目的
为了展示相同个体不同组学数据，或者不同表型数据之间的相似性关系以及之间的差异性，所以采用了普氏分析。这里展示的是如何将两组数据进行普氏分析并进行可视化
一、导入数据
1 | 微生物种水平丰度表
图一 微生物种水平丰度表
2 | 食物数据的非加权unifrac矩阵
图二 食物数据
# 食物数据
# load the food distance matrix, unweighted unifrac
food_un <- read.delim("data/diet/processed_food/dhydrt_smry_no_soy_beta/unweighted_unifrac_dhydrt.smry.no.soy.txt", row = 1) # weighted in not significant
food_dist <- as.dist(food_un)
##############taxonomy############
# load taxonomy collapsed for each person
tax <- read.delim("data/microbiome/processed_average/UN_tax_CLR_mean_norm_s.txt", row = 1) # updates from reviewer comments
# drop soylents
tax <- tax[,colnames(tax) %in% colnames(food_un)]
# make taxonomy distance matrix
tax_dist <- dist(t(tax))
###############nutrients############
# load nutrition data
nutr <- read.delim("data/diet/processed_nutr/nutr_65_smry_no_soy.txt", row = 1)
# normalize nutrition data across features (rows)
nutr_n <- sweep(nutr, 1, rowSums(nutr), "/")
# make nutrition distance matrix (euclidean)
nutr_dist <- dist(t(nutr_n))
二、进行procrustes分析并作图
1 | 首先以食物和微生物为例
# make pcoas
pcoa_f <- as.data.frame(pcoa(food_dist)$vectors)
pcoa_t <- as.data.frame(pcoa(tax_dist)$vectors)
# procrustes
pro <- procrustes(pcoa_f, pcoa_t)
pro_test <- protest(pcoa_f, pcoa_t, perm = 999) #普氏分析组间数据的检验
eigen <- sqrt(pro$svd$d)
percent_var <- signif(eigen/sum(eigen), 4)*100
beta_pro <- data.frame(pro$X)
trans_pro <- data.frame(pro$Yrot)
beta_pro$UserName <- rownames(beta_pro)
beta_pro$type <- "Food (Unweighted Unifrac)"
trans_pro$UserName <- rownames(trans_pro)
trans_pro$type <- "Microbiome (Aitchison's)"
colnames(trans_pro) <- colnames(beta_pro)
pval <- signif(pro_test$signif, 1)
plot <- rbind(beta_pro, trans_pro)
food_micro <- ggplot(plot) +
geom_point(size = 2, alpha=0.75, aes(x = Axis.1, y = Axis.2, color = type)) +
scale_color_manual(values = c("#5a2071", "#5f86b7")) +
theme_classic() +
geom_line(aes(x= Axis.1, y=Axis.2, group=UserName), col = "darkgrey", alpha = 0.6) +
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size=9),
legend.position = 'bottom',
axis.text = element_text(size=4),
axis.title = element_text(size=9),
aspect.ratio = 1) +
guides(color = guide_legend(ncol = 1)) +
annotate("text", x = 0.3, y = -0.27, label = paste0("p-value=",pval), size = 2) +
xlab(paste0("PC 1 [",percent_var[1],"%]")) +
ylab(paste0("PC 2 [",percent_var[2],"%]"))
food_micro_leg <- get_legend(food_micro) #得到ggplot图的图例信息
food_micro + theme(legend.position = "none")
2 | 营养与微生物之间的普氏分析
# make pcoas
pcoa_n <- as.data.frame(pcoa(nutr_dist)$vectors)
pcoa_t <- as.data.frame(pcoa(tax_dist)$vectors)
# procrustes
pro <- procrustes(pcoa_n, pcoa_t)
pro_test <- protest(pcoa_n, pcoa_t, perm = 999)
eigen <- sqrt(pro$svd$d)
percent_var <- signif(eigen/sum(eigen), 4)*100
beta_pro <- data.frame(pro$X)
trans_pro <- data.frame(pro$Yrot)
beta_pro$UserName <- rownames(beta_pro)
beta_pro$type <- "Nutrient (Euclidean)"
trans_pro$UserName <- rownames(trans_pro)
trans_pro$type <- "Microbiome (Aitchison's)"
colnames(trans_pro) <- colnames(beta_pro)
pval <- signif(pro_test$signif, 1)
plot <- rbind(beta_pro, trans_pro)
nutr_micro <- ggplot(plot) +
geom_point(size = 2, alpha=0.75, aes(x = Axis.1, y = Axis.2, color = type)) +
scale_color_manual(values = c("#5f86b7", "#2bbaa7")) +
theme_classic() +
geom_line(aes(x= Axis.1, y=Axis.2, group=UserName), col = "darkgrey", alpha = 0.6) +
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size=9),
legend.position = 'bottom',
axis.text = element_text(size=4),
axis.title = element_text(size=9),
aspect.ratio = 1) +
guides(color = guide_legend(ncol = 1)) +
annotate("text", x = 0.01, y = -0.19, label = paste0("p-value=",pval), size = 2) +
xlab(paste0("PC 1 [",percent_var[1],"%]")) +
ylab(paste0("PC 2 [",percent_var[2],"%]"))
nutr_micro + theme(legend.position = "none")
nutr_micro_leg <- get_legend(nutr_micro)
3 | 谷物类数据和微生物数据（欧几里得距离）
# Import the beta diversity table from grains_beta (weighted or unweighted)
food_beta <- read.table(file="data/diet/fiber/grains_data/grains_beta/unweighted_unifrac_grains_fiber.txt")
food_dist <- as.dist(food_beta)
# refer this code to make a pcoa you will have to fix naming and play with the data structure of the dataframe called plot to get this to work.
