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TelM_MS_analysis.r
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TelM_MS_analysis.r
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library(RColorBrewer)
library(forcats)
library(ggplot2)
library(ggpubr)
library(Hmisc)
monument_read_abun <- read.csv('monumentome_read_abundance.csv',check.names = F)
monument_metadata <- read.csv('Metadata_Monumentome_2022-04-25_remove_comma.csv',check.names = F)
# read filter (100 reads)
read_posi <- c()
for (i in 1:length(monument_read_abun$name)){
sample <- monument_read_abun$name[i]
sample_id <- strsplit(sample,'.',fixed = T)[[1]][1]
bracken <- strsplit(sample,'.',fixed = T)[[1]][2]
if (bracken == 'bracken_num'){
read_posi <- c(read_posi,i)
rownames(monument_read_abun)[i] <- sample_id
}
}
monument_read <- monument_read_abun[read_posi,]
monument_read <- monument_read[,-1]
monument_read[monument_read < 100] <- 0
# Just keep bacterial
taxa_anno_file <- read.csv('monument_taxonomic_level-Adjust_final.csv',check.names = F,header = T)
bacteria_list <- taxa_anno_file$taxa[which(taxa_anno_file$Kingdom == 'Bacteria')]
monument_read <- monument_read[,bacteria_list]
# Calculate RSA
data_normalise <- function(df) {
return(df/rowSums(df))
}
monument_abun <- monument_read/rowSums(monument_read) # relative abundance data
monument_abun[is.na(monument_abun)] <- 0
monument_abun$sample_id <- rownames(monument_abun)
# abundance data table
monument_data <- merge(monument_metadata,monument_abun,by.x="sample_id",by.y="sample_id")
for (i in 1:length(monument_data$city)){
if (is.na(monument_data$latitude[i])){
city <- monument_data$city[i]
if (city == 'Tel Megiddo'){
monument_data[i,'latitude'] <- 32.5806
monument_data[i,'longitude'] <- 35.1795
}else if (city == 'Ulaanbaatar'){
monument_data[i,'latitude'] <- 47.8923
monument_data[i,'longitude'] <- 106.9090
}else if (city == 'Seoul'){
monument_data[i,'latitude'] <- 37.5480
monument_data[i,'longitude'] <- 126.9887
}
}
}
# write.csv(monument_data,'monument_data_filter_with_control_b.csv',row.names = F)
## read data table
monument_read$sample_id <- rownames(monument_read)
monument_read_data <- merge(monument_metadata,monument_read,by.x="sample_id",by.y="sample_id")
for (i in 1:length(monument_read_data$city)){
if (is.na(monument_read_data$latitude[i])){
city <- monument_read_data$city[i]
if (city == 'Tel Megiddo'){
monument_read_data[i,'latitude'] <- 32.5806
monument_read_data[i,'longitude'] <- 35.1795
}else if (city == 'Ulaanbaatar'){
monument_read_data[i,'latitude'] <- 47.8923
monument_read_data[i,'longitude'] <- 106.9090
}else if (city == 'Seoul'){
monument_read_data[i,'latitude'] <- 37.5480
monument_read_data[i,'longitude'] <- 126.9887
}
}
}
# write.csv(monument_read_data,'monument_read_data_with_control_b.csv',row.names = F)
# ------------------------------------------------------------------------------
# Extract Tel Megiddo samples
telm_file <- monument_data[monument_data$city == "Tel Megiddo",]
monument_file_filter <- monument_data[-c(which(rowSums(monument_data[,12:length(colnames(monument_data))]) == 0)),-c(which(colSums(monument_data[,12:length(colnames(monument_data))]) == 0) + 11)]
# remove taxa with zero abundance
taxa_all_list <- colnames(telm_file)[12:length(colnames(telm_file))]
taxa_abun_sum <- colSums(telm_file[12:length(colnames(telm_file))])
posi <- which(taxa_abun_sum!=0)
telm_file <- telm_file[,c(colnames(telm_file)[1:11],taxa_all_list[posi])]
#-------------------------------------------------------------------------------
# taxa count per city
# Create a data form to store the number of times that taxa appears in each city
taxa_all_list <- colnames(monument_file_filter)[12:length(colnames(monument_file_filter))]
monument_file_filter$city <- factor(monument_file_filter$city)
taxa_count_per_city <- data.frame(row.names = c(levels(monument_file_filter$city)))
for (i in 1:length(levels(monument_file_filter$city))){
for (j in 1: length(taxa_all_list)){
city <- levels(monument_file_filter$city)[i]
taxa <- taxa_all_list[j]
count <- sum(monument_file_filter[monument_file_filter$city == city,][,taxa] != 0)
