和弦图
https://jokergoo.github.io/circlize_book/book/advanced-usage-of-chorddiagram.html
箱线点图+一对一的显著性检验
library(ggpubr)
library(ggplot2)
library(RColorBrewer)
library(gridExtra)
library(dplyr)
library(cowplot)
set.seed(123)
fm <- matrix(rnorm(100), nrow=200) %>% as.data.frame()
colnames(fm) <- c(paste0("Gene_", 1))
rownames(fm) <- paste0("Sample_", 1:200)
fm$alt_subtype <- rep(c('type1','type2','type3','type4'),time=c(50,50,50,50))
ggplot(fm, aes(x = alt_subtype, y = Gene_1, fill = alt_subtype)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.1, color = "grey20",size = 1) +
ylim(c(min(fm$Gene_1)-(max(fm$Gene_1)-min(fm$Gene_1))*0.1,
max(fm$Gene_1)+(max(fm$Gene_1)-min(fm$Gene_1))*0.1)) +
scale_fill_manual(values = brewer.pal(6, "Paired")[c(6,5,4,3)]) +
ylab("score (log2)") +
xlab("") +
ggtitle('Gene_1') +
theme_cowplot() +
theme(legend.position="none",
axis.title = element_text(size = 15),
axis.text = element_text(size = 15),
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0)) +
geom_signif(comparisons = list(c(1, 2)),map_signif_level = T, textsize = 5, test = wilcox.test,step_increase = 0.2)+
geom_signif(comparisons = list(c(3, 4)),map_signif_level = T, textsize = 5, test = wilcox.test,step_increase = 0.2)+
theme(plot.margin = unit(c(0.5,1,0,1), "cm"))
image.png
堆叠图
library(ggplot2)
result$clusters = factor(result$clusters,levels =c(1:11))
stack_plot=ggplot(result, aes(x=group, fill=clusters, y=Freq*100))+
# 数据输入：样本、物种、丰度
geom_col(position='stack') +
# stack：堆叠图
labs(x='group', y='Relative Abundance (%)')+
# 给xy轴取名
scale_y_continuous(expand=c(0, 0))+
# 调整y轴属性
theme(axis.text.x=element_text(angle=45, hjust=1))
# angle：调整横轴标签倾斜角度
# hjust：上下移动横轴标签
ggsave(plot=stack_plot, filename="stack_plot.pdf")
ggsave(plot=stack_plot, filename="stack_plot.png")
image.png
带回归线和bar和差异性检验的箱线图
library(tidyverse)
library(ggpubr)
library(magrittr)
library(ggsci)
library(ggsignif)
set.seed(123)
fm <- matrix(rnorm(100), nrow=200) %>% as.data.frame()
colnames(fm) <- c(paste0("Gene_", 1))
rownames(fm) <- paste0("Sample_", 1:200)
fm$alt_subtype <- rep(c('type1','type2','type3','type4'),time=c(50,50,50,50))
fm$group=NULL
text <- fm %>%
group_by(alt_subtype) %>%
top_n(1,Gene_1) %>%
column_to_rownames('alt_subtype') %>%
set_colnames('num') %>%
mutate(num=round(num,digits = 1)) %>%
mutate(alt_subtype=rownames(.))
