Introduction to ggforge

Introduction

ggforge is a comprehensive visualization framework designed for biomedical and bioinformatics research. This package is greatly inspired by and modified from plotthis by Panwen Wang, with enhancements for high-quality biomedical visualizations.

Key Features

🎨 48 plotting functions
🧬 Specialized for biomedical and bioinformatics research
🎯 Unified API design across all functions
🌈 Extensive color palette collection
🔧 Intelligent type detection and automatic styling
📊 Easy multi-panel figure composition

Installation

# Install from GitHub
devtools::install_github("Zaoqu-Liu/ggforge")

Loading the Package

library(ggforge)
library(ggplot2)
library(dplyr)
set.seed(8525)

Core Concepts

Unified API Design

All ggforge plotting functions follow a consistent pattern:

PlotFunction(
  data,                    # Input data frame
  x, y,                    # Main aesthetics
  group_by = NULL,         # Grouping variable
  split_by = NULL,         # Split into multiple plots
  palette = "Paired",      # Color palette
  theme = "theme_ggforge", # Theme
  ...                      # Function-specific parameters
)

Intelligent Type Detection

ggforge automatically detects variable types and applies appropriate styling:

Continuous: Numeric variables → gradient scales
Discrete: Factors/characters → discrete colors
Temporal: Date/DateTime → time axis formatting

Split and Combine

Create multi-panel figures easily with split_by:

LinePlot(..., split_by = "group", combine = TRUE, nrow = 1)

Basic Statistical Plots

Scatter Plots

Visualize relationships between two continuous variables:

# Create sample data
scatter_data <- data.frame(
  x = rnorm(100),
  y = rnorm(100),
  group = sample(c("A", "B", "C"), 100, replace = TRUE)
)
scatter_data$y <- scatter_data$y + 0.5 * scatter_data$x

ScatterPlot(
  data = scatter_data,
  x = "x",
  y = "y",
  group_by = "group",
  palette = "npg",
  add_smooth = TRUE,
  add_stat = TRUE,
  title = "Correlation Analysis",
  xlab = "Variable X",
  ylab = "Variable Y"
)

Key parameters:

add_smooth: Add regression line
add_stat: Display correlation statistics
group_by: Color points by group

Box Plots

Compare distributions across groups:

# Create sample data
box_data <- data.frame(
  group = rep(c("Control", "Treat1", "Treat2"), each = 40),
  value = c(rnorm(40, 10, 2), rnorm(40, 12, 2.5), rnorm(40, 15, 2))
)

BoxPlot(
  data = box_data,
  x = "group",
  y = "value",
  palette = "lancet",
  add_point = TRUE,
  pt_alpha = 0.4,
  title = "Treatment Effect Comparison",
  xlab = "Treatment Group",
  ylab = "Expression Level"
)

Violin Plots

Show the full distribution shape:

ViolinPlot(
  data = box_data,
  x = "group",
  y = "value",
  palette = "npg",
  add_box = TRUE,
  add_point = TRUE,
  pt_size = 0.8,
  pt_alpha = 0.4,
  title = "Distribution Comparison"
)

Density Plots

Smooth distribution visualization:

DensityPlot(
  data = box_data,
  x = "value",
  group_by = "group",
  palette = "npg",
  add_rug = TRUE,
  title = "Distribution Density",
  xlab = "Expression Value"
)

Bar Plots

Summarize data with error bars:

BarPlot(
  data = box_data,
  x = "group",
  y = "value",
  palette = "Set2",
  add_errorbar = TRUE,
  errorbar_type = "se",
  title = "Mean Values with Standard Error",
  xlab = "Treatment Group",
  ylab = "Mean Expression"
)

Line Plots

Visualize trends over time or continuous variables:

# Create time series data
time_data <- data.frame(
  time = rep(1:10, 3),
  value = c(
    cumsum(rnorm(10, 0.5, 1)),
    cumsum(rnorm(10, 0.3, 1)),
    cumsum(rnorm(10, 0.7, 1))
  ),
  group = rep(c("Group A", "Group B", "Group C"), each = 10)
)

LinePlot(
  data = time_data,
  x = "time",
  y = "value",
  group_by = "group",
  palette = "nejm",
  add_smooth = TRUE,
  title = "Time Course Analysis",
  xlab = "Time Point",
  ylab = "Value"
)

Enrichment Analysis

Enrichment analysis is crucial for interpreting high-throughput biological data.