taxa_count_per_city[city,taxa] <- count
}
}
# write.csv(taxa_count_per_city,'monument_taxa_conunt_per_city.csv')
telm_uniq_taxa <- c()
for (i in colnames(taxa_count_per_city)){
if (sum(taxa_count_per_city[,i])==taxa_count_per_city["Tel Megiddo",i]){
telm_uniq_taxa <- c(telm_uniq_taxa,i)
}
}
telm_uniq_taxa_file <- data.frame(telm_uniq_taxa)
# write.csv(telm_uniq_taxa_file,'TelMegiddo_uniq_taxa.csv')
## Calculate average relative abundance in city and global
monument_abun <- data.frame(row.names = c(levels(monument_file_filter$city)))
for (i in 1:length(levels(monument_file_filter$city))){
for (j in 1: length(taxa_all_list)){
city <- levels(monument_file_filter$city)[i]
taxa <- taxa_all_list[j]
abun <- sum(monument_file_filter[monument_file_filter$city == city,][,taxa])/length(monument_file_filter[monument_file_filter$city == city,][,taxa])
monument_abun[city,taxa] <- abun
}
}
# Calculate global average relative abundance
global_proportion <- c()
for (i in 1:(length(taxa_all_list))){
abun <- sum(monument_abun[,i])/length(monument_abun[,i])
global_proportion <- c(global_proportion,abun)
}
monument_abun <- rbind.data.frame(global_proportion,monument_abun)
rownames(monument_abun)[1] <- 'Global'
# write.csv(monument_abun,'monument_abun_per_city.csv')
taxa_anno_filter <- taxa_anno_file[which(taxa_anno_file$taxa %in% colnames(monument_abun)),]
taxa_anno_filter[taxa_anno_filter$Phylum =='','Phylum'] <- 'None'
# taxa_anno_filter <- droplevels(taxa_anno_filter)
taxa_anno_filter$Phylum <- factor(taxa_anno_filter$Phylum)
phylum_df <- data.frame(row.names = rownames(monument_abun)[2:dim(monument_abun)[1]])
for (i in 2:length(rownames(monument_abun))){
city <- rownames(monument_abun)[i]
for (j in 1:length(levels(taxa_anno_filter$Phylum))){
phylum <- levels(taxa_anno_filter$Phylum)[j]
taxa_list <- taxa_anno_filter[taxa_anno_filter$Phylum == phylum,][,'taxa']
# Calculate the sum of relative abundance values of taxa in the same phylum
percentage <- sum(as.numeric(monument_abun[city,taxa_list]))
phylum_df[city,phylum] <- percentage
}
phylum_df[city,'No-annotation'] <- 1 - sum(phylum_df[city,levels(taxa_anno_filter$Phylum)])
}
# write.csv(phylum_df,'20221128monument_phylum_abundance.csv')
# write.csv(taxa_anno_filter,'monument_taxonomy_annotation.csv',row.names = F)
# ------------------------------------------------------------------------------
# Tel Megiddo vs. other monument cites
city_abun <- as.data.frame(t(monument_abun))
city_abun <- city_abun[order(city_abun$`Tel Megiddo`,decreasing = T),]
data <- city_abun[1:25,]
data2 <- t(data)
for (i in colnames(data2)){
data2[,i] <- (data2[,i] - min(data2[,i]))/(max(data2[,i]) - min(data2[,i]))
}
data2 <- t(data2)
data2 <- as.data.frame(data2)
library(reshape2)
data2$Taxa <- rownames(data2)
data2$Taxa <- fct_inorder(data2$Taxa)
data2$Taxa <- fct_relevel(data2$Taxa,rev(levels(data2$Taxa)))
city_name <- colnames(data2)
for (i in 1:length(city_name)){
city_name[i] <- gsub('_',', ',city_name[i])
}
colnames(data2) <- city_name
data_m <- melt(data2, id.vars=c("Taxa"))
head(data_m)
p = ggplot(data_m, aes(x=variable,y=Taxa))+
geom_tile(aes(fill=value))+
theme(axis.text.x=element_text(angle=45,hjust=1, vjust=1))+
labs(fill="RSA\n(normalized)")+
xlab('City')
# ggsave('top25_TelM_RSA_normalized.png',p,
# width = 16,
# height = 14,
# units = "cm",
# dpi = 600)
data$Taxa <- rownames(data)
data$Taxa <- fct_inorder(data$Taxa)
data$Taxa <- fct_relevel(data$Taxa,rev(levels(data$Taxa)))
colnames(data) <- city_name
data_m <- melt(data, id.vars=c("Taxa"))
head(data_m)
p = ggplot(data_m, aes(x=variable,y=Taxa))+
geom_tile(aes(fill=value))+
theme(axis.text.x=element_text(angle=45,hjust=1, vjust=1))+
labs(fill="RSA")+
xlab('City')
# ggsave('top25_TelM_RSA_new.png',p,
# width = 15,
# height = 14,
# units = "cm",
# dpi = 600)
# ------------------------------------------------------------------------------
# Tel Megiddo taxa analysis
# sum(monument_abun['Tel Megiddo',])
taxa_anno_file <- read.csv('monument_taxonomic_level-Adjust_final.csv',check.names = F,header = T)
# 1
library(readxl)