p=ggplot(fm, aes(alt_subtype, Gene_1)) +
stat_boxplot(geom="errorbar", position=position_dodge(width=0.2), width=0.1) + # 使用 stat_boxplot 添加误差条
geom_boxplot(position=position_dodge(width=0.2), width=0.4) + # 添加箱线图
geom_point(aes(fill=alt_subtype, group=alt_subtype, color=alt_subtype), pch=21, position=position_dodge(0.2)) +
geom_text(data=text, aes(label=num, y=num + 0.1), size=4, color="black", hjust=0.5, vjust=0.5) + # 添加文本标签
geom_signif(comparisons=list(c("type1", "type2")), map_signif_level=TRUE, textsize=6, test="wilcox.test", step_increase=0.2) + # 为特定比较添加显著性标签
scale_size_continuous(range=c(1, 3)) + # 调整大小比例
stat_cor(label.y=4, aes(label=paste(..rr.label.., ..p.label.., sep="~`,`~"), group=1), color="black", label.x.npc="middle") + # 添加相关性标签
stat_regline_equation(label.y=3.75, aes(group=1), color="black", label.x.npc="middle") +
scale_fill_simpsons(alpha=0.7) +
scale_color_simpsons(alpha=0.7) +
labs(x=NULL, y=NULL) + # 删除x和y轴标签
theme(
plot.margin=unit(c(0.5, 0.5, 0.5, 0.5), units="cm"), # 调整图形边距
axis.line=element_line(color="black", size=0.4), # 自定义坐标轴线
panel.grid.minor=element_blank(), # 删除次要网格线
panel.grid.major=element_line(size=0.2, color="#e5e5e5"), # 自定义主要网格线
panel.background=element_blank(), # 删除面板背景
axis.text.y=element_text(color="black", size=10, face="bold"), # 自定义y轴上的文本
axis.text.x=element_text(color="black", size=10, hjust=1, face="bold", angle=45), # 自定义x轴上的文本
axis.line.x.top=element_line(color="black"), # 自定义顶部x轴线
axis.text.x.top=element_blank(), # 删除顶部x轴上的文本
axis.ticks.y.right=element_blank(), # 删除右侧y轴刻度
axis.text.y.right=element_blank(), # 删除右侧y轴文本
axis.ticks.x.top=element_blank(), # 删除顶部x轴刻度
panel.spacing.x=unit(0, "cm"), # 删除x轴间距
panel.border=element_blank(), # 删除面板边框
legend.position="right", # 删除图例
panel.spacing=unit(0, "lines")
) + # 删除面板之间的间距
guides(x.sec="axis", y.sec="axis")
image.png
分面箱线图加点图
set.seed(123)
fm <- matrix(rnorm(100), nrow=200,ncol = 8) %>% as.data.frame()
colnames(fm) <- c(paste0("Gene_", 1:8))
rownames(fm) <- paste0("Sample_", 1:200)
fm$alt_subtype <- rep(c('type1','type2','type3','type4'),time=c(50,50,50,50))
fm$group <- rep(c('group1','group2'),time=c(100,100))
fm$alt_subtype %>% unique()
col <- c("#5CB85C", "#337AB7", "#F0AD4E", "#D9534F")
comparisons <- list(c("type1", "type2"),
c("type1", "type3"),
c("type1", "type4"),
c("type2", "type3"),
c("type2", "type4"),
c("type3", "type4"))
plist <- list() # 创建一个空的列表 'plist'，用于存储后续循环中生成的图形
colnames(fm)
for (i in 1:8) {
bar_tmp <- fm[, c(colnames(fm)[i], "alt_subtype")] # 从 'Exp_plot' 中提取当前基因的表达信息和样本组
colnames(bar_tmp) <- c("Expression", "group")
pb1 <- ggboxplot(bar_tmp, # Create a boxplot using ggboxplot.
x = "group", # X-axis is for groups.
y = "Expression", # Y-axis is for expression levels.
color = "group", # Fill by sample group.
fill = NULL,
add = "jitter", # Add jitter points.
bxp.errorbar.width = 0.8,
width = 0.5,
size = 0.1,
font.label = list(size = 20),
palette = col) +
theme(panel.background = element_blank())
pb1 <- pb1 + theme(axis.line = element_line(colour = "black")) +
theme(axis.title.x = element_blank()) # 调整坐标轴
pb1 <- pb1 + theme(axis.title.y = element_blank()) +
theme(axis.text.x = element_text(size = 15, angle = 45, vjust = 1, hjust = 1))
pb1 <- pb1 + theme(axis.text.y = element_text(size = 15)) +
ggtitle(colnames(fm)[i]) +
theme(plot.title = element_text(hjust = 0.5, size = 15, face = "bold"))
pb1 <- pb1 + theme(legend.position = "NA") # 删除图例（因为样本类型已经显示在横轴上）
pb1 <- pb1 + stat_compare_means(method = "t.test", hide.ns = FALSE,
comparisons = comparisons,label = "p.signif",vjust=0.02,bracket.size=0.6) # 执行显著性测试，使用 t 检验，并添加不同组别之间的比较
plist[[i]] <- pb1 # 将生成的图形存储在 'plist' 中
}
#Align and arrange the plots into a grid.
