在模仿中精进数据可视化_使用R语言绘制多组差异分析的火山图
❝
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
图片
直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(ggrepel)
library(ggfun)
加载数据
####----load Data----####
list.files(path = "./Input/", pattern = "_DEG.csv", full.names = T)
all_deg <- map(list.files(path = "./Input/", pattern = "_DEG.csv", full.names = T),
function(x){
read_delim(file = x, col_names = T, delim = ",") %>%
dplyr::mutate(group = str_remove(basename(x), pattern = "_DEG.*"))
}) %>%
do.call(rbind, .)
开始绘图
####----plot----####
p <- ggplot(data = all_deg) +
geom_jitter(data = all_deg %>%
dplyr::filter(change == "Normal"),
aes(x = group, y = log2FoldChange,
color = change, size = abs(log2FoldChange),
alpha = abs(log2FoldChange)),
width = 0.4) +
geom_jitter(data = all_deg %>%
dplyr::filter(change != "Normal"),
aes(x = group, y = log2FoldChange,
color = change, size = abs(log2FoldChange),
alpha = abs(log2FoldChange)),
width = 0.4) +
geom_jitter(data = all_deg %>%
dplyr::group_by(group) %>%
dplyr::arrange(desc(abs(log2FoldChange))) %>%
dplyr::slice_head(n = 15) %>%
dplyr::ungroup() %>%
na.omit(),
aes(x = group, y = log2FoldChange,
size = abs(log2FoldChange)),
width = 0.4,
shape = 21,
fill = "#dd1c77") +
geom_text_repel(data = all_deg %>%
dplyr::group_by(group) %>%
dplyr::arrange(desc(abs(log2FoldChange))) %>%
dplyr::slice_head(n = 15) %>%
dplyr::ungroup() %>%
na.omit(),
aes(x = group, y = log2FoldChange, label = SYMBOL)) +
geom_tile(aes(x = group, y = 0, fill = group), height = 0.4) +
geom_text(data = all_deg %>%
dplyr::select(group) %>%
dplyr::distinct(group, .keep_all = T),
aes(x = group, y = 0, label = group),
size = 6) +
geom_hline(yintercept = c(-log2(1.5), log2(1.5))) +
scale_y_continuous(limits = c(-5, 5)) +
scale_size(range = c(1,15)) +
scale_alpha(range = c(0.1, 1)) +
scale_color_manual(values = c("Up" = "#f46d43",
"Normal" = "#bdbdbd",
"Down" = "#3288bd")) +
scale_fill_manual(values = c('#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3')) +
theme_bw() +
theme(
axis.text = element_text(color = "#000000", size = 12),
axis.title = element_text(color = "#000000", size = 15),
panel.grid = element_blank(),
legend.background = element_roundrect(color = "#969696")
)
ggsave(filename = "Output/p.pdf",
plot = p,
height = 12,
width = 12)
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] ggfun_0.1.5 ggrepel_0.9.6 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[7] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] bit_4.0.5 gtable_0.3.5 compiler_4.3.0 crayon_1.5.2 Rcpp_1.0.13
[6] tidyselect_1.2.1 parallel_4.3.0 textshaping_0.3.7 systemfonts_1.1.0 scales_1.3.0
[11] R6_2.5.1 labeling_0.4.3 generics_0.1.3 munsell_0.5.1 pillar_1.9.0
[16] tzdb_0.4.0 rlang_1.1.4 utf8_1.2.4 stringi_1.8.3 bit64_4.0.5
[21] timechange_0.2.0 cli_3.6.3 withr_3.0.1 magrittr_2.0.3 grid_4.3.0
[26] vroom_1.6.4 rstudioapi_0.15.0 hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5
[31] glue_1.8.0 farver_2.1.2 ragg_1.2.6 fansi_1.0.6 colorspace_2.1-1
[36] tools_4.3.0 pkgconfig_2.0.3
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
合作、联系和交流
有很多小伙伴在后台私信作者，非常抱歉，我经常看不到导致错过，请添加下面的微信联系作者，一起交流数据分析和可视化。