telm_anno_file_2 <- read_excel('TelMegiddo - sampling_final.xlsx',sheet = 1, na = 'NA')
telm_file_backup <- telm_file
for (i in 1:length(telm_file$pangea_barcode)){
s_id <- telm_file$pangea_barcode[i]
s_id <- strsplit(s_id,'-')[[1]][2]
s_id <- substr(s_id,2,length(strsplit(s_id,'')[[1]]))
telm_file[i,'Site'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Site_merged']
telm_file[i,'Material'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Material']
telm_file[i,'Access'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Access']
}
telm_file2 <- telm_file[,c(1:11,3430,3431,3432,12:3429)]
rownames(telm_file2) <- telm_file2$sample_id
# Filter of read data
monument_read_file_filter <- monument_read_data[-c(which(rowSums(monument_read_data[,12:length(colnames(monument_read_data))]) == 0)),-c(which(colSums(monument_read_data[,12:length(colnames(monument_read_data))]) == 0)+11)]
telm_read_file <- monument_read_file_filter[monument_read_file_filter$city == "Tel Megiddo",]
# remove taxa with zero abundance
taxa_all_list2 <- colnames(telm_read_file)[12:length(colnames(telm_read_file))]
taxa_read_sum <- colSums(telm_read_file[12:length(colnames(telm_read_file))])
posi2 <- which(taxa_read_sum!=0)
telm_read_file <- telm_read_file[,c(colnames(telm_read_file)[1:11],taxa_all_list2[posi2])]
for (i in 1:length(telm_read_file$pangea_barcode)){
s_id <- telm_read_file$pangea_barcode[i]
s_id <- strsplit(s_id,'-')[[1]][2]
s_id <- substr(s_id,2,length(strsplit(s_id,'')[[1]]))
telm_read_file[i,'Site'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Site_merged']
telm_read_file[i,'Material'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Material']
telm_read_file[i,'Access'] <- telm_anno_file_2[telm_anno_file_2$number == s_id,'Access']
}
telm_read_file2 <- telm_read_file[,c(1:11,3430,3431,3432,12:3429)]
rownames(telm_read_file2) <- telm_read_file2$sample_id
telm_read_data <- telm_read_file2[,15:dim(telm_read_file2)[2]] # 44 samples
rownames(telm_read_data) <- telm_read_file2$sample_id
telm_taxa_list <- colnames(telm_file2)[15:dim(telm_file2)[2]]
## diversity analysis ----------------------------------------------------------
# a diversity
library(vegan)
alpha_diversity <- function(otu){
Read_number <- rowSums(otu)
Observed_species <- estimateR(otu)[1, ] # Richness index
Chao1 <- estimateR(otu)[2, ] # Chao 1 index
ACE <- estimateR(otu)[4, ] # ACE index
Shannon <- diversity(otu,'shannon') # shannon index
Simpson <- diversity(otu,'simpson') # Gini-Simpson index
Goods_coverage <- 1 - rowSums(otu == 1) / rowSums(otu)
output <- data.frame(Read_number, Observed_species, Chao1, ACE, Shannon, Simpson, Goods_coverage)
return(output)
}
telm_alpha_diversity <- alpha_diversity(telm_read_data)
telm_alpha_diversity <- telm_alpha_diversity[-c(which(telm_alpha_diversity$Read_number<1000)),]
telm_alpha_diversity <- telm_alpha_diversity[-c(which(telm_alpha_diversity$Observed_species<10)),]
for (i in 1:length(rownames(telm_alpha_diversity))){
id <- rownames(telm_alpha_diversity)[i]
telm_alpha_diversity[i,'Site'] <- telm_read_file2[telm_read_file2$sample_id == id, 'Site']
telm_alpha_diversity[i,'Material'] <- telm_read_file2[telm_read_file2$sample_id == id, 'Material']
telm_alpha_diversity[i,'Access'] <- telm_read_file2[telm_read_file2$sample_id == id, 'Access']
}
# write.csv(telm_alpha_diversity,'telm_alpha_diversity_final.csv')
sample_id_list <- rownames(telm_alpha_diversity)
telm_file2 <- telm_file2[sample_id_list,]
telm_read_file2 <- telm_read_file2[sample_id_list,]
# write.csv(telm_file2,'telm_abun_40samples.csv')
# write.csv(telm_read_file2,'telm_reads_40samples.csv')
telm_read_data <- telm_read_file2[,15:dim(telm_read_file2)[2]] # just contain read data
telm_abun_data <- telm_file2[,15:dim(telm_file2)[2]] # just contain abundance data
## Access-Material (stone access sample) ---------------------------------------
# just run when need to control material when analysis the access
telm_file2 <- telm_file2[telm_file2$Material == 'stone',]
telm_read_file2 <- telm_read_file2[telm_read_file2$Material == 'stone',]
telm_read_data <- telm_read_file2[,15:dim(telm_read_file2)[2]] # just contain read data
