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ggforge

Comprehensive Visualization Toolkit for Biomedical Research

ggforge is a comprehensive and elegant visualization framework designed for biomedical and bioinformatics research. Built on ggplot2 with a modern modular architecture, it provides 40+ plotting functions covering statistical graphics, enrichment analysis, single-cell transcriptomics, spatial analysis, survival analysis, and genomics visualization.

Documentation

Complete documentation is available at: https://zaoqu-liu.github.io/ggforge

Installation 

# Install from R-universe (recommended)
install.packages("ggforge", repos = "https://zaoqu-liu.r-universe.dev")

# Or install from GitHub
if (!require("remotes")) install.packages("remotes")
remotes::install_github("Zaoqu-Liu/ggforge")

Quick Start

Examples

Basic Statistical Plots

Box Plots

data <- data.frame(
  group = rep(c("Control", "Treatment A", "Treatment B"), each = 50),
  value = c(rnorm(50, 10, 2), rnorm(50, 12, 2.5), rnorm(50, 15, 2))
)

BoxPlot(
  data = data,
  x = "group",
  y = "value",
  palette = "lancet",
  add_point = TRUE,
  pt_alpha = 0.3,
  xlab = "Treatment Group",
  ylab = "Expression Level",
  title = "Gene Expression Across Treatment Groups"
)

Violin Plots

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

Scatter Plots

n <- 200
scatter_data <- data.frame(
  gene_A = rnorm(n, 100, 20),
  gene_B = rnorm(n, 100, 20),
  cell_type = sample(c("T cell", "B cell", "Monocyte"), n, replace = TRUE)
)
scatter_data$gene_B <- scatter_data$gene_B + 0.7 * scatter_data$gene_A + rnorm(n, 0, 10)

ScatterPlot(
  data = scatter_data,
  x = "gene_A",
  y = "gene_B",
  color_by = "cell_type",
  palette = "jco",
  add_smooth = TRUE,
  add_stat = TRUE,
  xlab = "Gene A Expression",
  ylab = "Gene B Expression",
  title = "Gene Expression Correlation"
)

Density Plots

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

Bar Plots

BarPlot(
  data = 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

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 Network

data("enrich_multidb_example")

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

Enrichment Map

data("enrich_example")

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

GSEA Visualization

GSEA Summary Plot

data("gsea_example")

GSEASummaryPlot(
  data = gsea_example,
  top_term = 20,
  palette = "RdBu",
  title = "GSEA Analysis Summary"
)

GSEA Enrichment Plot

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

Single-Cell Analysis

Dimensionality Reduction

data("dim_example")

DimPlot(
  data = dim_example,
  dims = c("basis_1", "basis_2"),
  group_by = "clusters",
  palette = "igv",
  pt_size = 1.2,
  label = TRUE,
  label_insitu = TRUE,
  title = "UMAP Clustering"
)

Feature Expression

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

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"
)

Genomics Visualization

Volcano Plot

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")

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)"
)

Manhattan Plot

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",
  threshold = 5e-8,
  title = "Genome-Wide Association Study"
)

Survival Analysis

Kaplan-Meier Curve

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"
)

Cox Regression - 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 - Forest Plot"
)

Cox Regression - 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 Regression with Statistical Table"
)

Network Visualization

Heatmap

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

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

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"
)

Color 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"

Advanced Features

Multi-Panel Layouts

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
)

Key Features

  • 48 plotting functions covering comprehensive biomedical visualization needs
  • Unified API design with consistent interface across all functions
  • Intelligent type detection for automatic variable styling
  • Extensive color palettes from RColorBrewer, ggsci, viridis, and custom collections
  • Multi-panel layouts with automatic data splitting and combination
  • Professional themes optimized for scientific publications

Function Categories

Statistical Plots

ScatterPlot(), LinePlot(), BarPlot(), BoxPlot(), ViolinPlot(), DensityPlot(), JitterPlot(), AreaPlot(), Histogram(), QQPlot(), TrendPlot(), RidgePlot(), DotPlot(), LollipopPlot(), WaterfallPlot()

Enrichment & Pathway Analysis

EnrichMap(), EnrichNetwork(), GSEASummaryPlot(), GSEAPlot()

Single-Cell & Spatial Analysis

DimPlot(), FeatureDimPlot(), VelocityPlot(), SpatImagePlot(), SpatPointsPlot(), SpatShapesPlot(), SpatMasksPlot()

Genomics

VolcanoPlot(), ManhattanPlot(), VennDiagram(), UpsetPlot()

Survival & Clinical

KMPlot() - Kaplan-Meier curves, CoxPlot() - Cox regression analysis, ROCCurve() - ROC curves

Networks & Relationships

CorPlot(), ChordPlot(), SankeyPlot(), Network(), AlluvialPlot()

Specialized Plots

Heatmap(), RadarPlot(), SpiderPlot(), PieChart(), RingPlot(), WordCloudPlot(), RarefactionPlot(), ClustreePlot(), CircosPlot(), SplitBarPlot()

Design Philosophy

ggforge follows key principles for scientific visualization:

  1. Consistency - Unified API design across all plotting functions
  2. Simplicity - Create complex visualizations with minimal code
  3. Flexibility - Full control over aesthetics and customization
  4. Intelligence - Automatic type detection and optimal defaults
  5. Quality - Professional output suitable for scientific publications

Citation

If you use ggforge in your research, please cite:

Liu Z (2025). ggforge: Comprehensive Visualization Toolkit for Biomedical Research.
R package version 1.0.0. https://github.com/Zaoqu-Liu/ggforge
Modified from plotthis by Panwen Wang. https://github.com/pwwang/plotthis

License

This project is licensed under the GPL-3 License. See LICENSE.md for details.

Author

Zaoqu Liu

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.

Additional acknowledgments: - ggplot2 - The grammar of graphics - patchwork - Multi-panel composition - The R community for their excellent packages and contributions