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S19S_Metabolome_Analysis.R
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S19S_Metabolome_Analysis.R
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### Demonstration R script to plot whole S19S FTICR-metric distributions
# Swtiches
match.wat.sed = F # Controls whether or not the sites are matched
# Load in necessary libraries first
library(ggplot2); library(reshape2) # For pretty plots
library(vegan) # For ecology
library(ggpubr) # For to combine plots
library(dplyr) # For reorganization
library(stringr) # For string manipulation
# ######################### #
#### Data Pre-processing ####
# ######################### #
# Set working directory
setwd("/path/to/Metabolome Data Files")
# Load in mol. data (contains values we want to plot)
data = read.csv("Processed_S19S_Sed-Water_08.12_Data.csv", stringsAsFactors = F, row.names = 1)
mol = read.csv("Processed_S19S_Sed-Water_08.12_Mol.csv", stringsAsFactors = F, row.names = 1)
# Removing poorly calibrated samples from the dataset
poor.cal = read.csv(list.files(pattern = "*_Poorly_Calibrated_Samples.csv"))
if(length(poor.cal[,1]) > 1){
data = data[,-which(colnames(data) %in% gsub("-", ".", poor.cal$samples))]
} else {
stop("You've specified to remove poor calibrants, but there were none provided.")
}
rm(poor.cal)
# Error checking step that ensures the masses in my mol file match my data file exactly
if(!identical(row.names(mol), row.names(data))){
stop("Your masses in the data file and mol file do not match. Maybe incorrect files were loaded.")
}
# Removing peaks that were not assigned a molecular formula
na.loc = which(mol$MolForm %in% NA)
mol = mol[-na.loc,]
data = data[-na.loc,]
rm(na.loc)
# Given that ICR data cannot reliable track abundance/concentration, we need to set data to presence/absence
data[data > 0] = 1
# Renaming bs1_classes
mol$bs1_class[grep(";", mol$bs1_class)] = "Multi-class"
# Adding mass
mol$Mass = as.numeric(as.character(row.names(mol)))
# Creating factors sheet
factors = data.frame(Samples = colnames(data), Sample_Type = "Surface Water", stringsAsFactors = F)
factors$Sample_Type[grep("Field", factors$Samples)] = "Sediment"
factors$Site = str_extract(factors$Samples, "[0-9]{4}")
### Matching sites, if the switch is set
if(match.wat.sed == T){
# Matching data across samples types
surf.sites = unique(factors$Site[which(factors$Sample_Type %in% "Surface Water")]) # Sites in surface water
sed.sites = unique(factors$Site[which(factors$Sample_Type %in% "Sediment")]) # Sites in sediment
common.sites = intersect(surf.sites, sed.sites) # Sites common to both sample types
# Subset factors based upon common sites
factors = factors[which(factors$Site %in% common.sites),]
data = data[,which(colnames(data) %in% factors$Samples)]
# Drop missing molecular formula
mol = mol[-which(rowSums(data) == 0),]
data = data[-which(rowSums(data) == 0),]
}
rm(match.wat.sed)
# ##################################################################### #
#### Analyzing all formula found in either surface water or sediment ####
# ##################################################################### #
# Unique sample types (i.e., water and sediment)
uniq.sample.type = unique(factors$Sample_Type)
# Shorthand way of doing those 6 commands above
data.by.type = as.data.frame(matrix(nrow = nrow(data), ncol = length(uniq.sample.type),
dimnames = list(row.names(data), uniq.sample.type)))
# Using a for-loop to run through our data and identify peaks present in each sample type
for(i in 1:length(uniq.sample.type)){
# Find temporary datasets for sample types
temp = data[,which(factors$Sample_Type %in% uniq.sample.type[i])]
# Add in sums into the parent data frame
data.by.type[,i] = rowSums(temp)
}
rm(temp, i)
# Resetting data back to presence/absence
data.by.type[data.by.type > 0] = 1
# Partitioning the molecular information based upon sample
temp.mol = mol[which(data.by.type$`Surface Water` > 0),] # Finding molecular information for surface water
temp.mol = temp.mol[,c("AI_Mod", "DBE", "NOSC")] # Selecting subset of interesting variables
temp.mol = melt(as.matrix(temp.mol)) # Melting as a matrix to get the Var1/Var2 melt format
temp.mol$Sample_Type = "Surface Water" # Adding on a qualifier to know what sample type the data is coming from
melt.mol.by.type = temp.mol # Creating the object to eventually go into ggplot
