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Meta16s_Gut.R
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# --------------------------------------------------------------------------------------------------------
# Title: Meta16s_Gut.R
# Author: Silver A. Wolf
# Last Modified: Mon, 21.11.2022
# Version: 0.3.8
# --------------------------------------------------------------------------------------------------------
# Libraries
library("circlize")
library("ComplexHeatmap")
library("dplyr")
library("ggplot2")
library("ggpubr")
library("kableExtra")
library("knitr")
library("metagMisc")
library("microbiome")
library("microbiomeExplorer")
library("microbiomeutilities")
library("micropower")
library("randomcoloR")
library("openxlsx")
library("parallel")
library("phyloseq")
library("sp")
library("stringr")
library("vegan")
# --------------------------------------------------------------------------------------------------------
# [1] Import and preprocess analysis data
# BIOM File
data.biom <- import_biom("output/final_otu_table_clean.biom", parseFunction = parse_taxonomy_default)
# Metadata
meta <- read.xlsx("metadata/22_02_Horses_Overview.xlsx", sheet = 1)
meta.filtered <- meta[meta$AB_Group != "SWITCHED" & !(meta$AB_Group == "CONTROL" & meta$Timepoint == "t2"), ]
# Group order
groups.order <- c("SSG", "5DG", "CONTROL")
# --------------------------------------------------------------------------------------------------------
# [2] Diversity Estimations
# Sample Depth
sample_depth <- sort(sample_sums(data.biom))
min(sample_depth)
max(sample_depth)
median(sample_depth)
sum(sample_depth)
# Alpha Diversity (Raw)
data.alpha <- microbiome::alpha(data.biom)
#png("results/div_rarefaction_curve.png", width = 16, height = 16, units = "cm", res = 500)
#rarecurve(as(t(otu_table(data.biom)), "matrix"), step = 50, cex = 0.5)
#dev.off()
# Alpha diversity (Rarefy)
data.rarefy <- rarefy_even_depth(data.biom, rngseed = 1, sample.size = min(sample_depth), replace = FALSE, trimOTUs = FALSE)
data.alpha.rarefy <- microbiome::alpha(data.rarefy)
#data.rarefy <- aggregate_top_taxa2(data.rarefy, 22, "Rank2")
# Beta Diversity (Bray-Curtis distance)
braycurtis <- phyloseq::distance(data.rarefy, method = "bray")
data.bray <- as.matrix(braycurtis)
# Beta Diversity (PCoA)
braycurtis.pcoa <- ordinate(physeq = data.rarefy, method = "PCoA", distance = "bray")
data.pcoa <- as.data.frame(braycurtis.pcoa$vectors, row.names = NULL, optional = FALSE, cut.names = FALSE, col.names = names(braycurtis.pcoa$vectors), fix.empty.names = TRUE, stringsAsFactors = default.stringsAsFactors())
# Add Metadata
meta.sorted = meta.filtered[match(rownames(data.alpha), meta.filtered$SampleID),]
data.alpha$HORSE = meta.sorted$HorseID
data.alpha$AB_GROUP = factor(meta.sorted$AB_Group, levels = groups.order)
data.alpha$TIMEPOINT = meta.sorted$Timepoint
data.alpha.rarefy$HORSE = meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$HorseID
data.alpha.rarefy$AB_GROUP = factor(meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$AB_Group, levels = groups.order)
data.alpha.rarefy$TIMEPOINT = meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$Timepoint
data.pcoa$HORSE = meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$HorseID
data.pcoa$AB_GROUP = factor(meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$AB_Group, levels = groups.order)
data.pcoa$TIMEPOINT = meta.sorted[meta.sorted$SampleID %in% rownames(data.alpha.rarefy),]$Timepoint
# Export Diversities
write.csv(data.alpha, file = "results/tab_div_alpha_raw.csv", quote = FALSE)
write.csv(data.alpha.rarefy, file = "results/tab_div_alpha_rarefy.csv", quote = FALSE)
write.csv(data.bray, file = "results/tab_div_beta_distance.csv", quote = FALSE)
write.csv(data.pcoa, file = "results/tab_div_beta_pcoa.csv", quote = FALSE)
# Export human-readable OTU table
data.otu <- phyloseq_to_df(data.biom)
write.csv(data.otu, file = "results/tab_otu.csv", row.names = FALSE, quote = FALSE)
data.otu.rarefy <- phyloseq_to_df(data.rarefy)
write.csv(data.otu.rarefy, file = "results/tab_otu_rarefy.csv", row.names = FALSE, quote = FALSE)
# Sort OTU table by metadata
otu.tmp.1 <- data.otu.rarefy[,1:7]
otu.tmp.2 <- data.otu.rarefy[,9:ncol(data.otu.rarefy)]
otu.tmp.3 <- meta.sorted[with(meta.sorted, order(AB_Group, HorseID, Day)), ]
otu.tmp.4 <- otu.tmp.2[,otu.tmp.3$SampleID]
otu.tmp.5 <- data.frame(otu.tmp.1, otu.tmp.4)