# Import the beta diversity table for taxonomy
taxa_beta <- read.table(file="data/microbiome/processed_average/UN_tax_beta/euclidean_UN_taxonomy_clr_s.txt")
tax_dist <- as.dist(taxa_beta)
# make pcoas
pcoa_f <- as.data.frame(pcoa(food_dist)$vectors)
pcoa_t <- as.data.frame(pcoa(tax_dist)$vectors)
# procrustes
pro <- procrustes(pcoa_f, pcoa_t)
pro_test <- protest(pcoa_f, pcoa_t, perm = 999)
eigen <- sqrt(pro$svd$d)
percent_var <- signif(eigen/sum(eigen), 4)*100
beta_pro <- data.frame(pro$X)
trans_pro <- data.frame(pro$Yrot)
beta_pro$UserName <- rownames(beta_pro)
beta_pro$type <- "Grain Fiber (Unweighted Unifrac)"
trans_pro$UserName <- rownames(trans_pro)
trans_pro$type <- "Microbiome (Aitchison's)"
colnames(trans_pro) <- colnames(beta_pro)
pval <- signif(pro_test$signif)
plot <- rbind(beta_pro, trans_pro)
grain_micro <- ggplot(plot) +
geom_point(size = 2, alpha=0.75, aes(x = Axis.1, y = Axis.2, color = type)) +
scale_color_manual(values = c("#fe9700", "#5f86b7")) +
theme_classic() +
geom_line(aes(x= Axis.1, y=Axis.2, group=UserName), col = "darkgrey", alpha = 0.6) +
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size=9),
legend.position = 'bottom',
axis.text = element_text(size=4),
axis.title = element_text(size=9),
aspect.ratio = 1) +
guides(color = guide_legend(ncol = 1)) +
annotate("text", x = 0.05, y = -0.27, label = paste0("p-value=",pval), size = 2) +
xlab(paste0("PC 1 [",percent_var[1],"%]")) +
ylab(paste0("PC 2 [",percent_var[2],"%]"))
grain_micro + theme(legend.position = "none")
grain_leg <- get_legend(grain_micro)
4 | 水果数据和微生物数据
# Import the beta diversity table from fruit_beta (weighted or unweighted)
food_beta <- read.table(file="data/diet/fiber/fruit_data/fruit_beta/unweighted_unifrac_fruit_fiber.txt")
food_dist <- as.dist(food_beta)
# make pcoas
pcoa_f <- as.data.frame(pcoa(food_dist)$vectors)
pcoa_t <- as.data.frame(pcoa(tax_dist)$vectors)
# procrustes
pro <- procrustes(pcoa_f, pcoa_t)
pro_test <- protest(pcoa_f, pcoa_t, perm = 999)
eigen <- sqrt(pro$svd$d)
percent_var <- signif(eigen/sum(eigen), 4)*100
beta_pro <- data.frame(pro$X)
trans_pro <- data.frame(pro$Yrot)
beta_pro$UserName <- rownames(beta_pro)
beta_pro$type <- "Fruit Fiber (Unweighted Unifrac)"
trans_pro$UserName <- rownames(trans_pro)
trans_pro$type <- "Microbiome (CLR-Euclidean)"
colnames(trans_pro) <- colnames(beta_pro)
pval <- signif(pro_test$signif, 3)
plot <- rbind(beta_pro, trans_pro)
fruit_micro <- ggplot(plot) +
geom_point(size = 2, alpha=0.75, aes(x = Axis.1, y = Axis.2, color = type)) +
scale_color_manual(values = c("#CBD13E", "#5f86b7")) +
theme_classic() +
geom_line(aes(x= Axis.1, y=Axis.2, group=UserName), col = "darkgrey", alpha = 0.6) +
theme(panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size=9),
legend.position = 'bottom',
axis.text = element_text(size=4),
axis.title = element_text(size=9),
aspect.ratio = 1) +
guides(color = guide_legend(ncol = 1)) +
annotate("text", x = 0.33, y = -0.33, label = paste0("p-value=",pval), size = 2) +
xlab(paste0("PC 1 [",percent_var[1],"%]")) +
ylab(paste0("PC 2 [",percent_var[2],"%]"))
fruit_micro + theme(legend.position = "none")
fruit_leg <- get_legend(fruit_micro)
# 合并四个图
plotC_H <- plot_grid( food_micro + theme(legend.position = "none"),
nutr_micro + theme(legend.position = "none"),
grain_micro + theme(legend.position = "none"),
fruit_micro + theme(legend.position = "none"),
ncol = 2,
align = "h",
labels = c("E", "F", "G", "H"))
save_plot("Figure2E_H.pdf", plotC_H, base_width = 3.5, base_height = 3.5)
5 | 出图，大功告成
图三 普氏分析图
参考文章
[1] https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30250-1
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