telm_abun_data <- telm_file2[,15:dim(telm_file2)[2]] # just contain abundance data
# telm_alpha_diversity <- read.csv('telm_alpha_diversity_final.csv',check.names = T,header = T)
telm_alpha_diversity <- telm_alpha_diversity[telm_alpha_diversity$X %in% telm_file2$sample_id,]
rownames(telm_alpha_diversity) <- telm_alpha_diversity$X
telm_alpha_diversity <- telm_alpha_diversity[,-1]
##------------------------------------------------------------------------------
## telm taxonomic annotation
anno_num <- which(colnames(telm_file2) %in% taxa_anno_file$taxa)
sum(colSums(telm_file2[,which(colnames(telm_file2) %in% taxa_anno_file$taxa)])/dim(telm_file2)[1]) # 0.7695763
taxa_avg_abun <- colSums(telm_file2[,15:dim(telm_file2)[2]])/dim(telm_file2)[1]
sum(taxa_avg_abun)
taxa_avg_abun <- as.data.frame(taxa_avg_abun)
taxa_anno_file_telm <- taxa_anno_file[which(taxa_anno_file$taxa %in% telm_taxa_list),]
taxa_anno_file_telm$Phylum[which(taxa_anno_file_telm$Phylum=="")] <- 'None'
taxa_anno_file_telm$Order[which(taxa_anno_file_telm$Order=="")] <- 'None'
taxa_anno_file_telm$Phylum <- factor(taxa_anno_file_telm$Phylum)
taxa_anno_file_telm$Order <- factor(taxa_anno_file_telm$Order)
# write.csv(taxa_anno_file_telm,'taxa_anno_file_telm.csv')
## group analysis ____________________________________________________________
variable <- 'Site'
# variable <- 'Material'
# variable <- 'Access'
telm_file2[,variable] <- factor(telm_file2[,variable])
for (i in 1:length(rownames(telm_alpha_diversity))){
id <- rownames(telm_alpha_diversity)[i]
telm_alpha_diversity[i,'group'] <- telm_read_file2[telm_read_file2$sample_id == id, variable]
}
sample_n <- table(telm_alpha_diversity$group)
mycompare <- list()
for (i in 1:(length(levels(factor(telm_alpha_diversity$group)))-1)){
k1 <- levels(factor(telm_alpha_diversity$group))[i]
for (j in (i+1):length(levels(factor(telm_alpha_diversity$group)))){
k2 <- levels(factor(telm_alpha_diversity$group))[j]
k <- c(k1,k2)
mycompare <- c(mycompare,list(k))
}
}
mycompare <- list(c("Two-chambered City Gate","Entrance"),c("Two-chambered City Gate","Area K"),c("Two-chambered City Gate","Area S" ),
c("Entrance",'Area K'))
mycompare <- list(c("Two-chambered City Gate","Area J"),c("Two-chambered City Gate","Area K"),c("Two-chambered City Gate","Area S" ),
c("Entrance",'Area K'),
c("Area J",'Area K'))
mycompare <- list(c("Two-chambered City Gate","Area K"),c("Two-chambered City Gate","Area S" ),
c("Entrance",'Area S'),
c("Area J",'Area K'),c("Area J",'Area S'))
mycompare <- list(c('cement','soil'),c('cement','stone'),
c('soil','stone'))
# mycompare <- list(c('non-public','public'))
kruskal.test(Observed_species~group,data=telm_alpha_diversity)
p = ggplot(telm_alpha_diversity,aes(x=group,y=Observed_species,color=group))+
geom_boxplot(alpha=1,outlier.size = 1,size = 0.9,width=0.7,fill='transparent')+
geom_jitter(position = position_jitter(0.1),size=3,alpha=0.6)+
theme_classic()+
labs(x='',y='Observed species')+
ylim(0,2600)+#ylim(0,3500)+#ylim(0,4200)+# ylim(0,4600)+
geom_signif(comparisons = mycompare,
step_increase = 0.12,
# color = 'black',
map_signif_level = T,
test = wilcox.test)+
theme(title = element_text(size=14))+
theme_bw()+
theme(axis.text.x = element_text(size=10,angle = 45,hjust = 1))+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+
theme(axis.text = element_text(colour = 'black',size = 9))+
theme(legend.position="none")+
scale_x_discrete(breaks = c(levels(factor(telm_alpha_diversity$group))),
labels = c(paste0(capitalize(levels(factor(telm_alpha_diversity$group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_obseved_species_new.png'),
# p,
# width = 6,
# height = 8.5,
# units = "cm",
# dpi = 600)
kruskal.test(Shannon~group,data=telm_alpha_diversity)
p = ggplot(telm_alpha_diversity,aes(x=group,y=Shannon,color=group))+
geom_boxplot(alpha=1,outlier.size = 1,size = 0.9,width=0.7,fill='transparent')+
geom_jitter(position = position_jitter(0.1),size=3,alpha=0.6)+
theme_classic()+
ylim(2,7.2)+# ylim(2,8.3)+# ylim(2,9.5)+#ylim(1,9.1)+#ylim(1,12.9)+
labs(x='',y='Shannon ')+
geom_signif(comparisons = mycompare,
step_increase = 0.13,
