[R构建函数基础篇] 计算多样性指数及绘图

本节来介绍如何R来自定义构建函数来进行多样性指数计算及绘图

加载R包

library(tidyverse)
library(vegan)
library(rstatix)
library(ggpubr)
library(magrittr)

导入数据

# otu数据表转置，行为样本，列为OTU编号
alpha <- read.delim("otu_taxa_table-2.xls",sep="\t",row.names = 1) %>% 
  t() %>% as.data.frame()

# 分组文件
group <- read_tsv("group.xls") %>% set_colnames(c("sample","group"))

定义函数计算多样性指数

alpha_diversity <- function(x,y) {
  Shannon <- diversity(x, index = 'shannon')
  Simpson <- diversity(x, index = 'simpson')  
  observed_species <- specnumber(x)
  Chao1 <- estimateR(x)[2,]
  ACE <- estimateR(x)[4,]
  pielou <- diversity(x,index = "shannon")/log(specnumber(x),exp(1))
  
  result <- data.frame(Shannon,Simpson,observed_species,Chao1,ACE,pielou) %>% 
    rownames_to_column("sample") %>% 
    left_join(.,y,by="sample")
    
  return(result)
  
}

导出指数计算结果

alpha_diversity(alpha,group) %>% write.table(file="alpah.xls",sep="\t",quote = F,row.names = F)

   sample  Shannon   Simpson observed_species    Chao1      ACE    pielou group
1      A1 6.022632 0.9898521             2061 2676.254 2715.836 0.7892378     A
2      A2 5.197605 0.9761031             1516 2181.152 2212.486 0.7096840     A
3      A3 5.199001 0.9763631             1533 2129.035 2184.918 0.7087953     A
4      A4 5.347510 0.9810318             1506 1995.025 1997.526 0.7308124     A
5      A5 5.395820 0.9857777             1362 1734.167 1765.997 0.7476843     A
6      B1 5.546399 0.9888982             1445 1917.961 1887.453 0.7623010     B
7      B2 5.541260 0.9889996             1302 1612.939 1602.964 0.7726610     B
8      B3 5.892619 0.9913167             1844 2268.215 2309.436 0.7836250     B
9      B4 6.130304 0.9940395             1852 2143.778 2156.409 0.8147643     B
10     B5 5.598551 0.9902785             1393 1854.570 1786.834 0.7733644     B
11     C1 4.255467 0.9194131             1241 1574.346 1627.599 0.5973698     C

整理绘图文件

在此选取我们需要的数据来进行展示

df <- alpha_diversity(alpha,group) %>% select(-sample,-observed_species,-Simpson) %>% 
  pivot_longer(-group)

# 先给定一些颜色
col <- c("#1F78B4","#33A02C","#FB9A99","#E31A1C","#FDBF6F","#B2DF8A",
         "#A6CEE3","#BA7A70","#9D4E3F","#829BAB")

定义绘图函数

make_plot <- function(data,x,y,z){
  ggplot(data,aes(x={{x}},y={{y}},fill={{x}}))+
  geom_violin(trim=F)+
    stat_boxplot(geom="errorbar",position=position_dodge(width=0.2),width=0.1)+
    geom_boxplot(position=position_dodge(width =0.8),width=0.1,fill="white")+
    scale_fill_manual(values={{z}})+
    facet_wrap(.~name,scales = "free")+
    theme_bw()+
    theme(panel.spacing.x = unit(0.2,"cm"),
          panel.spacing.y = unit(0.1, "cm"),
          axis.title = element_blank(),
          strip.text.x = element_text(size=12,color="black"),
          axis.text = element_text(color="black"),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank(),
          legend.position = "non",
          plot.margin=unit(c(0.3,0.3,0.3,0.3),units=,"cm"))
}

数据可视化

make_plot(df,group,value,col)

数据获取

可以看到通过自定义两个函数后，续如果再有类似的需求可以直接调用非常的方便；当然此图还可以在上面添加方差分析显著性标记，这个以后在做介绍；需要数据的请评论区留言