Enrichment Network

Visualize relationships between enriched terms based on gene overlap:

# Load example enrichment data
data("enrich_multidb_example")

EnrichNetwork(
  data = enrich_multidb_example,
  top_term = 20,
  layout = "fr",
  palette = "Set3",
  title = "Enrichment Network Analysis"
)

What this shows:

Nodes represent enriched terms
Edges connect terms sharing genes
Node size indicates significance
Colors distinguish different databases

Enrichment Map

Create similarity-based enrichment maps:

data("enrich_example")

EnrichMap(
  data = enrich_example,
  top_term = 25,
  layout = "fr",
  palette = "Spectral",
  title = "GO Enrichment Map"
)

Interpretation:

Clusters of nodes indicate related biological processes
Distance reflects similarity between terms
Useful for identifying major functional themes

GSEA Visualization

Gene Set Enrichment Analysis (GSEA) identifies coordinated changes in pre-defined gene sets.

GSEA Summary Plot

Overview of multiple GSEA results:

# Load GSEA example data
data("gsea_example")

GSEASummaryPlot(
  data = gsea_example,
  top_term = 20,
  palette = "RdBu",
  title = "GSEA Results Overview"
)

Understanding the plot:

Each row represents a gene set
Running enrichment score (RES) shown as line plot
Normalized enrichment score (NES) shown as color
Quickly identify up/down-regulated pathways

Individual GSEA Plot

Detailed view of specific pathway enrichment:

GSEAPlot(
  data = gsea_example,
  gs = gsea_example$ID[1],
  title = gsea_example$Description[1]
)

Single-Cell Analysis

Single-cell RNA-seq reveals cellular heterogeneity at unprecedented resolution.

Dimensionality Reduction

Visualize cell populations in reduced dimensions:

# Load dimension reduction example
data("dim_example")

DimPlot(
  data = dim_example,
  dims = c("basis_1", "basis_2"),
  group_by = "clusters",
  palette = "igv",
  pt_size = 1.5,
  label = TRUE,
  label_insitu = TRUE,
  title = "Cell Clustering (UMAP)"
)

Best practices:

Use UMAP or t-SNE for visualization
Color by cluster assignments or cell types
Add labels for easier interpretation

Feature Expression

Overlay gene expression on dimensionality reduction:

# Create feature data
dim_example$feature <- rnorm(nrow(dim_example))

FeatureDimPlot(
  data = dim_example,
  dims = c("basis_1", "basis_2"),
  features = "feature",
  palette = "viridis",
  pt_size = 1.5,
  title = "Gene Expression on UMAP"
)

RNA Velocity

Visualize cellular dynamics and differentiation trajectories:

# Prepare embedding matrices
embedding <- as.matrix(dim_example[, c("basis_1", "basis_2")])
v_embedding <- as.matrix(dim_example[, c("stochasticbasis_1", "stochasticbasis_2")])

VelocityPlot(
  embedding = embedding,
  v_embedding = v_embedding,
  plot_type = "grid",
  title = "RNA Velocity Analysis"
)

Velocity plot types:

raw: Individual cell velocity vectors
grid: Smoothed velocity field
stream: Streamline visualization

Genomics Visualization

Volcano Plot

Standard visualization for differential expression:

# Create differential expression data
deg_data <- data.frame(
  gene = paste0("Gene", 1:500),
  log2FC = rnorm(500, 0, 1.5),
  pvalue = runif(500, 0, 0.1)
)
deg_data$padj <- p.adjust(deg_data$pvalue, method = "BH")

# Add some significant genes
sig_up <- sample(1:500, 25)
sig_down <- sample(1:500, 25)
deg_data$log2FC[sig_up] <- abs(rnorm(25, 2, 0.5))
deg_data$log2FC[sig_down] <- -abs(rnorm(25, 2, 0.5))
deg_data$padj[c(sig_up, sig_down)] <- runif(50, 0, 0.01)

VolcanoPlot(
  data = deg_data,
  x = "log2FC",
  y = "padj",
  label_by = "gene",
  x_cutoff = 1,
  y_cutoff = 0.05,
  nlabel = 10,
  title = "Differential Expression Analysis",
  xlab = "log2 Fold Change",
  ylab = "-log10(Adjusted P-value)"
)

