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Quadrant Scatter Plot

Author

[Editor] ZCode (GLM) — AI-generated, independently AI-reviewed;

Modified

2026-11-05

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A quadrant (or nine-grid) scatter plot displays two measurements for the same set of features, overlays quadrant threshold lines, and highlights named features of interest with ggrepel labels [1]. It is the standard figure for multi-omics concordance (RNA vs protein, RNA vs ATAC) or for showing genes in biologically meaningful regions (up/up, up/down, etc.).

Example

QuadrantPlot DEMO

Setup

  • System Requirements: Cross-platform (Linux/MacOS/Windows)

  • Programming Language: R

  • Dependent packages: ggplot2; ggrepel; dplyr

if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
if (!requireNamespace("ggrepel", quietly = TRUE)) install.packages("ggrepel")
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")

library(ggplot2)
library(ggrepel)
library(dplyr)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.6.2 (2026-10-29)
 os       Ubuntu 26.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-11-05
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.11.9 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 ! package * version    date (UTC) lib source
 P dplyr   * 1.2.1      2026-04-03 [?] RSPM
 P ggplot2 * 4.0.3.9000 2026-11-03 [?] Github (tidyverse/ggplot2@b638d65)
 P ggrepel * 0.9.8      2026-03-17 [?] RSPM

 [1] /home/runner/.cache/R/renv/library/Bizard-5140bdc6/linux-ubuntu-resolute/R-4.6/x86_64-pc-linux-gnu
 [2] /home/runner/work/_temp/Library
 [3] /opt/R/4.6.2/lib/R/library

 * ── Packages attached to the search path.
 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

We simulate log2 fold changes for 500 genes measured by RNA-seq and proteomics, with a subset of concordant and discordant genes, plus named highlights.

set.seed(2101)
n <- 500
genes <- paste0("Gene", sprintf("%04d", 1:n))
rna <- rnorm(n, sd = 1.5)
prot <- rna * 0.7 + rnorm(n, sd = 0.8)
# make a few discordant genes
disc <- sample(1:n, 15)
prot[disc] <- -rna[disc] + rnorm(15, sd = 0.3)
df_q <- data.frame(gene = genes, rna = rna, prot = prot)

# genes to label
label_genes <- c("Gene0001", "Gene0002", "Gene0015", "Gene0020",
                 "Gene0005", "Gene0008", "Gene0003")

Visualization

1. Nine-Grid Quadrant Scatter with Labels

threshold <- 1
ggplot(df_q, aes(rna, prot)) +
  geom_point(alpha = 0.4, size = 1.5, colour = "grey50") +
  geom_vline(xintercept = c(-threshold, threshold), linetype = "dashed", colour = "grey40") +
  geom_hline(yintercept = c(-threshold, threshold), linetype = "dashed", colour = "grey40") +
  geom_point(data = subset(df_q, gene %in% label_genes),
             colour = "#B2182B", size = 2.5) +
  ggrepel::geom_text_repel(
    data = subset(df_q, gene %in% label_genes),
    aes(label = gene), size = 3, colour = "#B2182B",
    box.padding = 0.4, max.overlaps = 20) +
  coord_equal(xlim = c(-4, 4), ylim = c(-4, 4)) +
  labs(x = "RNA-seq log2FC", y = "Proteomics log2FC",
       title = "RNA vs protein concordance") +
  theme_classic(base_size = 11)
Figure 1: Quadrant scatter: RNA-seq vs proteomics log2FC. Vertical and horizontal lines mark fold-change thresholds (±1). Named genes are labelled with ggrepel.

2. Coloured Quadrant Regions

df_q <- df_q |>
  mutate(quadrant = case_when(
    rna > threshold & prot > threshold ~ "Concordant up",
    rna < -threshold & prot < -threshold ~ "Concordant down",
    abs(rna) > threshold & abs(prot) > threshold ~ "Discordant",
    TRUE ~ "Not significant"))

quad_cols <- c("Concordant up" = "#B2182B", "Concordant down" = "#2166AC",
               "Discordant" = "#4D9342", "Not significant" = "grey80")

ggplot(df_q, aes(rna, prot, colour = quadrant)) +
  geom_point(size = 1.5, alpha = 0.6) +
  geom_vline(xintercept = c(-threshold, threshold), linetype = "dashed", colour = "grey40") +
  geom_hline(yintercept = c(-threshold, threshold), linetype = "dashed", colour = "grey40") +
  scale_colour_manual(values = quad_cols) +
  geom_point(data = subset(df_q, gene %in% label_genes),
             colour = "black", size = 2.5, shape = 21, fill = "gold") +
  ggrepel::geom_text_repel(
    data = subset(df_q, gene %in% label_genes),
    aes(label = gene), size = 3, colour = "black",
    box.padding = 0.4, max.overlaps = 20) +
  coord_equal(xlim = c(-4, 4), ylim = c(-4, 4)) +
  labs(x = "RNA-seq log2FC", y = "Proteomics log2FC", colour = "Quadrant") +
  theme_classic(base_size = 11) +
  theme(legend.position = "bottom")
Figure 2: Genes colour-coded by quadrant: concordant up (red), concordant down (blue), discordant (green), and non-significant (grey).
Tip

Practice tips

  • Adjust the threshold to match your data’s fold-change cutoff; the default ±1 corresponds to |log2FC| > 1.
  • Use geom_text_repel() with max.overlaps = 20 for readable labels even with many highlighted genes.
  • The quadrant assignment via case_when() can be adapted for any number of regions by adding more conditions.

Application

Quadrant scatter plots are the standard figure for multi-omics concordance, drug-response screens, and CRISPR screen comparisons.

Reference

[1] Xiaofan Lu, et al. FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency. iMetaMed. 2025. doi:10.1002/imm3.70005