p = plot_grid(plist[[1]], plist[[2]], plist[[3]],
plist[[4]], plist[[5]], plist[[6]],
plist[[7]], plist[[8]],ncol = 4)
image.png
分面箱线图加点图2
p=ggplot(df, # 用来画图的数据集名称
aes(Species, # 数据集的Species列作为横坐标
value, # 数据集的value列作为纵坐标
color = Species, # 根据数据集的Species列设置箱线图的边框颜色
fill = Species))+ # 根据数据集的Species列设置散点图和箱线图的填充颜色
geom_jitter(width=.15)+ # 将散点图的宽度设置为0.15,缩窄一些
geom_boxplot(width=.4, alpha=.2)+ # 将箱线图的宽度设置为0.4，缩窄了一些，不透明度设置为0.2，填充颜色变得很浅
scale_color_manual(values = c("#BF5960","#6F99AD","#F9A363"))+ # 自定义边框颜色
scale_fill_manual(values = c("#BF5960","#6F99AD","#F9A363"))+ # 自定义填充颜色，可以和边框颜色不一样，我觉得配套的比较好看，就用了一样的颜色
theme_test()+ # 把背景颜色去掉并添加边框，可以修改为其他背景，比如theme_classic
labs(y="Length of petal", x="")+ # 修改横纵坐标名称
stat_compare_means(comparisons = list, method = "wilcox.test", #像这种非配对的非正态分布的数据，可以用非参数检验方法，这里选的是非参的其中一种，叫秩和检验
size=4, # 将p值字体改小一些
label = "p.signif")+ # 把p值改为星号
scale_y_continuous(expand = c(0.1,0.1))+ # 将y轴上下拉宽一点，这样被遮住的p值就可以呈现出来啦~
facet_wrap(.~variable, # 同时做几个图，这几个图分别对应数据转换前的第1列到第4列
nrow=2, ncol=2, # 图排成两行两列
scale="free")+ # 横纵坐标不要统一成一样的，各个图根据自己情况来自动设置横纵坐标的范围
theme(strip.background = element_blank(), #把头上的灰框去掉
axis.text = element_text(color="black", size=10),
axis.line = element_blank())
image.png
柱状图加点图+显著性
library(ggh4x)
set.seed(123)
dfb <- iris[,c(1,5)]
dfb %>%
pivot_longer(!Species) %>%
select(Species,value) %>%
na.omit() -> dfb.1
library(ggplot2)
ebtop<-function(x){
return(mean(x)+sd(x))
}
ebbottom<-function(x){
return(mean(x)-sd(x))
}
ggplot(data=dfb.1,aes(x=Species,y=value))+
stat_summary(geom = "bar",
fun = mean,
fill="#c6c3c3")+
stat_summary(geom = "errorbar",
fun.min = ebbottom,
fun.max = ebtop,
width=0.2)+
geom_jitter(width = 0.3)+
geom_signif(comparisons = list(c('setosa','versicolor'),
c('versicolor','virginica'),
c('setosa','virginica')
),
test = t.test,
test.args = list(var.equal=T,
alternative="two.side"),
y_position = c(8,8.5,9.2),#自助修改
annotations = c(""),
parse = T)+
annotate(geom = "text",
x=1.5,y=8.65,
label=expression(italic(P)~'='~2.22%*%10^-16))+
annotate(geom = "text",
x=2.5,y=9.15,
label=expression(italic(P)~'='~1.7%*%10^-7))+
annotate(geom = "text",
x=2,y=9.8,
label=expression(italic(P)~'='~2.22%*%10^-16))+
scale_y_continuous(expand = c(0,0),
limits = c(0,10),
breaks = seq(0,10,2))+ # 将箱线图的宽度设置为0.4，缩窄了一些，不透明度设置为0.2，填充颜色变得很浅
theme_minimal()+
theme(panel.grid = element_blank(),
axis.line.y = element_line(),
axis.ticks.y = element_line(),
axis.title.y = element_text(hjust=8,
size=15),
axis.text.x = element_text(angle = 30,
hjust = 1,
size=10))+
guides(y=guide_axis_truncated(trunc_lower = 0,
trunc_upper = 10))+
labs(x=NULL,y="Survival Rate")
image.png
箱线图加点图
#像这种多组之间两两比较，需要提前设置一个list，里面包含的是两两比较的对象