temp.mol = mol[which(data.by.type$`Sediment` > 0),]
temp.mol = temp.mol[,c("AI_Mod", "DBE", "NOSC")] # Selecting subset of interesting variables
temp.mol = melt(as.matrix(temp.mol)) # Melting as a matrix to get the Var1/Var2 melt format
temp.mol$Sample_Type = "Sediment" # Adding on a qualifier to know what sample type the data is coming from
melt.mol.by.type = rbind(melt.mol.by.type, temp.mol)
rm(temp.mol)
# Adding in molecular formula-by-sample count into our melt.mol.by.type object
melt.mol.by.type = rbind(melt.mol.by.type, data.frame(Var1 = colnames(data),
Var2 = "Molecular Formula Count",
value = colSums(data),
Sample_Type = factors$Sample_Type))
# Statistics
metric.stats = NULL
uniq.met = unique(as.character(melt.mol.by.type$Var2))
for(i in 1:length(uniq.met)){
w = which(melt.mol.by.type$Var2 %in% uniq.met[i])
wil.test = wilcox.test(value~Sample_Type, data = melt.mol.by.type[w,], alternative = "two.sided")
wil.test = data.frame(Comparison = uniq.met[i], W = wil.test$statistic, P.value = wil.test$p.value,
Test = wil.test$alternative)
metric.stats = rbind(metric.stats, wil.test)
}
metric.stats$P.value = p.adjust(metric.stats$P.value, method = "fdr")
rm(wil.test, w, i)
# Plotting metrics
ggplot(melt.mol.by.type, aes(x = value, group = Sample_Type))+
geom_density(aes(fill = Sample_Type), alpha = 0.5)+
facet_wrap(Var2~., scales = "free", ncol = 1)+
theme_bw() + theme(text = element_text(size=12, color="black"),
axis.text = element_text(color = "black"),
axis.ticks = element_line(color = "black"),
panel.background = element_blank(),
panel.grid = element_blank())
# ################################################ #
#### Comparing el. comp. and bs1 between groups ####
# ################################################ #
### Elemental Composition by sample
el.comp = matrix(data = 0, nrow = ncol(data), ncol = length(unique(mol$El_comp)),
dimnames = list(colnames(data), unique(mol$El_comp)))
for(i in 1:nrow(el.comp)){
temp = mol[which(data[,i] > 0),] # Mol data for a given sample
for(j in 1:ncol(el.comp)){
el.comp[i,j] = length(which(temp$El_comp %in% colnames(el.comp)[j]))
}
} # Counting the number of times a given elemental composition appears in a dataset
el.comp = as.data.frame(t(apply(el.comp, 1, function(x) (x/sum(x))*100))) # Relative abundance
el.comp = cbind(factors, el.comp)
el.comp = melt(el.comp, id.vars = colnames(factors))
### Compound Class by sample
comp.class = matrix(data = 0, nrow = ncol(data), ncol = length(unique(mol$bs1_class)),
dimnames = list(colnames(data), unique(mol$bs1_class)))
for(i in 1:nrow(comp.class)){
temp = mol[which(data[,i] > 0),] # Mol data for a given sample
for(j in 1:ncol(comp.class)){
comp.class[i,j] = length(which(temp$bs1_class %in% colnames(comp.class)[j]))
}
} # Counting the number of times a given elemental composition appears in a dataset
rm(temp, i, j)
comp.class = as.data.frame(t(apply(comp.class, 1, function(x) (x/sum(x))*100)))
comp.class = cbind(factors, comp.class)
comp.class = melt(comp.class, id.vars = colnames(factors))
comp.class$variable = gsub("Hydrocarbon", "HC", comp.class$variable) # Shortening names
comp.class$variable = gsub("Carbohydrate", "Carb.", comp.class$variable)
# Performing stats on el. comp
el.stats = NULL
for(curr.el in unique(el.comp$variable)){
temp = el.comp[which(el.comp$variable %in% curr.el),]
max.val = max(temp$value)
temp = wilcox.test(value~Sample_Type, data = temp, alternative = "two.sided")
temp = data.frame(Comparison = curr.el, W = temp$statistic, p.value = temp$p.value, max.val = max.val)
el.stats = rbind(el.stats, temp)
}
el.stats$p.value = p.adjust(el.stats$p.value, method = "fdr")
el.stats$Symbol = symnum(el.stats$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " "))
rm(curr.el, temp)
# Performing stats on comp. class
comp.stats = NULL
for(curr.comp in unique(comp.class$variable)){
temp = comp.class[which(comp.class$variable %in% curr.comp),]
max.val = max(temp$value)
temp = wilcox.test(value~Sample_Type, data = temp, alternative = "two.sided")
temp = data.frame(Comparison = curr.comp, W = temp$statistic, p.value = temp$p.value, max.val = max.val)
comp.stats = rbind(comp.stats, temp)
}
comp.stats$p.value = p.adjust(comp.stats$p.value, method = "fdr")
comp.stats$Symbol = symnum(comp.stats$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " "))
rm(curr.comp, temp)