otu.row.1 <- c(rep("", 7), otu.tmp.3$HorseID)
otu.row.2 <- c(rep("", 7), otu.tmp.3$Name)
otu.row.3 <- c(rep("", 7), otu.tmp.3$AB_Group)
otu.row.4 <- c(rep("", 7), otu.tmp.3$Timepoint)
otu.tmp.6 <- rbind(colnames(otu.tmp.5), otu.tmp.5)
otu.tmp.6 <- rbind(otu.row.4, otu.tmp.6)
otu.tmp.6 <- rbind(otu.row.3, otu.tmp.6)
otu.tmp.6 <- rbind(otu.row.2, otu.tmp.6)
otu.tmp.6 <- rbind(otu.row.1, otu.tmp.6)
write.table(otu.tmp.6, file = "results/tab_otu_rarefy_reordered.csv", row.names = FALSE, col.names = FALSE, quote = FALSE, sep = ",")
# Total OTUs
nrow(data.otu)
# Unique Genus Level Taxa
length(unique(data.otu$Rank6))
# Diversity values per group
max(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
min(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
median(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t1",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "SSG" & data.alpha.rarefy$TIMEPOINT == "t2",]$diversity_shannon)
max(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
min(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
median(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t1",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "5DG" & data.alpha.rarefy$TIMEPOINT == "t2",]$diversity_shannon)
max(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "CONTROL" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
min(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "CONTROL" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
median(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "CONTROL" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "CONTROL" & data.alpha.rarefy$TIMEPOINT == "t0",]$diversity_shannon)
mean(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP == "CONTROL" & data.alpha.rarefy$TIMEPOINT == "t1",]$diversity_shannon)
# PCA
colours.days = c("t0" = "#00BA38",
"t1" = "#F8766D",
"t2" = "#619CFF"
)
colours.groups = c("SSG" = "#00ff7f",
"5DG" = "#ffa500",
"CONTROL" = "#00bfff"
)
eigenvalue_pc1 = round(braycurtis.pcoa$values$Relative_eig[1]*100, 1)
eigenvalue_pc2 = round(braycurtis.pcoa$values$Relative_eig[2]*100, 1)
# Timepoints - All Samples
png("results/div_pca_time_all.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa,
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "TIMEPOINT",
shape = "TIMEPOINT",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.days,
title = "All Samples - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t2", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Timepoints - SSG
png("results/div_pca_time_ssg.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$AB_GROUP == "SSG", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "TIMEPOINT",
shape = "TIMEPOINT",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.days,
title = "SSG - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t2" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Timepoints - 5DG
png("results/div_pca_time_5dg.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$AB_GROUP == "5DG", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "TIMEPOINT",
shape = "TIMEPOINT",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.days,
title = "5DG - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t2" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Timepoints - Control
png("results/div_pca_time_control.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$AB_GROUP == "CONTROL", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "TIMEPOINT",
shape = "TIMEPOINT",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.days[1:2],
title = "CONTROL - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "CONTROL", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "CONTROL", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Groups - All Samples
png("results/div_pca_group_all.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa,
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "AB_GROUP",
shape = "AB_GROUP",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.groups,
title = "All Samples - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$AB_GROUP == "CONTROL", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Groups - t0
png("results/div_pca_group_t0.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$TIMEPOINT == "t0", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "AB_GROUP",