list = list(c("setosa","versicolor"),c("virginica","versicolor"),c("setosa","virginica"))
把数据转成纵性数据
df=reshape2::melt(iris, # 需要转换的数据
id.vars=5, # 第5列Species不动
measure.vars=1:4) # 第1列到第4列全部转换成纵性数据
ggplot(df, # 用来画图的数据集名称
aes(Species, # 数据集的Species列作为横坐标
value, # 数据集的value列作为纵坐标
color = Species, # 根据数据集的Species列设置箱线图的边框颜色
fill = Species))+ # 根据数据集的Species列设置散点图和箱线图的填充颜色
geom_jitter(width=.15)+ # 将散点图的宽度设置为0.15,缩窄一些
geom_boxplot(width=.4, alpha=.2)+ # 将箱线图的宽度设置为0.4，缩窄了一些，不透明度设置为0.2，填充颜色变得很浅
scale_color_manual(values = c("#BF5960","#6F99AD","#F9A363"))+ # 自定义边框颜色
scale_fill_manual(values = c("#BF5960","#6F99AD","#F9A363"))+ # 自定义填充颜色，可以和边框颜色不一样，我觉得配套的比较好看，就用了一样的颜色
theme_test()+ # 把背景颜色去掉并添加边框，可以修改为其他背景，比如theme_classic
labs(y="Length of petal", x="")+ # 修改横纵坐标名称
stat_compare_means(comparisons = list, method = "wilcox.test", #像这种非配对的非正态分布的数据，可以用非参数检验方法，这里选的是非参的其中一种，叫秩和检验
size=4, # 将p值字体改小一些
label = "p.signif")+ # 把p值改为星号
scale_y_continuous(expand = c(0.1,0.1))+ # 将y轴上下拉宽一点，这样被遮住的p值就可以呈现出来啦~
facet_wrap(.~variable, # 同时做几个图，这几个图分别对应数据转换前的第1列到第4列
nrow=2, ncol=2, # 图排成两行两列
scale="free")+ # 横纵坐标不要统一成一样的，各个图根据自己情况来自动设置横纵坐标的范围
theme(strip.background = element_blank(), #把头上的灰框去掉
axis.text = element_text(color="black", size=10),
axis.line = element_blank()) # 把横纵坐标的字体颜色改为黑色
image.png
箱线图加连接线
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
set.seed(123)
df <- iris[,c(1,5)] %>% subset(Species %in% c('setosa','versicolor'))
df$pair=rep(paste0('pair',seq(1:50)),time=2)
df <- set_colnames(df,c('Expression','Group1','Group2'))
ggplot(df,aes(x=Group1,y=Expression,color=Group1))+
geom_boxplot(aes(color=Group1),width=0.8,lwd=1,outlier.shape=NA)+#箱线图
scale_color_manual(limits=c("setosa","versicolor"),values=c("#993365","#666632"))+ #设置颜色
geom_jitter(size=3.5,shape=21,alpha=0.8, #加散点，设置散点大小、形状、透明度
aes(fill=Group2),
position=position_dodge(0.5))+
#scale_fill_brewer(palette='Set3')+
stat_compare_means(method="t.test",paired=TRUE,#配对t检验
comparisons=list(c("setosa","versicolor")))+##按分组进行统计检验
geom_line(aes(group=Group2),
color='grey40',lwd=0.6,#线条粗细
position=position_dodge(0.5))+
labs(x='',y='Expression',title='',subtitle='')+
##以下是ggplot2的主题设置，修改边框、背景、标题、字体等
theme_classic()+#主题设置
theme(axis.line=element_line(colour='black',size=1),#坐标轴线条颜色、粗细
axis.text=element_text(size=14,color='black'),#坐标轴字体大小、颜色
axis.text.x=element_text(angle=45,hjust=1),legend.position = '',
axis.title=element_text(size=20,color='black'))
image.png
单因素方差箱线图
library(tidyverse)
library(ggtext)
library(ggprism)
library(ggsignif)
library(rstatix)
library(ggpubr)
df <- iris %>% pivot_longer(-Species) %>%
dplyr::filter(Species=="setosa") %>% select(-Species)
result.aov <- aov(value ~ name, data = df)
result.tukey <- TukeyHSD(result.aov)