# Making boxplots for compound class and elem. comp.
el.plot = ggplot(data = el.comp)+
geom_boxplot(aes(x = variable, y = value, color = Sample_Type))+
theme_bw() + xlab(NULL) + ylab("Relative Abundance (%)") + labs(color = "Sample Type:")+
geom_text(data = el.stats, aes(x = Comparison, y = max.val+2, label = as.character(Symbol)), size = 7)+
theme(text = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 14),
legend.title = element_text(color = "black", size = 14),
panel.border = element_rect(color = "black"),
axis.ticks = element_line(colour = "black"),
panel.grid = element_blank(),
panel.background = element_blank())
comp.plot = ggplot(data = comp.class)+
geom_boxplot(aes(x = variable, y = value, color = Sample_Type))+
theme_bw() + xlab(NULL) + ylab("Relative Abundance (%)") + labs(color = "Sample Type:")+
geom_text(data = comp.stats, aes(x = Comparison, y = max.val+2, label = as.character(Symbol)), size = 7)+
theme(text = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 14),
legend.title = element_text(color = "black", size = 14),
panel.border = element_rect(color = "black"),
axis.ticks = element_line(colour = "black"),
panel.grid = element_blank(),
panel.background = element_blank())
ggarrange(el.plot, comp.plot, ncol = 1, common.legend = T)
# ####################################################### #
#### Generating pie charts for elem. composition & bs1 ####
# ####################################################### #
# Partitioning the molecular information based upon sample
surfacew_mol = mol[which(data.by.type$`Surface Water` > 0),] # Finding molecular information for surface water
sediment_mol = mol[which(data.by.type$`Sediment` > 0),]
# Generate tables for the frequency of elemental compositions
surfacew_table_El_comp = as.data.frame(table(surfacew_mol$El_comp))
sediment_table_El_comp = as.data.frame(table(sediment_mol$El_comp))
surfacew_table_El_comp$Freq = (surfacew_table_El_comp$Freq/sum(surfacew_table_El_comp$Freq))*100
sediment_table_El_comp$Freq = (sediment_table_El_comp$Freq/sum(sediment_table_El_comp$Freq))*100
# Generate tables for the freqency of boundary sets
surfacew_table_bs1_class = as.data.frame(table(surfacew_mol$bs1_class))
sediment_table_bs1_class = as.data.frame(table(sediment_mol$bs1_class))
surfacew_table_bs1_class$Freq = (surfacew_table_bs1_class$Freq/sum(surfacew_table_bs1_class$Freq))*100
sediment_table_bs1_class$Freq = (sediment_table_bs1_class$Freq/sum(sediment_table_bs1_class$Freq))*100
# Adding sample type qualifiers
surfacew_table_El_comp$SampleType = "Surface Water"
sediment_table_El_comp$SampleType = "Sediment"
surfacew_table_bs1_class$SampleType = "Surface Water"
sediment_table_bs1_class$SampleType = "Sediment"
# Combining frequency tables
CombinedTable_El_comp = rbind(surfacew_table_El_comp, sediment_table_El_comp)
CombinedTable_bs1_class = rbind(surfacew_table_bs1_class, sediment_table_bs1_class)
rm(list = ls(pattern = c("sediment|surfacew")))
# Calculating position
CombinedTable_El_comp = CombinedTable_El_comp %>% group_by(SampleType) %>%
mutate(Position = (cumsum(Freq) - Freq/2)) %>% mutate(Label = paste0(round(Freq, digits = 2), "%"))
CombinedTable_bs1_class = CombinedTable_bs1_class %>% group_by(SampleType) %>%
mutate(Position = (cumsum(Freq) - Freq/2)) %>% mutate(Label = paste0(round(Freq, digits = 2), "%"))
