R 实用｜替换多变量方差分析（以 PCoA 的 PERMANOVA 分析为例）
最编程
2024-04-17 18:32:53
...
# 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)
#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$eig
#为样本点坐标添加分组信息
plot_data <- merge(plot_data, group, by = 'ID', all.x = TRUE)
head(plot_data)
# 计算加权bray-curtis距离
dune_dist <- vegdist(otu, method="bray", binary=F)
dune_pcoa <- cmdscale(dune_dist, k=(nrow(otu) - 1), eig=T)
dune_pcoa_points <- as.data.frame(dune_pcoa$points)
sum_eig <- sum(dune_pcoa$eig)
eig_percent <- round(dune_pcoa$eig/sum_eig*100,1)
colnames(dune_pcoa_points) <- paste0("PCoA", 1:3)
dune_pcoa_result <- cbind(dune_pcoa_points, group)
head(dune_pcoa_result)
library(ggplot2)
ggplot(dune_pcoa_result, aes(x=PCoA1, y=PCoA2, fill=group)) +
geom_point(shape = 21,color = 'black',size=4) +
stat_ellipse(level=0.95)+
scale_fill_manual(values = c('#73bbaf','#d15b64','#592c93'))+
labs(x=paste("PCoA 1 (", eig_percent[1], "%)", sep=""),
y=paste("PCoA 2 (", eig_percent[2], "%)", sep="")) +
theme_classic()
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