#$name
diff lwr upr p adj
Petal.Width-Petal.Length -1.216 -1.360087 -1.071913 0
Sepal.Length-Petal.Length 3.544 3.399913 3.688087 0
Sepal.Width-Petal.Length 1.966 1.821913 2.110087 0
Sepal.Length-Petal.Width 4.760 4.615913 4.904087 0
Sepal.Width-Petal.Width 3.182 3.037913 3.326087 0
Sepal.Width-Sepal.Length -1.578 -1.722087 -1.433913 0
aov_pvalue <- result.tukey$name %>% as.data.frame() %>%
rownames_to_column(var="group") %>%
dplyr::select(1,`p adj`) %>%
separate(`group`, into=c("group2", "group1"), sep="-", convert = TRUE) %>%
select(2,1,3) %>%
select(-1,-2) %>%
mutate(p_signif=symnum(`p adj`, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.01, 0.05,1),
symbols = c("**", "*", "ns")))
df_pvalue <- df %>%
wilcox_test(value ~ name) %>%
add_significance(p.col="p.adj") %>%
add_xy_position(x="name") %>% select(-p.adj) %>%
bind_cols(aov_pvalue)
df %>%
ggplot(aes(name,value))+
stat_summary(fun = mean,geom = "errorbar", width=.2,
fun.max = function(x) mean(x) + sd(x),
fun.min = function(x) mean(x) - sd(x)) +
stat_summary(fun = mean, geom = "crossbar",width = 0.4,color = "black",size=0.5) +
geom_jitter(aes(fill=name,color=name,shape=name),width = 0.1, height = 0)+
stat_pvalue_manual(df_pvalue,label="p_signif",label.size=5,hide.ns=T)+
scale_shape_manual(values = c(21,22,23,24)) +
scale_fill_manual(values=c("#679289","#ee2e31","#c9cba3","#f4c095"))+
scale_color_manual(values=c("#679289","#ee2e31","#c9cba3","#f4c095"))+
scale_y_continuous(guide = "prism_minor",
limits = c(0, 10),
expand = c(0, 0))+
labs(x=NULL,y="setosa (10<sup>3</sup>/g)")+
theme_prism()+
theme(strip.background = element_blank(),
legend.background = element_rect(color=NA),
legend.key = element_blank(),
legend.spacing.x = unit(-0.09,"in"),
legend.spacing.y = unit(-0.09,"in"),
legend.text = element_text(color="black",size=6,face="bold"),
legend.position = "top",
axis.text.x=element_blank(),
axis.text.y=element_text(color="black",size=8),
axis.title.y = element_markdown(color="black",face="bold",size=10),
axis.ticks.x = element_blank())+
guides(shape = guide_legend(override.aes = list(size=3),
direction = "horizontal",
nrow=3, byrow=TRUE))
image.png
分组箱线图加显著性
dd2 <-iris %>%
pivot_longer(cols=1:4,
names_to= "Immune",
values_to = "Fraction")
ggplot(dd2,aes(x=Immune, y=Fraction,
fill = Species, color = Species)) +
geom_boxplot(notch = F, alpha = 0.95,
outlier.shape = 16,
outlier.colour = "black", #outlier点用黑色
outlier.size = 0.65) +
#自定义配色
scale_fill_manual(values= c('#CD534C','#0073C2','#EFC000')) +
theme_classic() +
theme(axis.text.x = element_text(angle = 60, hjust = 1,size = 10),
axis.text.y = element_text(angle = 90, size = 12),
axis.title.y = element_text(angle = 90, size = 15)) +