Customization:

Adjust cutoffs for significance
Control number of labeled genes
Use custom color schemes

Manhattan Plot

GWAS and QTL mapping visualization:

# Create sample GWAS data
gwas_data <- data.frame(
  chr = rep(paste0("chr", 1:22), each = 500),
  pos = rep(1:500, 22) * 1e5,
  pvalue = runif(11000, 0, 1)
)
gwas_data$pvalue[sample(1:11000, 30)] <- runif(30, 0, 1e-8)

ManhattanPlot(
  data = gwas_data,
  chr_by = "chr",
  pos_by = "pos",
  pval_by = "pvalue",
  signif = 5e-8,
  title = "Genome-Wide Association Study"
)

Survival Analysis

Kaplan-Meier survival curves are essential for clinical research:

# Create survival data
surv_data <- data.frame(
  time = rexp(150, 0.01),
  status = sample(0:1, 150, replace = TRUE, prob = c(0.4, 0.6)),
  risk = sample(c("Low", "High"), 150, replace = TRUE)
)

KMPlot(
  data = surv_data,
  time = "time",
  status = "status",
  group_by = "risk",
  palette = "jco",
  show_risk_table = TRUE,
  show_conf_int = TRUE,
  show_pval = TRUE,
  title = "Overall Survival Analysis",
  xlab = "Time (months)",
  ylab = "Survival Probability"
)

Components:

Survival curves with confidence intervals
Risk table showing numbers at risk
Log-rank test p-value
Customizable time points

Cox Regression Analysis

Cox proportional hazards model for multivariate survival analysis.

Forest Plot

# Create sample data with multiple covariates
cox_data <- data.frame(
  time = rexp(200, 0.01),
  event = sample(0:1, 200, replace = TRUE, prob = c(0.3, 0.7)),
  age = rnorm(200, 60, 10),
  bmi = rnorm(200, 25, 4),
  gender = sample(c("Male", "Female"), 200, replace = TRUE),
  stage = sample(c("I", "II", "III", "IV"), 200, replace = TRUE),
  treatment = sample(c("A", "B"), 200, replace = TRUE)
)

CoxPlot(
  data = cox_data,
  time = "time",
  event = "event",
  vars = c("age", "bmi", "gender", "stage", "treatment"),
  plot_type = "forest",
  palette = "nejm",
  title = "Multivariate Cox Regression"
)

Interpretation:

Points represent hazard ratios (HR)
Error bars show 95% confidence intervals
Colors indicate risk direction: Red (Risky, HR > 1), Blue (Protective, HR < 1), Grey (Not significant)
Legend positioned at bottom for easy reading

Detailed Forest Plot

CoxPlot(
  data = cox_data,
  time = "time",
  event = "event",
  vars = c("age", "gender", "stage", "treatment"),
  plot_type = "forest2",
  palette = "lancet",
  digits = 2,
  title = "Cox Analysis with Statistical Details"
)

Features:

Includes statistical table alongside forest plot
HR values with 95% CI displayed
P-values for each covariate
Automatic handling of categorical and continuous variables

Network Visualization

Heatmap

Enhanced heatmap for matrix data:

# Create sample matrix
set.seed(123)
mat <- matrix(rnorm(100), 10, 10)
rownames(mat) <- paste0("Gene", 1:10)
colnames(mat) <- paste0("Sample", 1:10)

Heatmap(
  data = mat,
  palette = "RdBu",
  title = "Gene Expression Heatmap"
)

Chord Diagram

Visualize relationships between categories:

# Create interaction data
chord_data <- data.frame(
  from = c("CD4 T", "CD8 T", "B cell", "NK", "Monocyte"),
  to = c("Fibroblast", "Endothelial", "Fibroblast", "Tumor", "Tumor"),
  value = c(15, 20, 10, 25, 18)
)

ChordPlot(
  data = chord_data,
  from = "from",
  to = "to",
  y = "value",
  palette = "Set3",
  title = "Cell-Cell Interaction Network"
)

Venn Diagram

Visualize set overlaps:

# Create gene sets
venn_data <- list(
  SetA = paste0("Gene", 1:100),
  SetB = paste0("Gene", 50:150),
  SetC = paste0("Gene", 80:180)
)

VennDiagram(
  data = venn_data,
  palette = "Set2",
  title = "Gene Set Overlap"
)

More Statistical Plots

ROC Curve

Receiver Operating Characteristic curves for classifier evaluation:

roc_data <- data.frame(
  truth = sample(0:1, 200, replace = TRUE),
  score = rnorm(200)
)
roc_data$score <- roc_data$score + roc_data$truth * 1.5

ROCCurve(
  data = roc_data,
  truth_by = "truth",
  score_by = "score",
  palette = "lancet",
  title = "ROC Analysis"
)

UpSet Plot

Complex set intersection visualization (alternative to Venn):

gene_lists <- list(
  Pathway_A = paste0("Gene", sample(1:200, 50)),
  Pathway_B = paste0("Gene", sample(1:200, 60)),
  Pathway_C = paste0("Gene", sample(1:200, 45)),
  Pathway_D = paste0("Gene", sample(1:200, 55))
)

UpsetPlot(gene_lists, palette = "Set2")

Sankey / Alluvial Plot

Visualize flow and transitions between categories:

flow_data <- data.frame(
  stage1 = rep(c("Healthy", "At Risk", "Disease"), each = 3),
  stage2 = rep(c("Recovered", "Stable", "Progressed"), 3),
  count = c(40, 8, 2, 5, 20, 5, 2, 5, 13)
)

SankeyPlot(flow_data, x = c("stage1", "stage2"), y = "count", palette = "Set3")

Radar Plot

Multivariate comparison on a circular grid:

radar_data <- data.frame(
  metric = rep(c("Speed", "Power", "Defense", "Accuracy", "Stamina"), 2),
  value = c(8, 6, 9, 7, 5, 5, 9, 4, 8, 7),
  player = rep(c("Player A", "Player B"), each = 5)
)

RadarPlot(
  radar_data,
  x = "metric",
  y = "value",
  group_by = "player",
  palette = "Set1",
  title = "Player Comparison"
)

Jitter Plot

Individual observation visualization with statistics:

JitterPlot(
  data = box_data,
  x = "group",
  y = "value",
  palette = "npg",
  highlight = "value > 15",
  highlight_color = "red",
  title = "Individual Observations"
)

Correlation Plot

Scatter plot with regression statistics:

cor_data <- data.frame(
  gene_A = rnorm(60),
  gene_B = rnorm(60),
  tissue = sample(c("Normal", "Tumor"), 60, replace = TRUE)
)
cor_data$gene_B <- cor_data$gene_A * 0.7 + rnorm(60, sd = 0.5)

CorPlot(
  data = cor_data,
  x = "gene_A",
  y = "gene_B",
  group_by = "tissue",
  palette = "jco",
  add_smooth = TRUE,
  title = "Gene Correlation Analysis"
)

Area Plot

Stacked area for composition change:

area_data <- data.frame(
  time = rep(paste0("T", 1:6), 3),
  proportion = c(0.5, 0.45, 0.4, 0.35, 0.3, 0.25,
                 0.3, 0.3, 0.35, 0.35, 0.4, 0.4,
                 0.2, 0.25, 0.25, 0.3, 0.3, 0.35),
  celltype = rep(c("Epithelial", "Immune", "Stromal"), each = 6)
)

AreaPlot(
  area_data,
  x = "time",
  y = "proportion",
  group_by = "celltype",
  palette = "nejm",
  title = "Cell Composition Change"
)

Pie & Ring Charts

cell_comp <- data.frame(
  type = c("T cell", "B cell", "NK cell", "Monocyte", "Other"),
  count = c(35, 20, 15, 20, 10)
)

p1 <- PieChart(cell_comp, x = "type", y = "count", palette = "Set2",
  title = "Pie Chart")
p2 <- RingPlot(cell_comp, x = "type", y = "count", group_by = "type",
  palette = "Set2", title = "Ring Chart")

patchwork::wrap_plots(p1, p2, nrow = 1)