shape = "AB_GROUP",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.groups,
title = "t0 - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t0" & data.pcoa$AB_GROUP == "CONTROL", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Groups - t1
png("results/div_pca_group_t1.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$TIMEPOINT == "t1", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "AB_GROUP",
shape = "AB_GROUP",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.groups,
title = "t1 - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t1" & data.pcoa$AB_GROUP == "CONTROL", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Groups - t2
png("results/div_pca_group_t2.png", width = 13, height = 13, units = "cm", res = 500)
ggscatter(data.pcoa[data.pcoa$TIMEPOINT == "t2", ],
x = "Axis.1",
y = "Axis.2",
xlab = paste("PC1 (", eigenvalue_pc1, "%)", sep = ""),
ylab = paste("PC2 (", eigenvalue_pc2, "%)", sep = ""),
xlim = c(-0.5, 0.25),
ylim = c(-0.25, 0.4),
color = "AB_GROUP",
shape = "AB_GROUP",
#star.plot = TRUE,
#mean.point = TRUE,
ellipse = TRUE,
ellipse.alpha = 0.3,
ellipse.border.remove = TRUE,
ellipse.type = "convex",
palette = colours.groups[1:2],
title = "t2 - PCA (Bray–Curtis Dissimilarity)"
) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t2" & data.pcoa$AB_GROUP == "SSG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
chull.raw <- data.pcoa[data.pcoa$TIMEPOINT == "t2" & data.pcoa$AB_GROUP == "5DG", 1:2]
chull.hpts <- chull(x = chull.raw$Axis.1, y = chull.raw$Axis.2)
chull.hpts <- c(chull.hpts, chull.hpts[1])
chull.coords <- chull.raw[chull.hpts,]
chull.poly <- Polygon(chull.coords, hole = F)
chull.area <- chull.poly@area
chull.area
# Boxplots (Groupwise)
boxplot.groups <- list(c("SSG", "5DG"))
png("results/div_box_group_shan.png", width = 20, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP != "CONTROL",], aes(x = AB_GROUP, y = diversity_shannon, fill = AB_GROUP)) +
geom_boxplot(alpha = 0.5) +
geom_jitter(alpha = 0.5) +
facet_wrap(~TIMEPOINT, scale = "free") +
coord_cartesian(ylim = c(4.6, 7.4)) +
scale_y_continuous(breaks = c(5, 6, 7)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.groups) +
stat_compare_means(comparisons = boxplot.groups,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(6.9, 7.1, 7.3),
size = 3,
paired = FALSE)
dev.off()
png("results/div_box_group_even.png", width = 20, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP != "CONTROL",], aes(x = AB_GROUP, y = evenness_simpson, fill = AB_GROUP)) +
geom_boxplot(alpha = 0.5) +
geom_jitter(alpha = 0.5) +
facet_wrap(~TIMEPOINT, scale = "free") +
coord_cartesian(ylim = c(0.02, 0.27)) +
scale_y_continuous(breaks = c(0.05, 0.15, 0.25)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.groups) +
stat_compare_means(comparisons = boxplot.groups,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(0.22, 0.24, 0.26),
size = 3,
paired = FALSE)
dev.off()
# Boxplots (Timewise)
boxplot.timepoints <- list(c("t0", "t1"), c("t1", "t2"), c("t0","t2"))
png("results/div_box_time_shan_group.png", width = 20, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP != "CONTROL",], aes(x = TIMEPOINT, y = diversity_shannon, fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(4.6, 7.4)) +
scale_y_continuous(breaks = c(5, 6, 7)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(6.9, 7.1, 7.3),
size = 3,
paired = TRUE)
dev.off()
png("results/div_box_time_shan_control.png", width = 10, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[(data.alpha.rarefy$AB_GROUP == "CONTROL") & (data.alpha.rarefy$TIMEPOINT != "t2"),], aes(x = TIMEPOINT, y = diversity_shannon, fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(4.6, 7.4)) +
scale_y_continuous(breaks = c(5, 6, 7)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints[1],
alternative = "two.sided",
method = "wilcox.test",
label.y = c(6.9, 7.1, 7.3),
size = 3,
paired = TRUE)
#fixed_control <- data.alpha.rarefy[(data.alpha.rarefy$AB_GROUP == "CONTROL") & (data.alpha.rarefy$TIMEPOINT != "t2"),]
#names(fixed_control)[names(fixed_control) == "AB_GROUP"] <- "PAP"
#ggplot(fixed_control, aes(x = TIMEPOINT, y = diversity_shannon, fill = PAP)) +
# geom_boxplot(alpha = 0.5) +
# geom_jitter(alpha = 0.5) +
# facet_wrap(~PAP, scale = "free") +
# coord_cartesian(ylim = c(4.6, 7.4)) +
# scale_y_continuous(breaks = c(5, 6, 7)) +
# stat_boxplot(geom = "errorbar", width = 0.5) +
# scale_fill_manual(values = colours.groups) +
# stat_compare_means(comparisons = boxplot.timepoints[1],
# alternative = "two.sided",
# method = "wilcox.test",
# label.y = c(6.9, 7.1, 7.3),
# size = 3,
# paired = TRUE)
dev.off()