map_signif_level = T,
test = wilcox.test)+
theme(title = element_text(size=14))+
theme_bw()+
theme(axis.text.x = element_text(size=10,angle = 45,hjust = 1))+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+
theme(axis.text = element_text(colour = 'black',size = 9))+
theme(legend.position="none")+
scale_x_discrete(breaks = c(levels(factor(telm_alpha_diversity$group))),
labels = c(paste0(capitalize(levels(factor(telm_alpha_diversity$group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_shannon_new.png'),
# p,
# width = 6,
# height = 8.5,
# units = "cm",
# dpi = 600)
kruskal.test(Simpson~group,data=telm_alpha_diversity)
p = ggplot(telm_alpha_diversity,aes(x=group,y=Simpson,color=group))+
geom_boxplot(alpha=1,outlier.size = 1,size = 0.9,width=0.7,fill='transparent')+
geom_jitter(position = position_jitter(0.1),size=3,alpha=0.6)+
theme_classic()+
ylim(0.77,1.02)+# ylim(0.77,1.08)+# ylim(0.77,1.14)+#ylim(0.4,1.2)+#ylim(0.4,1.62)+
labs(x='',y='Simpson')+
geom_signif(comparisons = mycompare,
step_increase = 0.12,
map_signif_level = T,
# color = 'black',
test = wilcox.test)+
theme(title = element_text(size=14))+
theme_bw()+
theme(axis.text.x = element_text(size=10,angle = 45,hjust = 1))+
theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+
theme(axis.text = element_text(colour = 'black',size = 9))+
theme(legend.position="none")+
scale_x_discrete(breaks = c(levels(factor(telm_alpha_diversity$group))),
labels = c(paste0(capitalize(levels(factor(telm_alpha_diversity$group))),' (',sample_n,')')))
#
# ggsave(paste0('telm_',variable,'_simpson_new.png'),
# p,
# width = 6,
# height = 8.5,
# units = "cm",
# dpi = 600)
# beta diversity -------------------------------------------------------
library(amplicon)
sample_group <- as.data.frame(telm_alpha_diversity$group)
colnames(sample_group) <- 'Group'
rownames(sample_group) <- rownames(telm_alpha_diversity)
sample_n <- table(telm_alpha_diversity$group)
# PCoA
pcoa_result <- BetaDiv(otu=t(telm_read_data),map=sample_group,group='Group',
dist='bray',method='PCoA',Micromet='adonis')
pcoa_points <- pcoa_result[[2]]
pcoa_method = 'PCoA'
pcoa_adonis <- pcoa_result[[5]]
library(dplyr)
library(tidyr)
hull_data <-
pcoa_points %>%
drop_na() %>%
group_by(Group) %>%
slice(chull(x, y))
ggplot(pcoa_points, aes(x=x, y=y, fill=Group)) +
geom_point(alpha=.7, size=2, pch=21) +
labs(x=paste0(pcoa_method," 1 (36.82%)"),
y=paste0(pcoa_method," 2 (12.28%)"),
# title=pcoa_adonis)+
title=expression(paste('adonis:',italic(r),'=0.028,',italic(p),'=0.324'))) +
# stat_ellipse(linetype=2,level=0.68,aes(group=Group, colour=Group))+
geom_polygon(data = hull_data,
aes(fill = Group,
colour = Group),
alpha = 0.3,
show.legend = FALSE)+
guides(color=F)+
theme_bw()+
geom_hline(aes(yintercept=0), colour="black", linetype=2) +
geom_vline(aes(xintercept=0), colour="black", linetype="dashed") +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title=element_text(vjust=-110,hjust=1.85,size = 10),
text=element_text(size=10),
axis.text =element_text(colour="black",size=9),
# axis.text.y=element_text(colour="black",size=7),
legend.text=element_text(size=9),
legend.background = element_rect(
size=0.3, linetype="solid",
colour ="black"),
legend.position= 'right')+
scale_fill_discrete(name = variable,
labels = c(paste0(capitalize(levels(factor(pcoa_points$Group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_PCoA_Bray_Crutis.png'),
# p,
# width = 16,
# height = 10,
# units = "cm",
# dpi = 600)
##Access----
p = ggplot(pcoa_points, aes(x=x, y=y, fill=Group)) +
geom_point(alpha=.7, size=2, pch=21) +
labs(x=paste0(pcoa_method," 1 (32.01%)"),
y=paste0(pcoa_method," 2 (15.78%)"),
# title=pcoa_adonis)+
title=expression(paste('adonis:',italic(r),'=0.031,',italic(p),'=0.645'))) +
# stat_ellipse(linetype=2,level=0.68,aes(group=Group, colour=Group))+
geom_polygon(data = hull_data,
aes(fill = Group,
colour = Group),
alpha = 0.3,
show.legend = FALSE)+
guides(color=F)+
theme_bw()+
geom_hline(aes(yintercept=0), colour="black", linetype=2) +
geom_vline(aes(xintercept=0), colour="black", linetype="dashed") +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title=element_text(vjust=-120,hjust=0.95,size = 10),
