作者：taoyan
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#先加載包
library(ggpubr)
#加載數(shù)據(jù)集ToothGrowth
data("ToothGrowth")
head(ToothGrowth)

##    len  supp  dose
## 1  4.2   VC   0.5
## 2  11.5  VC   0.5
## 3  7.3   VC   0.5
## 4  5.8   VC   0.5
## 5  6.4   VC   0.5
## 6  10.0  VC   0.5

比較方法

R中常用的比較方法主要有下面幾種：

各種比較方法后續(xù)有時(shí)間一一講解。

添加p-value

主要利用ggpubr包中的兩個(gè)函數(shù)：

• compare_means():可以進(jìn)行一組或多組間的比較
• stat_compare_mean():自動(dòng)添加p-value媒咳、顯著性標(biāo)記到ggplot圖中

compare_means()函數(shù)

該函數(shù)主要用用法如下：

compare_means(formula, data, method = "wilcox.test", paired = FALSE,
  group.by = NULL, ref.group = NULL, ...)

注釋：

• formula:形如x~group，其中x是數(shù)值型變量，group是因子草丧，可以是一個(gè)或者多個(gè)
• data：數(shù)據(jù)集
• method:比較的方法，默認(rèn)為"wilcox.test", 其他可選方法為："t.test"莹桅、"anova"昌执、"kruskal.test"
• paired:是否要進(jìn)行paired test(TRUE or FALSE)
• group_by: 比較時(shí)是否要進(jìn)行分組
• ref.group: 是否需要指定參考組

stat_compare_means()函數(shù)

主要用法：

stat_compare_means(mapping = NULL, comparisons = NULL hide.ns = FALSE,
                   label = NULL,  label.x = NULL, label.y = NULL,  ...)

注釋：

• mapping:由aes()創(chuàng)建的一套美學(xué)映射
• comparisons:指定需要進(jìn)行比較以及添加p-value、顯著性標(biāo)記的組
• hide.ns:是否要顯示顯著性標(biāo)記ns
• label:顯著性標(biāo)記的類型诈泼，可選項(xiàng)為：p.signif(顯著性標(biāo)記)懂拾、p.format(顯示p-value)
• label.x、label.y:顯著性標(biāo)簽調(diào)整
• ...:其他參數(shù)

比較獨(dú)立的兩組

compare_means(len~supp, data=ToothGrowth)

image

結(jié)果解釋：

• .y:測(cè)試中使用的y變量
• p:p-value
• p.adj:調(diào)整后的p-value铐达。默認(rèn)為p.adjust.method="holm"
• p.format:四舍五入后的p-value
• p.signif:顯著性水平
• method:用于統(tǒng)計(jì)檢驗(yàn)的方法

繪制箱線圖

p <- ggboxplot(ToothGrowth, x="supp", y="len", color = "supp", 
palette = "jco", add = "jitter")#添加p-valuep+stat_compare_means()

image

#使用其他統(tǒng)計(jì)檢驗(yàn)方法
p+stat_compare_means(method = "t.test")

image

上述顯著性標(biāo)記可以通過(guò)label.x岖赋、label.y、hjust及vjust來(lái)調(diào)整
顯著性標(biāo)記可以通過(guò)aes()映射來(lái)更改：

• aes(label=..p.format..)或aes(lebel=paste0("p=",..p.format..)):只顯示p-value瓮孙，不顯示統(tǒng)計(jì)檢驗(yàn)方法
• aes(label=..p.signif..):僅顯示顯著性水平
• aes(label=paste0(..method..,"\n", "p=",..p.format..)):p-value與顯著性水平分行顯示

舉個(gè)栗子：

p+stat_compare_means(aes(label=..p.signif..), label.x = 1.5, label.y = 40)

image

也可以將標(biāo)簽指定為字符向量贾节，不要映射，只需將p.signif兩端的..去掉即可

p+stat_compare_means(label = "p.signif", label.x = 1.5, label.y = 40)

image

比較兩個(gè)paired sample

compare_means(len~supp, data=ToothGrowth, paired = TRUE)

image

利用ggpaired()進(jìn)行可視化

ggpaired(ToothGrowth, x="supp", y="len", color = "supp", line.color = "gray", 
line.size = 0.4, palette = "jco")+ stat_compare_means(paired = TRUE)

image

多組比較

Global test

compare_means(len~dose, data=ToothGrowth, method = "anova")

image

可視化

ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+
stat_compare_means()