# Create piechart from table data
col = colorRampPalette(c("#b2182b", "white", "#2166ac"))(8)
Comp_pie = ggplot(data = CombinedTable_El_comp, aes(x = "", y = Freq, fill = Var1))+
geom_bar(width = 1, stat = "identity")+
coord_polar("y", start=0)+
facet_wrap(SampleType~., ncol = 1)+
geom_text(aes(label = Label), position = position_stack(vjust = 0.5))+
scale_fill_manual(values = col)+theme_bw()+
theme(axis.text = element_blank(), axis.title = element_blank(),
axis.ticks = element_blank(), panel.background = element_blank(),
panel.grid = element_blank(), panel.border = element_blank())
col = colorRampPalette(c("#762a83", "white", "#1b7837"))(10)
Class_pie = ggplot(data = CombinedTable_bs1_class, aes(x = "", y = Freq, fill = Var1))+
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start=0) +
facet_wrap(SampleType~., ncol = 1)+
geom_text(aes(label = Label), position = position_stack(vjust = 0.5))+
scale_fill_manual(values = col)+theme_bw()+
theme(axis.text = element_blank(), axis.title = element_blank(),
axis.ticks = element_blank(), panel.background = element_blank(),
panel.grid = element_blank(), panel.border = element_blank())
ggarrange(Comp_pie, Class_pie)
# ########################### #
#### Multivariate Analyses ####
# ########################### #
# Principal component analysis
pca = prcomp(x = t(data))
# Everything below this line should not change very much whether we use PCA or NMDS
ordination.scores = scores(pca) # Works with both PCA and NMDS, change the object accordingly
ordination.scores = as.data.frame(ordination.scores) # ggplot doesn't like matrices - needs to be converted to a data frame
ordination.scores$SampleType = factors$Sample_Type # Adding in sample type to our ordination scores object
# We have everything necessary for ggplot - we want to plot PC1 and PC2
ggplot(data = ordination.scores, aes(x = PC1, y = PC2, color = SampleType))+
xlab(paste0("PC1 (", summary(pca)$importance[2,1]*100, "%)"))+
ylab(paste0("PC2 (", summary(pca)$importance[2,2]*100, "%)"))+
geom_point(size = 2) + theme_bw()+
theme(text = element_text(color = "black", size = 12),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(color = "black", size = 14),
legend.text = element_text(color = "black", size = 14),
panel.border = element_rect(color = "black"),
axis.ticks = element_line(colour = "black"),
panel.grid = element_blank(),
panel.background = element_blank())
# PERMANOVA
dist = vegdist(t(data), method = "euclidean", binary = T)
perm = adonis(dist~factors$Sample_Type, permutations = 999)
# Beta-dispersion analysis
beta.disp = betadisper(dist, group = factors$Sample_Type)
beta.disp = data.frame(Type = as.character(beta.disp$group), Distance = as.numeric(beta.disp$distances),
stringsAsFactors = F)
beta.stats = wilcox.test(Distance~Type, data = beta.disp)
# Plotting beta-diserpsion
ggplot(data = beta.disp, aes(x = Type, y = Distance))+
geom_boxplot(aes(color = Type))+
xlab(NULL)+ylab("Distance to Centroid")+
theme_bw()+theme(legend.position = "none",
axis.text = element_text(color = "black", size = 11),
axis.title = element_text(color = "black", size = 13),
axis.ticks = element_line(color = "black"),
panel.border = element_rect(color = "black"),
panel.background = element_blank(),
panel.grid = element_blank())