text=element_text(size=10),
axis.text =element_text(colour="black",size=9),
# axis.text.y=element_text(colour="black",size=7),
legend.text=element_text(size=9),
legend.background = element_rect(
size=0.3, linetype="solid",
colour ="black"),
legend.position= "top")+
scale_fill_discrete(name = variable,
labels = c(paste0(capitalize(levels(factor(pcoa_points$Group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_PCoA_Bray_Crutis.png'),
# p,
# width = 10,
# height = 11,
# units = "cm",
# dpi = 600)
##----
# NMDS
nmds_result <- BetaDiv(otu=t(telm_read_data),map=sample_group,group='Group',
dist='bray',method='NMDS',Micromet='adonis')
nmds_points <- nmds_result[[2]]
nmds_method = 'NMDS'
nmds_adonis <- nmds_result[[5]]
hull_data <-
nmds_points %>%
drop_na() %>%
group_by(Group) %>%
slice(chull(x, y))
p = ggplot(nmds_points, aes(x=x, y=y, fill=Group)) +
geom_point(alpha=.7, size=2, pch=21) +
labs(x=paste(nmds_method,"1", sep=""),
y=paste(nmds_method,"2",sep=""),
title=expression(paste('adonis:',italic(r),'=0.288,',italic(p),'=0.002')))+
annotate("text", x= 0.4, y= 1.1,label='Stress=0.13',size=3.5 )+
# stat_ellipse( linetype=2,level=0.68,aes(group =Group, colour= Group))+
# Plot_ConvexHull(nmds_points, col_g)+
geom_polygon(data = hull_data,
aes(fill = Group,
colour = Group),
alpha = 0.3,
show.legend = FALSE)+
guides(color=F)+
theme_bw()+
geom_hline(aes(yintercept=0), colour="black", linetype=2) +
geom_vline(aes(xintercept=0), colour="black", linetype="dashed") +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title=element_text(vjust=-110,hjust=1.85,size = 10),
text=element_text(size=10),
# axis.title.y =element_text(size=7,face="bold",colour="black"),
# axis.title.x =element_text(size=7,face="bold",colour="black"),
# axis.text=element_text(size=7,face="bold"),
axis.text =element_text(colour="black",size=9),
# axis.text.y=element_text(colour="black",size=7),
legend.text=element_text(size=9),
legend.background = element_rect(
size=0.3, linetype="solid",
colour ="black"),
legend.position= 'right')+
scale_fill_discrete(name = variable,
labels = c(paste0(capitalize(levels(factor(nmds_points$Group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_NMDS_Bray_Crutis.png'),
# p,
# width = 16,
# height = 10,
# units = "cm",
# dpi = 600)
## access -----
p = ggplot(nmds_points, aes(x=x, y=y, fill=Group)) +
geom_point(alpha=.7, size=2, pch=21) +
labs(x=paste(nmds_method,"1", sep=""),
y=paste(nmds_method,"2",sep=""),
title=expression(paste('adonis:',italic(r),'=0.028,',italic(p),'=0.326')))+
annotate("text", x= 0.4, y= 1,label='Stress=0.13',size=3.5 )+
# stat_ellipse( linetype=2,level=0.68,aes(group =Group, colour= Group))+
# Plot_ConvexHull(nmds_points, col_g)+
geom_polygon(data = hull_data,
aes(fill = Group,
colour = Group),
alpha = 0.3,
show.legend = FALSE)+
guides(color=F)+
theme_bw()+
geom_hline(aes(yintercept=0), colour="black", linetype=2) +
geom_vline(aes(xintercept=0), colour="black", linetype="dashed") +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title=element_text(vjust=-28,hjust=0.01,size = 10),
text=element_text(size=10),
# axis.title.y =element_text(size=7,face="bold",colour="black"),
# axis.title.x =element_text(size=7,face="bold",colour="black"),
# axis.text=element_text(size=7,face="bold"),
axis.text =element_text(colour="black",size=9),
# axis.text.y=element_text(colour="black",size=7),
legend.text=element_text(size=9),
legend.background = element_rect(
size=0.3, linetype="solid",
colour ="black"),
legend.position= 'top')+
scale_fill_discrete(name = variable,
labels = c(paste0(capitalize(levels(factor(nmds_points$Group))),' (',sample_n,')')))
# ggsave(paste0('telm_',variable,'_NMDS_Bray_Crutis.png'),
# p,
# width = 10,
# height = 11,
# units = "cm",
# dpi = 600)
### ------
## std for adonis in PCoA and NMDS (1000 times)
##delate ----------------------
# variable <- 'Site'
variable <- 'Material'
# variable <- 'Access'
# telm_alpha_diversity <- read.csv("telm_alpha_diversity_final.csv",header = T)
rownames(telm_alpha_diversity) <- telm_alpha_diversity[,1]