theme(legend.position = "top")+
stat_compare_means(symnum.args=list(cutpoints = c(0, 0.001, 0.01, 0.05, 1),
symbols = c("***", "**", "*", "ns")),
label = "p.signif")
image.png
单个因素小提琴图加显著性
ggplot(data = iris[,c(1,5)]) +
geom_boxplot(mapping = aes(x = Species, y = Sepal.Length, colour = Species), # 箱线图的映射
#alpha = 0.5, # 箱线图的透明度
size = 1.5, # 箱线图边界的粗细
width = 0.6) + # 箱线图的宽度
geom_jitter(mapping = aes(x = Species, y = Sepal.Length, colour =Species), # 散点图的映射
size = 1.5,alpha = 0.3) + # 散点的大小和透明度
scale_color_manual(limits = c("setosa", "versicolor", "virginica"), # 手动设置颜色的范围
values = c("#85B22E", "#5F80B4", "#E29827")) + # 不同样本的颜色
geom_signif(mapping = aes(x = Species, y = Sepal.Length), # 显著性线的映射
comparisons = list(c("setosa", "versicolor"), c("setosa", "virginica"), c("versicolor", "virginica")), # 两两比较
map_signif_level = TRUE, # 映射显著性水平
tip_length = c(0, 0, 0), # 显著性线的长度
y_position = c(15, 15.8, 16.6), # 显著性线的纵坐标位置
size = 1, # 显著性线的粗细
textsize = 4, # 显著性标记的大小
test = "t.test") + # 统计检验的类型
theme_classic(base_line_size = 1) + # 经典主题，轴线较粗
labs(title = "Gene Expression by Sepal.Length", x = "Species", y = "Expression level") + # 设置标题和轴标签
theme(plot.title = element_text(size = 15, colour = "black", hjust = 0.5), # 标题的外观
axis.title.y = element_text(size = 15, color = "black", face = "bold", vjust = 1.9, hjust = 0.5, angle = 90), # 纵坐标轴标题的外观
legend.title = element_text(color = "black", size = 15, face = "bold"), # 图例标题的外观
legend.text = element_text(color = "black", size = 10, face = "bold"), # 图例标签的外观
axis.text.x = element_text(size = 13, color = "black", face = "bold", vjust = 0.5, hjust = 0.5, angle = 0), # 横坐标轴标签的外观
axis.text.y = element_text(size = 13, color = "black", face = "bold", vjust = 0.5, hjust = 0.5, angle = 0)) # 纵坐标轴标签的外观
image.png
小提琴和柱状图
ggplot(iris[,c(1,5)], aes(x=Species,y=Sepal.Length))+
geom_violin(aes(fill = Species,color=Species), trim = FALSE)+
geom_boxplot(aes(fill=Species),notch = F,width=0.3)+
labs(x="", y="Sequencing depth")+
theme_bw(base_line_size = 1.05,base_rect_size = 1.05)+
scale_fill_manual(values=c("#3EB185","#CCE3FA","#3F88C5","#F3AC66","#DD8CBC"))+
scale_color_manual(values=c("#3EB185","#CCE3FA","#3F88C5","#F3AC66","#DD8CBC"))+
annotate('text', label = 'Bacteria', x =3.5, y =8, angle=0, size =5,color="#DC9E05")+
annotate("rect", xmin = 0, xmax =4, ymin = 2.5, ymax = 9, alpha = 0.1,fill="#FAE3AD") +#"#CCE3FA"
theme(axis.text=element_text(colour='black',size=10))+
scale_y_continuous(expand = c(0, 0), limit = c(2.5, 9))
image.png
蜜蜂图加两两显著性分析
library(tidyverse)
library(ggbeeswarm)
library(scales)
library(ggfun)
set.seed(1115)
df <- tibble(
`Day 0-Old` = abs(rnorm(100, mean = 10, sd = 10)),
`Day 0-Young` = abs(rnorm(100, mean = 30, sd = 30)),
