在模仿中精进数据可视化_使用R语言绘制Mantel Test图
❝
在模仿中精进数据可视化该系列推文中，我们将从各大顶级学术期刊的Figure入手，
解读文章的绘图思路，
模仿文章的作图风格，
构建适宜的绘图数据，
并且将代码应用到自己的实际论文中。
绘图缘由：小伙伴们总会展示出一些非常好看且精美的图片。我大概率会去学习和复现一下。其实每个人的时间和精力都非常有限和异常宝贵的。之所以我会去做，主要有以下原因：
图片非常好看，我自己看着也手痒痒 图片我自己在Paper也用的上，储备着留着用 保持了持续学习的状态
❝今天绘制一张微生物领域常见的图
Mantel Test图。
使用缊哥的神包linkET。
不过我今天绘制的结果，略有一些不同。
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(vegan)
library(linkET)
library(ggnewscale)
加载数据
####----load Data----####
data("varechem", package = "vegan")
data("varespec", package = "vegan")
mantel <- mantel_test(varespec, varechem,
spec_select = list(Spec01 = 1:7,
Spec02 = 8:18,
Spec03 = 19:37,
Spec04 = 38:44)) %>%
mutate(rd = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf),
labels = c("< 0.2", "0.2 - 0.4", ">= 0.4")),
pd = cut(p, breaks = c(-Inf, 0.01, 0.05, Inf),
labels = c("< 0.01", "0.01 - 0.05", ">= 0.05")))
绘图
####----plot----####
p <- qcorrplot(correlate(varechem), type = "upper", diag = FALSE, grid_col = NA) +
geom_tile(color = "#000000", fill = NA, linewidth = 0.4) +
geom_point(aes(size = abs(r), fill = r), shape = 21) +
scale_size(range = c(4, 10)) +
new_scale("size") +
geom_couple(aes(colour = pd, size = rd),
data = mantel,
curvature = nice_curvature()) +
scale_fill_gradient2(low = "#4d9221", high = "#c51b7d") +
scale_size_manual(values = c(0.5, 1.5, 3)) +
scale_colour_manual(values = c("#f46d43","#762a83","#CCCCCC99")) +
guides(size = guide_legend(title = "Mantel's r",
override.aes = list(colour = "grey35"),
order = 2),
colour = guide_legend(title = "Mantel's p",
override.aes = list(size = 3),
order = 1),
fill = guide_colorbar(title = "Pearson's r", order = 3))
p
ggsave(filename = "Output/p.pdf",
plot = p,
height = 8,
width = 9)
版本信息
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS 15.1.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Shanghai
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggnewscale_0.5.0 linkET_0.0.7.4 vegan_2.6-4 lattice_0.22-5 permute_0.9-7 lubridate_1.9.3
[7] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[13] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] utf8_1.2.4 generics_0.1.3 xml2_1.3.5 stringi_1.8.3 hms_1.1.3
[6] magrittr_2.0.3 grid_4.3.0 timechange_0.2.0 RColorBrewer_1.1-3 Matrix_1.6-5
[11] mgcv_1.9-0 fansi_1.0.6 scales_1.3.0 textshaping_0.3.7 cli_3.6.3
[16] rlang_1.1.4 commonmark_1.9.0 munsell_0.5.1 splines_4.3.0 withr_3.0.1
[21] tools_4.3.0 parallel_4.3.0 tzdb_0.4.0 colorspace_2.1-1 vctrs_0.6.5
[26] R6_2.5.1 lifecycle_1.0.4 MASS_7.3-60 ragg_1.2.6 cluster_2.1.6
[31] pkgconfig_2.0.3 pillar_1.9.0 gtable_0.3.5 glue_1.8.0 Rcpp_1.0.13
[36] systemfonts_1.1.0 xfun_0.49 tidyselect_1.2.1 rstudioapi_0.15.0 farver_2.1.2
[41] nlme_3.1-163 labeling_0.4.3 compiler_4.3.0 markdown_1.12 gridtext_0.1.5
历史绘图合集
公众号推文一览
进化树合集
环状图
散点图
基因家族合集
换一个排布方式：
首先查看基础版热图:
然后再看进阶版热图:
基因组共线性
WGCNA ggplot2版本
其他科研绘图
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