  y1              = "OPC.like",
  y2              = "Cilia.like",
  group.by =  "subtype"
)
p__
```

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

### Generate Triadic Color Scheme

Source: https://enblacar.github.io/SCpubr-book/06_accessories/01_colorpalette.html

Generates a triadic color scheme from a base color. This creates three colors evenly spaced around the color wheel, offering vibrant and balanced palettes.

```r
colors <- SCpubr::do_ColorPalette(colors.use = "#E63946",
                                  triadic = TRUE)
colors
#> [1] "#E63946" "#46E639" "#3946E6"
```

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

### Strip Plot Colored by Continuous Value

Source: https://enblacar.github.io/SCpubr-book/04_distributions/04_stripplot.html

Generates a strip plot where points are colored according to a continuous variable. This allows for visualizing the intensity or magnitude of a feature across the plot.

```r
p <- SCpubr::do_StripPlot(sample = sample,
                          features = "nCount_RNA",
                          scale_type = "continuous")
p__
```

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

### Split DotPlot by Condition

Source: https://enblacar.github.io/SCpubr-book/02_gene_expression/04_dotplot.html

Use the `split.by` parameter to create separate facets for each condition. Note that `split.by` cannot be combined with named feature lists, clustering, or z-scoring.

```R
p <- SCpubr::do_DotPlot(sample = sample,
                        features = c("CDC25C", "KIF18B", "KIF14", "CENPE"),
                        split.by = "subtype")
p__
```

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

### CNV Heatmap Including Chromosome Arms

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/08_cnvheatmap.html

Generates a CNV heatmap that includes both p and q arms of chromosomes. This provides a more granular view of CNV distribution. Requires chromosome location data.

```r
p <- SCpubr::do_CNVHeatmap(sample = sample,
                           infercnv_object = infercnv_obj,
                           chromosome_locations = human_chr_locations,
                           include_chr_arms = TRUE)
p
```

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

### Adjust Top Interactions for Ligand-Receptor Plot

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/09_ligandreceptorplot.html

Customizes the number of top ligand-receptor interactions displayed in the plot. Useful for focusing on the most significant interactions.

```r
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   top_interactions = 50)
p
```

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

### Faceting Ligand-Receptor Plot

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/09_ligandreceptorplot.html

Splits the ligand-receptor interaction plot into facets based on either the ligand or the receptor complex. This helps in analyzing interactions from a specific perspective.

```r
# Facet by ligand
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   split.by = "ligand.complex")

# Facet by receptor
p <- SCpubr::do_LigandReceptorPlot(liana_output = liana_output,
                                   split.by = "receptor.complex")
p
```

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

### Volcano Plot Using Text Labels

Source: https://enblacar.github.io/SCpubr-book/05_downstream_analyses/07_volcanoplot.html

Uses text labels instead of points for genes on the volcano plot. This can improve readability for certain datasets.

```r
p <- SCpubr::do_VolcanoPlot(sample = sample,
                            de_genes = de_genes,
                            use_labels = TRUE)
p
```