`Day 21-Old` = abs(rnorm(100, mean = 60, sd = 60)),
`Day 21-Young` = abs(rnorm(100, mean = 500, sd = 500)),
`Day 35-Old` = abs(rnorm(100, mean = 1000, sd = 1000)),
`Day 35-Young` = abs(rnorm(100, mean = 5000, sd = 5000))
)
# data cleaning
df_clean <- df %>%
tidyr::gather(key = "key", value = "value") %>%
dplyr::mutate(group = str_split(string = key, pattern="-", simplify = T)[,2])
# data statistics
df_clean_mean <- df_clean %>%
group_by(key) %>%
summarise(mean = mean(value),
mean_scale = log10(mean))
df_clean_mean
# data signigicant
table(df_clean$key)
signif_out <- c()
for (i in c("Day 0","Day 21","Day 35")) {
out <- t.test(df_clean %>% dplyr::filter(key == paste(i,"Old",sep = "-")) %>% pull(value),
df_clean %>% dplyr::filter(key == paste(i,"Young",sep = "-")) %>% pull(value))
signif_out <- c(signif_out, out$p.value)
}
# scale_y_log10
breaks_log10 <- function(x) {
low <- floor(log10(min(x)))
high <- ceiling(log10(max(x)))
10^(seq.int(low, high))
}
ggplot(data = df_clean) +
geom_quasirandom(aes(x = key, y = value, shape = group, fill = group),
method = "pseudorandom", size = 3, alpha = 0.7) +
scale_shape_manual(values = c(21, 22)) +
scale_fill_manual(values = c("#80b1d3", "#fdb462")) +
annotate(geom = "segment", x = 0.6, xend = 1.4, y = 11.4, yend = 11.4, linewidth = 1) +
annotate(geom = "segment", x = 1.6, xend = 2.4, y = 34.8, yend = 34.8, linewidth = 1) +
annotate(geom = "segment", x = 2.6, xend = 3.4, y = 68.4, yend = 68.4, linewidth = 1) +
annotate(geom = "segment", x = 3.6, xend = 4.4, y = 529, yend = 529, linewidth = 1) +
annotate(geom = "segment", x = 4.6, xend = 5.4, y = 1179, yend = 1179, linewidth = 1) +
annotate(geom = "segment", x = 5.6, xend = 6.4, y = 6217, yend = 6217, linewidth = 1) +
scale_y_log10(breaks = breaks_log10,
labels = trans_format(log10, math_format(10^.x))) +
annotation_logticks(sides = "l", outside = TRUE) +
coord_cartesian(clip = "off") +
scale_x_discrete(labels = c("Day 0", "Day 0", "Day 21", "Day 21", "Day 35", "Day 35")) +
labs(x = "Sample", y = "Data") +
geom_hline(yintercept = 100, linetype = "dashed") +
# Day 0
annotate(geom = "segment", x = 1, xend = 2, y = 500, yend = 500) +
annotate(geom = "segment", x = 1, xend = 1, y = 500, yend = 300) +
annotate(geom = "segment", x = 2, xend = 2, y = 500, yend = 300) +
annotate(geom = "text", x = 1.5, y = 800, label = bquote(italic("p")~"< 0.01"), size = 5) +
# Day 21
annotate(geom = "segment", x = 3, xend = 4, y = 10000, yend = 10000) +
annotate(geom = "segment", x = 3, xend = 3, y = 10000, yend = 2000) +
annotate(geom = "segment", x = 4, xend = 4, y = 10000, yend = 8000) +
annotate(geom = "text", x = 3.5, y = 15000, label = bquote(italic("p")~"< 0.01"), size = 5) +