telm_alpha_diversity <- telm_alpha_diversity[,-1]
telm_file2[,variable] <- factor(telm_file2[,variable])
for (i in 1:length(rownames(telm_alpha_diversity))){
id <- rownames(telm_alpha_diversity)[i]
telm_alpha_diversity[i,'group'] <- telm_read_file2[telm_read_file2$sample_id == id, variable]
}
##delate ----------------------
r_1000 <- c()
p_1000 <- c()
for (i in 1:1000){
telm_alpha_diversity2 <- telm_alpha_diversity[sample(dim(telm_alpha_diversity)[1],20.8),] # 80%
telm_read_data2 <- telm_read_data[rownames(telm_alpha_diversity2),]
sample_group <- as.data.frame(telm_alpha_diversity2$group)
colnames(sample_group) <- 'Group'
rownames(sample_group) <- rownames(telm_alpha_diversity2)
sample_n <- table(telm_alpha_diversity2$group)
pcoa_result <- BetaDiv(otu=t(telm_read_data2),map=sample_group,group='Group',
dist='bray',method='PCoA',Micromet='adonis')
pcoa_adonis <- pcoa_result[[5]]
r_1000 <- c(r_1000, as.numeric(strsplit(pcoa_adonis,' ')[[1]][2]))
p_1000 <- c(p_1000, as.numeric(strsplit(pcoa_adonis,' ')[[1]][4]))
print(i)
}
avg_r <- mean(r_1000)
avg_p <- mean(p_1000)
sum(r_1000>0)
sum(p_1000<0.05)
r_1000_df <- as.data.frame(r_1000)
# p = ggplot(r_1000_df, aes(x = r_1000)) +
# geom_density(alpha = 0.3)+
# xlab("adonis:r")+
# theme_bw()
# # ggsave('material_PCoA_adonis_r.png',p,dpi = 600)
p_1000_df <- as.data.frame(p_1000)
# p = ggplot(p_1000_df, aes(x = p_1000)) +
# geom_density(alpha = 0.3)+
# xlab("adonis:p")+
# theme_bw()
# # ggsave('material_PCoA_adonis_p.png',p,dpi = 600)
r_p_adonis_1000 <- cbind.data.frame(r_1000,p_1000)
# write.csv(r_p_adonis_1000,'PCoA_1000_adonis_access-stone.csv')
#####
# sd.p=function(x){sd(x)*sqrt((length(x)-1)/length(x))}
# sd.p(r_1000)
# sd(p_1000)
# mean(r_1000)
###### repeat 1000 times NMDS
r_1000 <- c()
p_1000 <- c()
for (i in 1:1000){
telm_alpha_diversity2 <- telm_alpha_diversity[sample(dim(telm_alpha_diversity)[1],20.8),] # 80%
telm_read_data2 <- telm_read_data[rownames(telm_alpha_diversity2),]
sample_group <- as.data.frame(telm_alpha_diversity2$group)
colnames(sample_group) <- 'Group'
rownames(sample_group) <- rownames(telm_alpha_diversity2)
sample_n <- table(telm_alpha_diversity2$group)
nmds_result <- BetaDiv(otu=t(telm_read_data2),map=sample_group,group='Group',
dist='bray',method='NMDS',Micromet='adonis')
nmds_adonis <- nmds_result[[5]]
r_1000 <- c(r_1000, as.numeric(strsplit(nmds_adonis,' ')[[1]][2]))
p_1000 <- c(p_1000, as.numeric(strsplit(nmds_adonis,' ')[[1]][4]))
}
avg_r <- mean(r_1000)
avg_p <- mean(p_1000)
sum(r_1000>0)
sum(p_1000<0.05)
# r_1000_df <- as.data.frame(r_1000)
# p = ggplot(r_1000_df, aes(x = r_1000)) +
# geom_density(alpha = 0.3)+
# xlab("adonis:r")+
# theme_bw()
# ggsave('material_PCoA_adonis_r.png',p,dpi = 600)
# p_1000_df <- as.data.frame(p_1000)
#
# p = ggplot(p_1000_df, aes(x = p_1000)) +
# geom_density(alpha = 0.3)+
# xlab("adonis:p")+
# theme_bw()
# ggsave('material_PCoA_adonis_p.png',p,dpi = 600)
r_p_adonis_1000 <- cbind.data.frame(r_1000,p_1000)
# write.csv(r_p_adonis_1000,'NMDS_1000_adonis_access-stone.csv')
# analysis microbiome composition ----------------------------------
telm_file2[,variable] <- factor(telm_file2[,variable])
abun_df <- data.frame(row.names = c(levels(telm_file2[,variable])))
for (i in 1:length(levels(telm_file2[,variable]))){
for (j in 1: length(telm_taxa_list)){
v <- levels(telm_file2[,variable])[i]
taxa <- telm_taxa_list[j]
abun <- sum(telm_file2[telm_file2[,variable] == v,][,taxa])/length(telm_file2[telm_file2[,variable] == v,][,taxa])
abun_df[v,taxa] <- abun
}
}
sum(abun_df[1,])
# write.csv(abun_df,'site/Tel_Megiddo_site_taxa_abun.csv')
# write.csv(abun_df,'material/Tel_Megiddo_material_taxa_abun.csv')
# write.csv(abun_df,'access/Tel_Megiddo_access_taxa_abun.csv')
# write.csv(abun_df,'access_stone/Tel_Megiddo_access_stone_taxa_abun.csv')
rownames(abun_df) <- abun_df[,1]
abun_df <- abun_df[,-1]
## Phylum relative abundance
phylum_df <- data.frame(row.names = c(levels(telm_file2[,variable])))
for (i in 1:length(levels(telm_file2[,variable]))){
city <- levels(telm_file2[,variable])[i]
for (j in 1:length(levels(taxa_anno_file_telm$Phylum))){
