在模仿中精进数据可视化_进阶版本的火山图
❝
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
❝火山图，大家都不再陌生了，我之前也绘制了几个版本的火山图。可以说是还不错，看得过去。
链接如下：
链接如下：
❝那么，今天我们绘制第三版本的火山图
不标记基因版本
标记基因版本
依旧不废话，直接上代码：
加载R包
rm(list = ls())
####----load R Package----####
library(tidyverse)
library(ggrepel)
library(readxl)
library(ggfun)
加载数据
####----load Data----####
df <- read_xlsx(path = "test_data.xlsx", col_names = T) %>%
dplyr::filter(!is.na(Symbol)) %>%
dplyr::rowwise() %>%
dplyr::mutate(mean_value = mean(c_across(where(is.numeric)), na.rm = TRUE)) %>%
dplyr::mutate(Control_mean = mean(c_across(2:6)),
Treatment_mean = mean(c_across(7:11))
) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(mean_value)) %>%
dplyr::distinct(Symbol, .keep_all = T) %>%
dplyr::filter(mean_value > 0) %>%
dplyr::select(-mean_value) %>%
dplyr::mutate(Control_mean_log = log(Control_mean + 1),
Treatment_mean_log = log(Treatment_mean + 1)) %>%
dplyr::mutate(Change = case_when(
logFoldChange > 2 & PValue < 0.05 ~ "Up",
logFoldChange < -2 & PValue < 0.05 ~ "Down",
.default = "Normal"
))
可视化
####----Plot----####
p <- ggplot(data = df) +
geom_point(aes(x = Control_mean_log, y = Treatment_mean_log,
size = abs(logFoldChange), color = Change),
alpha = 0.25) +
geom_abline(intercept = log2(2), slope = 1, linetype = 2) +
geom_abline(intercept = -log2(2), slope = 1, linetype = 2) +
geom_abline(intercept = 0, slope = 1, linetype = 2) +
geom_text_repel(data = df %>%
dplyr::filter(Change == "Up") %>%
dplyr::arrange(desc(Control_mean_log)) %>%
dplyr::slice_head(n = 10),
aes(x = Control_mean_log,
y = Treatment_mean_log,
label = Symbol
),
nudge_x = -0.1,
nudge_y = .1,
segment.curvature = -1e-20,
arrow = arrow(length = unit(0.015, "npc")),
direction = "y", hjust = "right"
) +
geom_text_repel(data = df %>%
dplyr::filter(Change == "Down") %>%
dplyr::arrange(desc(Treatment_mean_log)) %>%
dplyr::slice_head(n = 10),
aes(x = Control_mean_log,
y = Treatment_mean_log,
label = Symbol
),
nudge_x = 0.1,
nudge_y = .1,
segment.curvature = -1e-20,
arrow = arrow(length = unit(0.015, "npc")),
direction = "y", hjust = "left"
) +
labs(x = "Control", y = "Treatment") +
scale_color_manual(values = c("Up" = "#fb6a4a",
"Normal" = "#d9d9d9",
"Down" = "#0570b0")) +
guides(color = guide_legend(override.aes = list(size = 5, alpha = 1))) +
theme_bw() +
theme(
panel.border =element_rect(linewidth = 1, color = "#000000"),
panel.grid = element_blank(),
legend.background = element_roundrect(color = "#000000"),
axis.text = element_text(size = 15, color = "#000000"),
axis.title = element_text(size = 20, color = "#000000")
)
p
ggsave(filename = "p.pdf",
plot = p,
height = 6,
width = 9)
不标记基因版本
标记基因版本
版本信息
####----sessionInfo----####
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS 14.6.1
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 readxl_1.4.3 ggrepel_0.9.6 lubridate_1.9.3 forcats_1.0.0
[6] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[11] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.5 crayon_1.5.2 compiler_4.3.0 tidyselect_1.2.1
[5] Rcpp_1.0.13 textshaping_0.3.7 systemfonts_1.1.0 scales_1.3.0
[9] R6_2.5.1 labeling_0.4.3 generics_0.1.3 munsell_0.5.1
[13] pillar_1.9.0 tzdb_0.4.0 rlang_1.1.4 utf8_1.2.4
[17] stringi_1.8.3 timechange_0.2.0 cli_3.6.3 withr_3.0.1
[21] magrittr_2.0.3 grid_4.3.0 rstudioapi_0.15.0 hms_1.1.3
[25] lifecycle_1.0.4 vctrs_0.6.5 glue_1.7.0 farver_2.1.2
[29] cellranger_1.1.0 ragg_1.2.6 fansi_1.0.6 colorspace_2.1-1
[33] tools_4.3.0 pkgconfig_2.0.3
历史绘图合集
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。
