跟着 Cell 学作图 | 主坐标分析(PCoA)及其可视化（vegan）

pcoa.jpg

Title：Targeted suppression of human IBD-associated gut microbiota commensals by phage consortia for treatment of intestinal inflammation

DOI：10.1016/j.cell.2022.07.003

22

本期图片

Snipaste_2022-09-27_00-52-33.png

Principal coordinate analyses (PCoA), Bray-Curtis dissimilarity, colored according to (E) disease or (F) Kp abundance

原文详见：https://mp.weixin.qq.com/s/uVBypI7bDS17LCrK80Vesw

# Load package
library(vegan)
library(ggplot2)
library(ggthemes)
# Load data
otu <- read.table('otu.txt',row.names = 1,header = T)
group <- read.table('group.txt',header = T)
# creat data
group$bacteria <- runif(55,0,20)
#pcoa
# vegdist函数，计算距离；method参数，选择距离类型
distance <- vegdist(otu, method = 'bray')
# 对加权距离进行PCoA分析
pcoa <- cmdscale(distance, k = (nrow(otu) - 1), eig = TRUE)

## plot data
# 提取样本点坐标
plot_data <- data.frame({pcoa$point})[1:2]

# 提取列名，便于后面操作。
plot_data$ID <- rownames(plot_data)
names(plot_data)[1:2] <- c('PCoA1', 'PCoA2')

# eig记录了PCoA排序结果中，主要排序轴的特征值（再除以特征值总和就是各轴的解释量）
eig = pcoa$eig

#为样本点坐标添加分组信息
plot_data <- merge(plot_data, group, by = 'ID', all.x = TRUE)
head(plot_data)

# figure1
ggplot(data = plot_data, aes(x=PCoA1, y=PCoA2, fill=group)) +
  geom_point(shape = 21,color = 'black',size=4) +
  scale_fill_manual(values = c('#73bbaf','#d15b64','#592c93'))+
  labs(x=paste("PCoA 1 (", format(100 * eig[1] / sum(eig), digits=4), "%)", sep=""),
       y=paste("PCoA 2 (", format(100 * eig[2] / sum(eig), digits=4), "%)", sep=""))+
  geom_hline(yintercept=0, linetype=4) +    
  geom_vline(xintercept=0 ,linetype=4)+          
  theme_few()+
  theme(legend.position = c(0.9, 0.2),
        legend.title = element_blank(),
        legend.background = element_rect(colour ="black"))
ggsave('pcoa1.pdf',width = 4,height = 4)

# figure2
ggplot(data = plot_data, aes(x=PCoA1, y=PCoA2, fill=bacteria)) +
  geom_point(shape = 21,color = 'black',size=4) +
  scale_fill_gradient(low = '#f2fe32',high = '#180f7c')+
  labs(x=paste("PCoA 1 (", format(100 * eig[1] / sum(eig), digits=4), "%)", sep=""),
       y=paste("PCoA 2 (", format(100 * eig[2] / sum(eig), digits=4), "%)", sep=""))+
  geom_hline(yintercept=0, linetype=4) +    
  geom_vline(xintercept=0 ,linetype=4)+          
  theme_few()+
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.15),
        legend.direction = "horizontal")
ggsave('pcoa2.pdf',width = 4.5,height = 4)
        

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往期内容

1. 即将满员！CNS图表复现|生信分析|R绘图 资源分享&讨论群！(内附推文合集)
2. 跟着 Nature Communication 学作图 | 热图+格子注释(通路富集相关)
3. 跟着 Nature Communication 学作图 | 百分比堆积柱状图+卡方检验
4. ggbiplot | 带箭头的主成分分析(PCA)图绘制

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