代谢组学和微生物组学关联分析

哪些微生物和代谢物是正相关/负相关的关系，先用Spearman相关系数看一看。

library(psych)
library(pheatmap)
library(RColorBrewer)
my_theme()
# 随机模拟数据
metab <- data.frame(matrix(c(rnorm(20, 0, 1),
                             rnorm(20, 1, 2),
                             rnorm(20, 2, 3),
                             rnorm(20, 4, 3),
                             rnorm(20, 3, 4)),
                           20,5))
colnames(metab) <- paste0("S", seq(1,5))
rownames(metab) <- c(paste0("OTU", seq(1,15)), paste0("metab",seq(1,5)))
cor_mat <- corr.test(t(metab), method = "spearman")
cor_mat_r <- data.frame(cor_mat$r[16:20, 1:15])
cor_mat_p <- data.frame(cor_mat$p[16:20, 1:15])
p_mat <- matrix('', nrow(cor_mat_p), ncol(cor_mat_p))
p_mat[cor_mat_p < 0.05 & cor_mat_p >= 0.01] <- "*"
p_mat[cor_mat_p < 0.01] <- "**"
colnames(p_mat) <- colnames(cor_mat_p)
rownames(p_mat) <- rownames(cor_mat_p)

# 列注释
anno_col <- data.frame(Phylum = factor(rep(c("Proteobacteria", "Bacteriodetes", "Firmicutes"),
                                           c(6,6,3))))
rownames(anno_col) <- colnames(cor_mat_r)

# 行注释
anno_row <- data.frame(MetaClass = factor(rep(c("A","B","C"), c(1,2,2))))
rownames(anno_row) <- rownames(cor_mat_r)

# 颜色
anno_color <- list(Phylum = c(Bacteriodetes = "#e63946", 
                              Firmicutes = "#f1faee", 
                              Proteobacteria = "#a8dadc"),
                   MetaClass = c(A = "#006d77",
                                 B = "#83c5be",
                                 C = "#edf6f9"))
# 出图
pheatmap(cor_mat_r,
         display_numbers = p_mat,
         number_color = "black",
         cluster_cols = T, 
         cluster_rows = T,
         color = colorRampPalette(rev(brewer.pal(n = 7,
                                                 name ="PRGn")))(100),
         annotation_col = anno_col,
         annotation_row = anno_row,
         annotation_colors = anno_color
         )

注： my_theme
function() {
library(ggplot2)
library(ggthemr)
theme_set(theme_classic() +
theme(axis.text = element_text(size = 8, face = "bold"), #坐标轴标签大小，加粗
axis.title = element_text(size = 12, face = "bold"), #坐标轴标题大小，加粗
plot.margin = unit(rep(1, 4), "cm"), #边距，顺序：上右下左
panel.grid = element_blank())) #不要横向和纵向格子线
}

image.png

部分参考自： https://www.sohu.com/a/457132483_120055884