image

#使用其他的方法
ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+ 
stat_compare_means(method = "anova")

image

Pairwise comparisons:如果分組變量中包含兩個(gè)以上的水平衷畦，那么會(huì)自動(dòng)進(jìn)行pairwise test,默認(rèn)方法為"wilcox.test"

compare_means(len~dose, data=ToothGrowth)

image

#可以指定比較哪些組
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
ggboxplot(ToothGrowth, x="dose", y="len", color = "dose",palette = "jco")+
stat_compare_means(comparisons=my_comparisons)+ # Add pairwise 
comparisons p-value stat_compare_means(label.y = 50) # Add global p-value

image

可以通過(guò)修改參數(shù)label.y來(lái)更改標(biāo)簽的位置

ggboxplot(ToothGrowth, x="dose", y="len", color = "dose",palette = "jco")+
stat_compare_means(comparisons=my_comparisons, label.y = c(29, 35, 40))+ # Add pairwise comparisons p-value 
stat_compare_means(label.y = 45) # Add global p-value

image

至于通過(guò)添加線條來(lái)連接比較的兩組栗涂，這一功能已由包ggsignif實(shí)現(xiàn)

##設(shè)定參考組
compare_means(len~dose, data=ToothGrowth, ref.group = "0.5",  #以dose=0.5組為參考組 
method = "t.test" )

image

#可視化
ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+ 
stat_compare_means(method = "anova", label.y = 40)+ # Add global p-value
stat_compare_means(label = "p.signif", method = "t.test", ref.group = "0.5") # Pairwise comparison against reference

image

參考組也可以設(shè)置為.all.即所有的平均值

compare_means(len~dose, data=ToothGrowth, ref.group = ".all.", method = "t.test")

image

#可視化
ggboxplot(ToothGrowth, x="dose", y="len", color = "dose", palette = "jco")+
stat_compare_means(method = "anova", label.y = 40)+# Add global p-value
stat_compare_means(label = "p.signif", method = "t.test", 
ref.group = ".all.")#Pairwise comparison against all

image

接下來(lái)利用survminer包中的數(shù)據(jù)集myeloma來(lái)講解一下為什么有時(shí)候我們需要將ref.group設(shè)置為.all.

library(survminer)#沒(méi)安裝的先安裝再加載
data("myeloma")
head(myeloma)

image

我們將根據(jù)患者的分組來(lái)繪制DEPDC1基因的表達(dá)譜，看不同組之間是否存在顯著性的差異祈争，我們可以在7組之間進(jìn)行比較斤程，但是這樣的話組間比較的組合就太多了，因此我們可以將7組中每一組與全部平均值進(jìn)行比較，看看DEPDC1基因在不同的組中是否過(guò)表達(dá)還是低表達(dá)忿墅。

compare_means(DEPDC1~molecular_group, data = myeloma, ref.group = ".all.", method = "t.test")

image

#可視化DEPDC1基因表達(dá)譜
ggboxplot(myeloma, x="molecular_group", y="DEPDC1", 
color = "molecular_group", add = "jitter", legend="none")+ 
rotate_x_text(angle = 45)+ 
geom_hline(yintercept = mean(myeloma$DEPDC1), linetype=2)+# Add horizontal line at base mean 
stat_compare_means(method = "anova", label.y = 1600)+ # Add global annova p-value 
stat_compare_means(label = "p.signif", method = "t.test", ref.group = ".all.")# Pairwise comparison against all

image

從圖中可以看出扁藕，DEPDC1基因在Proliferation組中顯著性地過(guò)表達(dá)，而在Hyperdiploid和Low bone disease顯著性地低表達(dá)

我們也可以將非顯著性標(biāo)記ns去掉疚脐，只需要將參數(shù)hide.ns=TRUE

ggboxplot(myeloma, x="molecular_group", y="DEPDC1", 
color = "molecular_group", add = "jitter", legend="none")+
rotate_x_text(angle = 45)+ 
geom_hline(yintercept = mean(myeloma$DEPDC1), linetype=2)+# Add horizontal line at base mean 
stat_compare_means(method = "anova", label.y = 1600)+ # Add global annova p-value 
stat_compare_means(label = "p.signif", method = "t.test", ref.group = ".all.", hide.ns = TRUE)# Pairwise comparison against all

image

多個(gè)分組變量

按另一個(gè)變量進(jìn)行分組之后進(jìn)行統(tǒng)計(jì)檢驗(yàn)亿柑，比如按變量dose進(jìn)行分組：

compare_means(len~supp, data=ToothGrowth, group.by = "dose")