Word Cloud

Visualize text frequency data:

terms <- data.frame(
  word = c("apoptosis", "proliferation", "migration", "invasion",
           "angiogenesis", "metastasis", "differentiation", "inflammation",
           "signaling", "metabolism", "autophagy", "senescence",
           "immunity", "transcription", "translation", "epigenetic"),
  score = c(8, 7, 6, 5, 9, 4, 7, 8, 6, 5, 3, 4, 7, 5, 4, 6)
)

WordCloudPlot(
  terms,
  word_by = "word",
  score_by = "score",
  palette = "Spectral",
  title = "Biological Process Keywords"
)

Discovering Functions

Use ggforge_gallery() to explore all available plotting functions:

# Show all functions
ggforge_gallery()

# Filter by category
ggforge_gallery("enrichment")
ggforge_gallery("survival")

Color Palettes

ggforge includes extensive color palettes from multiple sources:

# Show available palettes
show_palettes(head(names(palette_list), 20))
#>  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
#> [16] "16" "17" "18" "19" "20"

Palette sources:

RColorBrewer palettes
ggsci journal palettes (Nature, Science, NEJM, Lancet, JAMA, JCO)
viridis color scales
d3 categorical scales

Usage tips:

Use colorblind-friendly palettes (viridis, cividis)
Match palette to data type (sequential vs. categorical)
Consider journal requirements

Advanced Features

Multi-Panel Layouts

Create complex multi-panel figures with split_by:

# Create data with multiple groups
multi_data <- data.frame(
  x = rep(1:10, 9),
  y = rnorm(90, rep(1:3, each = 30), 0.5),
  group = rep(c("A", "B", "C"), each = 30),
  condition = rep(rep(c("Ctrl", "Treat1", "Treat2"), each = 10), 3)
)

LinePlot(
  data = multi_data,
  x = "x",
  y = "y",
  group_by = "condition",
  split_by = "group",
  palette = "nejm",
  combine = TRUE,
  nrow = 1
)

Faceting

Use facet_by for within-plot faceting:

# Create faceted data
facet_data <- data.frame(
  x = rep(1:20, 6),
  y = rnorm(120) + rep(rep(c(0, 2, 4), each = 20), 2),
  condition = rep(c("Cond1", "Cond2"), each = 60),
  time = rep(rep(c("Day1", "Day2", "Day3"), each = 20), 2)
)

LinePlot(
  data = facet_data,
  x = "x",
  y = "y",
  group_by = "condition",
  facet_by = "time",
  palette = "jco",
  add_smooth = TRUE
)

Theme Customization

All plots support theme customization:

# Apply different themes
BoxPlot(
  data = box_data,
  x = "group",
  y = "value",
  palette = "npg",
  theme = "theme_ggforge",
  theme_args = list(base_size = 14)
)

Best Practices

Data Preparation

Always ensure your data is in tidy format:

One row per observation
One column per variable
Each value in its own cell

# Good: Tidy format
data_tidy <- data.frame(
  sample = rep(c("S1", "S2", "S3"), each = 3),
  gene = rep(c("GeneA", "GeneB", "GeneC"), 3),
  expression = rnorm(9)
)

# Bad: Wide format
# data_wide <- data.frame(
#   sample = c("S1", "S2", "S3"),
#   GeneA = rnorm(3),
#   GeneB = rnorm(3),
#   GeneC = rnorm(3)
# )

Color Selection

Guidelines for choosing colors:

Colorblind-friendly: Use viridis, cividis, or similar
Consistency: Use same colors for same categories across figures
Contrast: Ensure sufficient contrast for readability
Journal requirements: Check specific requirements

High-Resolution Export

Recommended settings for saving figures:

# High-resolution PDF
ggsave(
  "figure.pdf",
  plot = your_plot,
  width = 8,
  height = 6,
  dpi = 300,
  device = "pdf"
)

# TIFF for some journals
ggsave(
  "figure.tiff",
  plot = your_plot,
  width = 8,
  height = 6,
  dpi = 600,
  device = "tiff",
  compression = "lzw"
)

Reproducibility

Always set seed for reproducible results:

set.seed(8525) # ggforge default seed

Troubleshooting

Common Issues

Issue 1: Column not found

# Check column names (case-sensitive)
colnames(your_data)

Issue 2: Palette not working

# See available palettes
names(palette_list)

# Or use custom colors
custom_colors <- c("#1F77B4", "#FF7F0E", "#2CA02C")

Issue 3: Legend position

# Control legend
YourPlot(..., theme_args = list(legend.position = "bottom"))

Getting Help

Documentation: ?FunctionName or help(FunctionName)
Examples: example(FunctionName)
GitHub Issues: https://github.com/Zaoqu-Liu/ggforge/issues
Email: liuzaoqu@163.com

Acknowledgments

This package is greatly inspired by and modified from plotthis by Panwen Wang. We are deeply grateful for the original work and design philosophy that made ggforge possible.

