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SFig.7ab-mitag-pcoa.R
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SFig.7ab-mitag-pcoa.R
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library(vegan)
library(ggplot2)
library(plyr)
#size(4*6)
otu <- read.delim('E:/我的坚果云/Project/Mariana586/0.Mitag/0.Prepare/Fig-water-compare-gao.txt', row.names = 1, sep = '\t', stringsAsFactors = F, check.names = F)
otu <- data.frame(t(otu))
#dis <- read.delim('bray.txt', row.names = 1, sep = '\t', stringsAsFactors = F, check.names = F)
group <- read.delim('E:/我的坚果云/Project/Mariana586/0.Mitag/0.Prepare/group.txt', sep = '\t', stringsAsFactors = F)
distance <- vegdist(otu, method = 'bray')
pcoa <- cmdscale(distance, k = (nrow(otu) - 1), eig = T)
#pcoa <- cmdscale(as.dist(dis), k = (nrow(dis) - 1), eig = T)
###anosim
anosim.result<-anosim(distance,group$Site,permutations=999)
summary(anosim.result)
ordiplot(scores(pcoa)[ ,c(1, 2)], type = 't')
summary(pcoa)
pcoa$eig
point <- data.frame(pcoa$point)
species <- wascores(pcoa$points[,1:2], otu)
write.csv(as.matrix(distance), 'distance.csv', quote = F)
write.csv(point, 'pcoa.sample.csv')
write.csv(species, 'pcoa.otu.csv')
pcoa_eig <- (pcoa$eig)[1:2] / sum(pcoa$eig)
sample_site
head(pcoa)
sample_site <- data.frame({pcoa$point})[1:2]
sample_site$names <- rownames(sample_site)
names(sample_site)[1:2] <- c('PCoA1', 'PCoA2')
sample_site <- merge(sample_site, group, by = 'names', all.x = T)
write.csv(sample_site, 'sample_site.csv', quote = F)
sample_site$Site <- factor(sample_site$Site, levels = c('Sediment', 'Water'))
sample_site$Water.Depth <- factor(sample_site$Water.Depth, levels = c('5k','6k', '7k', '8k', '9k', '10k'))
sample_site$Position <- factor(sample_site$Position, levels = c('North slope', 'South slope', 'axis'))
group_border <- ddply(sample_site, 'Site', function(df) df[chull(df[[2]], df[[3]]), ]) #注：group_border 作为下文 geom_polygon() 的做图数据使用
sample_site
pcoa_plot <- ggplot(sample_site, aes(PCoA1, PCoA2, group = Site)) +
theme(panel.grid = element_line(color = 'gray', linetype = 2, size = 0.1), panel.background = element_rect(color = 'black', fill = 'transparent'), legend.key = element_rect(fill = 'transparent')) + #去掉背景框
geom_vline(xintercept = 0, color = 'gray', size = 0.4) +
geom_hline(yintercept = 0, color = 'gray', size = 0.4) +
geom_polygon(data = group_border, aes(fill = Site),alpha=0.1) +
guides(fill = guide_legend(order = 1), shape = guide_legend(order = 2), color = guide_legend(order = 3)) +
scale_shape_manual(values = c(17,11, 16,15,12,8)) +
geom_point(aes(color = Position, shape = Water.Depth), size = 2.5, alpha = 0.8) +
#scale_discrete_manual(values=c('#01CB03','#633434','#696969'),
# aesthetics = 'colour',
# labels = c('South slope', 'North slope',"axis")) +
labs(x = paste('PCoA axis1: ', round(100 * pcoa_eig[1], 2), '%'), y = paste('PCoA axis2: ', round(100 * pcoa_eig[2], 2), '%')) +
annotate('text', label = 'None-CD', x = -0.31, y = -0.15, size = 5, colour = '#C673FF') +