# Day 35
annotate(geom = "segment", x = 5, xend = 6, y = 80000, yend = 80000) +
annotate(geom = "segment", x = 5, xend = 5, y = 80000, yend = 10000) +
annotate(geom = "segment", x = 6, xend = 6, y = 80000, yend = 60000) +
annotate(geom = "text", x = 5.5, y = 120000, label = bquote(italic("p")~"< 0.01"), size = 5) +
theme_classic() +
theme(axis.text = element_text(size = 15),
axis.text.y.left = element_text(margin = margin(r = 10)),
legend.background = element_roundrect(color = "#808080", linetype = 1))
image.png
云雨图
library(ggplot2) #绘图
library(ggsignif) #添加统计检验
library(ggdist) #云雨图
iris$Species
Vec1 <- c("setosa", "versicolor", "virginica")
comb_list <- list()
for(i in 1:(length(Vec1)-1)) {
for(j in (i+1):length(Vec1)) {
comb <- combn(c(Vec1[i], Vec1[j]), 2)
if(!any(comb[1,] == comb[2,])) {
comb_list[length(comb_list)+1] <- list(comb)
}
}
}
#选择一个颜色运行,如果修改数据，请根据你的分组数量，设置对应数量的颜色
Custom.color <- c("#d3838a","#efa48a","#cbd27e")
Custom.color <- c("#4a9a5b","#8ab522","#d1d628")
Custom.color <- c("#245892","#877eac","#a9d296")
Custom.color <- c("#ae3f51","#d6b55a","#79aec1")
ggplot(iris, aes(x = Species, y = Sepal.Length,fill=Species)) +
geom_jitter(mapping = aes(color=Species),width = .05, alpha = 0.5,size=0.9)+ #绘制散点图
geom_boxplot(position = position_nudge(x = 0.14),width=0.1,outlier.size = 0,outlier.alpha =0)+ #绘制箱线图，并通过position设置偏移
stat_halfeye(mapping = aes(fill=Species),width = 0.2, .width = 0, justification = -1.2, point_colour = NA,alpha=0.6) + #绘制云雨图，并通过position设置偏移
scale_fill_manual(values = Custom.color)+ #映射云雨图和箱线图的颜色
scale_color_manual(values = Custom.color)+ #映射散点的颜色
expand_limits(x = c(0.5, 3.8))+ #扩展画板，若显示不全，请根据你的数据范围手动调整或删除此行
# ylim(11,35)+ #控制y轴显示范围，若显示不全，请根据你的数据范围手动调整或删除此行
xlab("Histological origins of cancer cells") + #设置X轴标题
ylab("Degree of cellular infiltration") + #设置Y轴标题
ggtitle("A visual case")+ #设置主标题
theme(axis.ticks.x = element_line(size = 0,color = "black"), #自定义主题
panel.background = element_rect(fill = "white", color = "white"), #设置画板
panel.grid.major.x = element_blank(), #设置网格
panel.grid.minor.x = element_blank(), #设置网格
panel.grid.major.y = element_line(color = "gray", size = 0.25), #设置网格
panel.grid.minor.y = element_blank(), #设置网格
panel.border = element_rect(color = "black", fill = NA,linewidth = 1), #设置边框
legend.position = "none", #隐藏图例
axis.title.x = element_text(size = 13), #调整X轴标题字体大小
axis.title.y = element_text(size = 13), #调整Y轴标题字体大小
axis.text.x = element_text(size = 12,hjust = 0.3), #设置x轴刻度字体偏移，若更换数据，可能需要重新设置
axis.text.y = element_text(size = 12), #设置Y轴刻度字体大小
plot.title = element_text(hjust = 0.5)
)+
geom_signif(comparisons = comb_list,step_increase = .1,map_signif_level = TRUE,vjust = 0.5,hjust= 0)
image.png