We also acknowledge: - ggplot2 for the grammar of graphics - patchwork for multi-panel composition - The entire R community for their excellent packages

Session Info

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.2.0   ggplot2_4.0.2 ggforge_1.0.1
#> 
#> loaded via a namespace (and not attached):
#>   [1] rlang_1.1.7           magrittr_2.0.4        clue_0.3-67          
#>   [4] GetoptLong_1.1.0      otel_0.2.0            matrixStats_1.5.0    
#>   [7] compiler_4.5.2        mgcv_1.9-3            png_0.1-8            
#>  [10] systemfonts_1.3.1     vctrs_0.7.1           ggalluvial_0.12.6    
#>  [13] stringr_1.6.0         pkgconfig_2.0.3       shape_1.4.6.1        
#>  [16] crayon_1.5.3          fastmap_1.2.0         backports_1.5.0      
#>  [19] magick_2.9.1          labeling_0.4.3        rmarkdown_2.30       
#>  [22] markdown_2.0          ragg_1.5.0            purrr_1.2.1          
#>  [25] xfun_0.56             cachem_1.1.0          litedown_0.9         
#>  [28] jsonlite_2.0.0        tweenr_2.0.3          parallel_4.5.2       
#>  [31] cluster_2.1.8.1       R6_2.6.1              bslib_0.10.0         
#>  [34] stringi_1.8.7         RColorBrewer_1.1-3    jquerylib_0.1.4      
#>  [37] ggmanh_1.14.0         Rcpp_1.1.1            iterators_1.0.14     
#>  [40] knitr_1.51            zoo_1.8-15            IRanges_2.44.0       
#>  [43] Matrix_1.7-4          splines_4.5.2         igraph_2.2.2         
#>  [46] tidyselect_1.2.1      dichromat_2.0-0.1     yaml_2.3.12          
#>  [49] ggVennDiagram_1.5.7   doParallel_1.0.17     codetools_0.2-20     
#>  [52] ggwordcloud_0.6.2     lattice_0.22-7        tibble_3.3.1         
#>  [55] plyr_1.8.9            withr_3.0.2           S7_0.2.1             
#>  [58] evaluate_1.0.5        gridGraphics_0.5-1    desc_1.4.3           
#>  [61] survival_3.8-3        polyclip_1.10-7       xml2_1.5.2           
#>  [64] ggupset_0.4.1         circlize_0.4.17       pillar_1.11.1        
#>  [67] checkmate_2.3.4       foreach_1.5.2         stats4_4.5.2         
#>  [70] generics_0.1.4        metR_0.18.3           S4Vectors_0.48.0     
#>  [73] commonmark_2.0.0      scales_1.4.0          glue_1.8.0           
#>  [76] proxyC_0.5.2          tools_4.5.2           ggnewscale_0.5.2     
#>  [79] data.table_1.18.2.1   forcats_1.0.1         fs_1.6.6             
#>  [82] grid_4.5.2            Cairo_1.7-0           tidyr_1.3.2          
#>  [85] colorspace_2.1-2      nlme_3.1-168          patchwork_1.3.2      
#>  [88] ggforce_0.5.0         cli_3.6.5             textshaping_1.0.4    
#>  [91] plotROC_2.3.3         ComplexHeatmap_2.26.1 gtable_0.3.6         
#>  [94] sass_0.4.10           digest_0.6.39         BiocGenerics_0.56.0  
#>  [97] ggrepel_0.9.7         rjson_0.2.23          farver_2.1.2         
#> [100] memoise_2.0.1         htmltools_0.5.9       pkgdown_2.2.0        
#> [103] lifecycle_1.0.5       GlobalOptions_0.1.3   gridtext_0.1.6       
#> [106] MASS_7.3-65

Zaoqu Liu

2026-03-01