annotate('text', label = 'CD', x = 0.35, y = 0.05, size = 5, colour = '#FF985C')
pcoa_plot
ggsave('PCoA.png', pcoa_plot, width = 6, height = 5)
otu <- read.delim('E:/我的坚果云/Project/Mariana586/0.Mitag/0.Prepare/Figs7-nowater.txt', row.names = 1, sep = '\t', stringsAsFactors = F, check.names = F)
otu <- data.frame(t(otu))
#dis <- read.delim('bray.txt', row.names = 1, sep = '\t', stringsAsFactors = F, check.names = F)
group <- read.delim('E:/我的坚果云/Project/Mariana586/0.Mitag/0.Prepare/group-b.txt', sep = '\t', stringsAsFactors = F)
distance <- vegdist(otu, method = 'bray')
pcoa <- cmdscale(distance, k = (nrow(otu) - 1), eig = T)
#pcoa <- cmdscale(as.dist(dis), k = (nrow(dis) - 1), eig = T)
pcoa
###anosim
anosim.result<-anosim(distance,group$Site,permutations=999)
summary(anosim.result)
ordiplot(scores(pcoa)[ ,c(1, 2)], type = 't')
summary(pcoa)
pcoa$eig
point <- data.frame(pcoa$point)
species <- wascores(pcoa$points[,1:2], otu)
write.csv(as.matrix(distance), 'distance.csv', quote = F)
write.csv(point, 'pcoa.sample.csv')
write.csv(species, 'pcoa.otu.csv')
pcoa_eig <- (pcoa$eig)[1:2] / sum(pcoa$eig)
sample_site
head(pcoa)
sample_site <- data.frame({pcoa$point})[1:2]
sample_site$names <- rownames(sample_site)
names(sample_site)[1:2] <- c('PCoA1', 'PCoA2')
sample_site <- merge(sample_site, group, by = 'names', all.x = T)
write.csv(sample_site, 'sample_site.csv', quote = F)
sample_site$Site <- factor(sample_site$Site, levels = c('slope', 'axis'))
sample_site$Water.Depth <- factor(sample_site$Water.Depth, levels = c('5k','6k', '7k', '8k', '9k', '10k'))
sample_site$Position <- factor(sample_site$Position, levels = c('North slope', 'South slope', 'axis'))
group_border <- ddply(sample_site, 'Site', function(df) df[chull(df[[2]], df[[3]]), ]) #注：group_border 作为下文 geom_polygon() 的做图数据使用
sample_site
pcoa_plot <- ggplot(sample_site, aes(PCoA1, PCoA2, group = Site)) +
theme(panel.grid = element_line(color = 'gray', linetype = 2, size = 0.1), panel.background = element_rect(color = 'black', fill = 'transparent'), legend.key = element_rect(fill = 'transparent')) + #去掉背景框
geom_vline(xintercept = 0, color = 'gray', size = 0.4) +
geom_hline(yintercept = 0, color = 'gray', size = 0.4) +
geom_polygon(data = group_border, aes(fill = Site),alpha=0.1) +
guides(fill = guide_legend(order = 1), shape = guide_legend(order = 2), color = guide_legend(order = 3)) +
scale_shape_manual(values = c(17, 16,15,12,8)) +
geom_point(aes(color = Position, shape = Water.Depth), size = 2.5, alpha = 0.8) + #可在这里修改点的透明度、大小
labs(x = paste('PCoA axis1: ', round(100 * pcoa_eig[1], 2), '%'), y = paste('PCoA axis2: ', round(100 * pcoa_eig[2], 2), '%')) +
#可通过修改下面四句中的点坐标、大小、颜色等，修改“A、B、C、D”标签
annotate('text', label = 'None-CD', x = -0.31, y = -0.15, size = 5, colour = '#C673FF') +
annotate('text', label = 'CD', x = 0.35, y = 0.05, size = 5, colour = '#FF985C')
pcoa_plot
ggsave('PCoA.png', pcoa_plot, width = 6, height = 5)