phylum <- levels(taxa_anno_file_telm$Phylum)[j]
taxa_list <- taxa_anno_file_telm[taxa_anno_file_telm$Phylum == phylum,][,'taxa']
# Calculate the sum of relative abundance values of taxa in the same phylum
percentage <- sum(as.numeric(abun_df[city,taxa_list]))
phylum_df[city,phylum] <- percentage
}
}
# write.csv(phylum_df,'site/Tel_Megiddo_site_phylum_abundance.csv')
# write.csv(phylum_df,'material/Tel_Megiddo_material_phylum_abundance.csv')
# write.csv(phylum_df,'access/Tel_Megiddo_access_phylum_abundance.csv')
# write.csv(phylum_df,'access_stone/Tel_Megiddo_access_stone_phylum_abundance.csv')
rownames(phylum_df) <- phylum_df[,1]
phylum_df <- phylum_df[,-1]
## order RSA table
order_df <- data.frame(row.names = c(levels(telm_file2[,variable])))
for (i in 1:length(levels(telm_file2[,variable]))){
city <- levels(telm_file2[,variable])[i]
for (j in 1:length(levels(taxa_anno_file_telm$Order))){
Order <- levels(taxa_anno_file_telm$Order)[j]
taxa_list <- taxa_anno_file_telm[taxa_anno_file_telm$Order == Order,][,'taxa']
# Calculate the sum of relative abundance values of taxa in the same phylum
percentage <- sum(as.numeric(abun_df[city,taxa_list]))
order_df[city,Order] <- percentage
}
}
# write.csv(order_df,'site/Tel_Megiddo_site_order_RSA.csv')
# write.csv(order_df,'material/Tel_Megiddo_material_order_abundance.csv')
# write.csv(order_df,'access/Tel_Megiddo_access_order_abundance.csv')
# write.csv(order_df,'access_stone/Tel_Megiddo_access_stone_order_abundance.csv')
# unique_phylum
# phylum_df2 <- phylum_df
# phylum_df2[phylum_df2 > 0] <- 1
# phylum_occupancy <- colSums(phylum_df2)
# phylum_occupancy <- phylum_occupancy[order(phylum_occupancy)]
#
# uniq_p_city <- phylum_df2[,c(names(phylum_occupancy[1:8]))]
phylum_average_abun <- colSums(phylum_df)/dim(phylum_df)[1]
phylum_average_abun <- phylum_average_abun[order(phylum_average_abun,decreasing = T)]
# phylum name list
# select_phylum <- names(phylum_average_abun[1:8])
select_phylum_df <- phylum_df[,select_phylum]
for (i in 1:dim(select_phylum_df)[1]){
select_phylum_df[i,'Other'] <- sum(phylum_df[i,-which(colnames(phylum_df) %in% select_phylum)])
}
select_phylum_df <- select_phylum_df[order(select_phylum_df$Actinobacteria),]
# write.csv(select_phylum_df,'material/select_phylum_df_plot.csv')
sample_n <- table(telm_file2[,variable])
sample_n2 <- sample_n[rownames(select_phylum_df)]
palette <- colorRampPalette(brewer.pal(10, "Set3"))(10)[1:9] # 9 7
# png("TelM_material_phylum_abundance.png", width = 6,height = 6, units = 'in', res = 600)
par(xpd = T, mgp = c(0,0.7,0), las=2,mar = par()$mar + c(6.5,0,0,8.7))
bp <- barplot(t(select_phylum_df*100), col=palette,
# names.arg= ,
args.legend = list(x = "bottom", inset=c(-0.5,0)), las =2,
cex.names=.7,ylab = "", axisnames = F, axes = T, space =0,
ylim = c(0,100))
mtext(text = paste0(capitalize(rownames(select_phylum_df)),' (',sample_n2,')'), side = 1, at = bp, line = 0.5, padj = 1, cex = 1)
title(ylab="Relative abundance % (Phylum level)",mgp=c(2,0,0),cex.lab=1.2)
legend("right",inset = c(-0.7,0), colnames(select_phylum_df), fill = palette , bty = 1, cex = 1)
par(mar=c(5, 4, 4, 2) + 0.1)
# dev.off()
## access-----------------
# png("TelM_access_phylum_abundance.png", width = 4,height = 6, units = 'in', res = 600)
par(xpd = T, mgp = c(0,0.7,0), las=2,mar = par()$mar + c(3.5,0,0,9))
bp <- barplot(t(select_phylum_df*100), col=palette,
# names.arg= ,
args.legend = list(x = "bottom", inset=c(-0.5,0)), las =2,
cex.names=.7,ylab = "", axisnames = F, axes = T, space =0,
ylim = c(0,100))
mtext(text = paste0(capitalize(rownames(select_phylum_df)),' (',sample_n2,')'), side = 1, at = bp, line = 0.5, padj = 1, cex = 1)
title(ylab="Relative abundance % (Phylum level)",mgp=c(2,0,0),cex.lab=1.2)
legend("right",inset = c(-2.3,0), colnames(select_phylum_df), fill = palette , bty = 1, cex = 1)
par(mar=c(5, 4, 4, 2) + 0.1)
# dev.off()
##
########
## taxa number in each phylum appeared in each site
phylum_taxa <- data.frame(row.names = c(levels(telm_file2[,variable])))
for (i in 1:length(levels(telm_file2[,variable]))){
city <- levels(telm_file2[,variable])[i]