image

#可視化
p <- ggboxplot(ToothGrowth, x="supp", y="len", color = "supp", 
palette = "jco", add = "jitter", facet.by = "dose", short.panel.labs = FALSE)#按dose進(jìn)行分面
#label只繪制
p-valuep+stat_compare_means(label = "p.format")

image

#label繪制顯著性水平
p+stat_compare_means(label = "p.signif", label.x = 1.5)

image

#將所有箱線圖繪制在一個(gè)panel中
p <- ggboxplot(ToothGrowth, x="dose", y="len", color = "supp", 
palette = "jco", add = "jitter")
p+stat_compare_means(aes(group=supp))

image

#只顯示p-value
p+stat_compare_means(aes(group=supp), label = "p.format")

image

#顯示顯著性水平
p+stat_compare_means(aes(group=supp), label = "p.signif")

image

進(jìn)行paired sample檢驗(yàn)
compare_means(len~supp, data=ToothGrowth, group.by = "dose", paired = TRUE)

image

#可視化
p <- ggpaired(ToothGrowth, x="supp", y="len", color = "supp", 
palette = "jco", line.color="gray", line.size=0.4, facet.by = "dose", 
short.panel.labs = FALSE)#按dose分面
#只顯示p-value
p+stat_compare_means(label = "p.format", paired = TRUE)

image

其他圖形

條形圖與線圖（一個(gè)分組變量）

#有誤差棒的條形圖，實(shí)際上我以前的文章里有純粹用ggplot2實(shí)現(xiàn)
ggbarplot(ToothGrowth, x="dose", y="len", add = "mean_se")+ 
stat_compare_means()+ 
stat_compare_means(ref.group = "0.5", label = "p.signif", label.y = c(22, 29))

image

#有誤差棒的線圖
ggline(ToothGrowth, x="dose", y="len", add = "mean_se")+
stat_compare_means()+ 
stat_compare_means(ref.group = "0.5", label = "p.signif", label.y = c(22, 29))

image

條形圖與線圖（兩個(gè)分組變量）

ggbarplot(ToothGrowth, x="dose", y="len", add = "mean_se", color = "supp", 
palette = "jco", position = position_dodge(0.8))+ 
stat_compare_means(aes(group=supp), label = "p.signif", label.y = 29)

image

ggline(ToothGrowth, x="dose", y="len", add = "mean_se", color = "supp", 
palette = "jco")+ 
stat_compare_means(aes(group=supp), label = "p.signif", label.y = c(16, 25, 29))

image

Sessioninfo

sessionInfo()
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936 
## [2] LC_CTYPE=Chinese (Simplified)_China.936 
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C 
## [5] LC_TIME=Chinese (Simplified)_China.936 
## 
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base 
## 
## other attached packages:
## [1] survminer_0.4.0 ggpubr_0.1.3 magrittr_1.5 ggplot2_2.2.1 
## 
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.11 compiler_3.4.0 plyr_1.8.4
## [4] tools_3.4.0 digest_0.6.12 evaluate_0.10 
## [7] tibble_1.3.3 gtable_0.2.0 nlme_3.1-131 
## [10] lattice_0.20-35 rlang_0.1.1 Matrix_1.2-10 
## [13] psych_1.7.5 ggsci_2.4 DBI_0.6-1 
## [16] cmprsk_2.2-7 yaml_2.1.14 parallel_3.4.0 
## [19] gridExtra_2.2.1 dplyr_0.5.0 stringr_1.2.0 
## [22] knitr_1.16 survMisc_0.5.4 rprojroot_1.2 
## [25] grid_3.4.0 data.table_1.10.4 KMsurv_0.1-5 
## [28] R6_2.2.1 km.ci_0.5-2 survival_2.41-3 
## [31] foreign_0.8-68 rmarkdown_1.5 reshape2_1.4.2 
## [34] tidyr_0.6.3 purrr_0.2.2.2 splines_3.4.0 
## [37] backports_1.1.0 scales_0.4.1 htmltools_0.3.6 
## [40] assertthat_0.2.0 mnormt_1.5-5 xtable_1.8-2 
## [43] colorspace_1.3-2 ggsignif_0.2.0 labeling_0.3 
## [46] stringi_1.1.5 lazyeval_0.2.0 munsell_0.4.3 
## [49] broom_0.4.2 zoo_1.8-0