UMAP

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot UMAP 插件的源码版本教程，您也可以使用 Hiplot 网站实现无代码绘图，更多信息请查看以下链接:

https://hiplot.cn/basic/umap?lang=zh_cn

UMAP 是一种非线性降维算法，适用于高维数据降维到 2 维或 3 维并进行可视化。该算法能够使较大相似度的点，t 分布在低维空间中的距离更近；而对于低相似度的点，t 分布在低维空间中的距离更远。

环境配置

  • 系统: Cross-platform (Linux/MacOS/Windows)

  • 编程语言: R

  • 依赖包: data.table; jsonlite; umap; ggpubr

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("umap", quietly = TRUE)) {
  install.packages("umap")
}
if (!requireNamespace("ggpubr", quietly = TRUE)) {
  install.packages("ggpubr")
}

# 加载包
library(data.table)
library(jsonlite)
library(umap)
library(ggpubr)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-28
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package    * version  date (UTC) lib source
 data.table * 1.18.0   2025-12-24 [1] RSPM
 ggplot2    * 4.0.1    2025-11-14 [1] RSPM
 ggpubr     * 0.6.2    2025-10-17 [1] RSPM
 jsonlite   * 2.0.0    2025-03-27 [1] RSPM
 umap       * 0.2.10.0 2023-02-01 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

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

数据准备

载入数据为数据集（基因名称及其对应的基因表达值）和样本信息（样本名称及分组）。

# 加载数据
data1 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/umap/data.json")$exampleData$textarea[[1]])
data1 <- as.data.frame(data1)
data2 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/umap/data.json")$exampleData$textarea[[2]])
data2 <- as.data.frame(data2)


# 整理数据格式
sample.info <- data2
rownames(data1) <- data1[, 1]
data1 <- as.matrix(data1[, -1])
## umap
set.seed(123)
umap_info <- umap(t(data1))
colnames(umap_info$layout) <- c("UMAP_1", "UMAP_2")
# handle data
umap_data <- data.frame(
  sample = colnames(data1),
  umap_info$layout
)
colorBy <- sample.info[match(colnames(data1), sample.info[, 1]), "Species"]
colorBy <- factor(colorBy, level = colorBy[!duplicated(colorBy)])
umap_data$colorBy = colorBy
shapeBy <- NULL

# 查看数据
head(data1[,1:5])
              M1  M2  M3  M4  M5
Sepal.Length 5.1 4.9 4.7 4.6 5.0
Sepal.Width  3.5 3.0 3.2 3.1 3.6
Petal.Length 1.4 1.4 1.3 1.5 1.4
Petal.Width  0.2 0.2 0.2 0.2 0.2
head(data2)
  Samples Species
1      M1  setosa
2      M2  setosa
3      M3  setosa
4      M4  setosa
5      M5  setosa
6      M6  setosa

可视化

# umap
p <- ggscatter(data = umap_data, x = "UMAP_1", y = "UMAP_2", size = 2, 
               palette = "lancet", color = "colorBy") +
  labs(color = "group") +
  ggtitle("UMAP Plot") +
  theme_classic() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))

p
图 1: umap

不同颜色表示不同样本，与 PCA（主成分分析）图形解释相同，不同之处在于可视化效果，t-SNE 中对于不相似的点，用一个较小的距离会产生较大的梯度来让这些点排斥开来。