RNA Velocity Plot

Author

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

Modified

2026-11-05

🤖 AI Skill — Download the Bizard unified skill for your AI assistant ⬇️ Download Skill ZIP

🔌 MCP Server — Let AI agents (Claude Code, Cursor, …) query Bizard live: claude mcp add bizard -- npx @openbiox/bizard-mcp — or the free hosted instance (no install, faster from mainland China): claude mcp add --transport http bizard http://biotree.top:38123/bizard-mcp/mcp 📖 MCP Docs

📖 Cite Bizard — if Bizard helps your research, please cite: Li, K., Zheng, H., Huang, K., et al. (2026). Bizard: A Community-Driven Platform for Accelerating and Enhancing Biomedical Data Visualization. iMetaMed, e70038. 🔗 DOI / BibTeX

RNA velocity — the time derivative of gene expression — is estimated from the ratio of unspliced to spliced mRNA in single-cell RNA-seq [1]. The canonical figure projects velocity arrows onto a low-dimensional embedding (PCA or UMAP): longer arrows indicate higher predicted transcriptional activity. This tutorial simulates velocity data and draws the arrow plot with geom_segment() + arrow().

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: R
Dependent packages: ggplot2; dplyr

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

library(ggplot2)
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)

 [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

With real data, velocyto.R computes a cell-level velocity matrix from the spliced/unspliced counts, then projects the arrows onto PCA or UMAP coordinates. We simulate that output directly: 120 cells in 3 clusters along a differentiation trajectory, with velocity vectors pointing forward along PC1.

set.seed(2501)
n_per <- 40
n_cell <- n_per * 3
cell_type <- rep(c("Progenitor", "Transitional", "Mature"), each = n_per)

pca1 <- c(rep(1, n_per), rep(5, n_per), rep(9, n_per)) + rnorm(n_cell, sd = 1.5)
pca2 <- c(rnorm(n_per, 3), rnorm(n_per, 5), rnorm(n_per, 3)) + rnorm(n_cell, sd = 0.5)

# velocity: points forward along PC1, magnitude increases with differentiation
vel_x <- c(runif(n_per, 0.5, 1.5), runif(n_per, 1.5, 3.0), runif(n_per, 2.5, 4.5))
vel_y <- rnorm(n_cell, sd = 0.5)

vel_df <- data.frame(PC1 = pca1, PC2 = pca2, cluster = cell_type,
                     vx = vel_x, vy = vel_y)
head(vel_df, 3)

       PC1      PC2    cluster        vx          vy
1 0.809963 6.042075 Progenitor 0.9189087 -0.66934696
2 1.598161 1.912981 Progenitor 0.6415596 -0.08982668
3 1.412747 2.820119 Progenitor 0.7437892 -0.78530950

Visualization

1. RNA Velocity Arrows on PCA

ggplot(vel_df, aes(PC1, PC2, colour = cluster)) +
  geom_point(size = 1.5, alpha = 0.5) +
  geom_segment(aes(xend = PC1 + vx, yend = PC2 + vy),
               arrow = arrow(length = unit(0.15, "cm"), type = "closed"),
               linewidth = 0.5, alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = c(Progenitor = "#445EAD", Transitional = "#EABF00",
                                 Mature = "#B2182B")) +
  labs(x = "PC1", y = "PC2", colour = "Cell type",
       title = "RNA velocity projected on PCA") +
  theme_classic(base_size = 11) +
  theme(legend.position = "bottom")

Figure 1: RNA velocity arrows projected on PCA: arrows point from each cell towards its predicted future state. Longer arrows indicate higher transcriptional activity.

2. Velocity Magnitude by Cell Type

vel_df$speed <- sqrt(vel_df$vx^2 + vel_df$vy^2)
p_kw <- kruskal.test(speed ~ cluster, data = vel_df)$p.value

ggplot(vel_df, aes(cluster, speed, fill = cluster)) +
  geom_boxplot(outlier.shape = NA, width = 0.55) +
  geom_jitter(width = 0.15, size = 0.6, alpha = 0.5, show.legend = FALSE) +
  scale_fill_manual(values = c(Progenitor = "#445EAD", Transitional = "#EABF00",
                               Mature = "#B2182B")) +
  labs(title = sprintf("Velocity magnitude (Kruskal-Wallis %s)",
       ifelse(p_kw < 0.001, "p < 0.001", sprintf("p = %.3f", p_kw))),
       x = NULL, y = "Velocity magnitude") +
  theme_classic(base_size = 11) +
  theme(legend.position = "none")

Figure 2: Velocity magnitude (speed) by cell type; mature cells show the highest transcriptional activity.

Tip

Practice tips

The velocity arrows use geom_segment() with arrow(length = unit(0.15, "cm"), type = "closed") — adjust the length for readability at different figure sizes.
With real data, use velocyto.R::gene.relative.velocity.estimates() to compute the velocity matrix, then velocyto.R::show.velocity.on.embedding.cor() for the built-in arrow plot; the ggplot2 recipe above gives finer control over aesthetics.
Colour the arrows by speed (sqrt(vx^2 + vy^2)) instead of cell type to highlight regions of high transcriptional activity.

Application

RNA velocity arrows are the standard figure for demonstrating differentiation direction in developmental and cancer scRNA-seq studies.

Reference

[1] La Manno G, Soldatov R, Zeisel A, et al. RNA velocity of single cells. Nature. 2018;560(7719):494-498. doi:10.1038/s41586-018-0414-6

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