png("results/div_box_time_even_group.png", width = 20, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[data.alpha.rarefy$AB_GROUP != "CONTROL",], aes(x = TIMEPOINT, y = evenness_simpson, fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(0.02, 0.27)) +
scale_y_continuous(breaks = c(0.05, 0.15, 0.25)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(0.22, 0.24, 0.26),
size = 3,
paired = TRUE)
dev.off()
png("results/div_box_time_even_control.png", width = 10, height = 10, units = "cm", res = 500)
ggplot(data.alpha.rarefy[(data.alpha.rarefy$AB_GROUP == "CONTROL") & (data.alpha.rarefy$TIMEPOINT != "t2"),], aes(x = TIMEPOINT, y = evenness_simpson, fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(0.02, 0.27)) +
scale_y_continuous(breaks = c(0.05, 0.15, 0.25)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints[1],
alternative = "two.sided",
method = "wilcox.test",
label.y = c(0.22, 0.24, 0.26),
size = 3,
paired = TRUE)
dev.off()
# Additional tests
# Significant Differences between SSG (t0) and SSG (t1) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT != "t2" & data.alpha.rarefy$AB_GROUP == "SSG",]
stat.df <- data.frame(GROUP = stat.data$TIMEPOINT, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH", paired = TRUE)
stat.res
# Significant Differences between 5DG (t0) and 5DG (t1) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT != "t2" & data.alpha.rarefy$AB_GROUP == "5DG",]
stat.df <- data.frame(GROUP = stat.data$TIMEPOINT, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH", paired = TRUE)
stat.res
# Significant Differences between SSG (t0) and SSG (t2) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT != "t1" & data.alpha.rarefy$AB_GROUP == "SSG",]
stat.df <- data.frame(GROUP = stat.data$TIMEPOINT, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH", paired = TRUE)
stat.res
# Significant Differences between 5DG (t0) and 5DG (t2) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT != "t1" & data.alpha.rarefy$AB_GROUP == "5DG",]
stat.df <- data.frame(GROUP = stat.data$TIMEPOINT, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH", paired = TRUE)
stat.res
# Significant Differences between SSG and Control (t1) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT == "t1" & data.alpha.rarefy$AB_GROUP != "5DG",]
stat.df <- data.frame(GROUP = stat.data$AB_GROUP, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH")
stat.res
# Significant Differences between 5DG and Control (t0) -> no
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT == "t0" & data.alpha.rarefy$AB_GROUP != "SSG",]
stat.df <- data.frame(GROUP = stat.data$AB_GROUP, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH")
stat.res
# Significant Differences between 5DG and Control (t1) -> yes
stat.data <- data.alpha.rarefy[data.alpha.rarefy$TIMEPOINT == "t1" & data.alpha.rarefy$AB_GROUP != "SSG",]
stat.df <- data.frame(GROUP = stat.data$AB_GROUP, AMR = stat.data$diversity_shannon)
stat.res <- pairwise.wilcox.test(as.numeric(stat.df$AMR), stat.df$GROUP, alternative = "two.sided", p.adjust.method = "BH")
stat.res
# Barplots
bar_data_aggregated <- aggregate_top_taxa2(data.rarefy, 9, "Rank2")
bar_data_melted <- psmelt(bar_data_aggregated)
num_taxa <- length(unique(bar_data_melted$OTU))
palette <- distinctColorPalette(num_taxa)
colours.phyla = c("Actinomycetota" = "#e2f6f3",
"Armatimonadota" = "#48a0a1",
"Bacillota" = "#7d97ca",
"Bacteroidota" = "#414770",
"Candidatus Saccharibacteria" = "#b4d6b4",
"Other Phlya" = "#95f8d6",
"Pseudomonadota" = "#a7ccf1",
"Spirochaetota" = "#f7edc0",
"Unclassified" = "#d4d7db",
"Verrucomicrobiota" = "#e4a080"
)
bar_ext_horse = c()
bar_ext_group = c()
bar_ext_time = c()
i = 1
for(e in bar_data_melted$Sample){
current_line = data.alpha.rarefy[rownames(data.alpha.rarefy) == e,]
bar_ext_horse[i] <- current_line$HORSE
bar_ext_group[i] <- as.character(current_line$AB_GROUP)
bar_ext_time[i] <- current_line$TIMEPOINT
i = i + 1
}
bar_data_melted$HORSE <- bar_ext_horse
bar_data_melted$AB_GROUP <- factor(bar_ext_group, levels = groups.order)
bar_data_melted$TIMEPOINT <- bar_ext_time
# Rename OTUs accordingly
bar_data_melted$OTU[bar_data_melted$OTU == "p__"] <- "Unclassified"
bar_data_melted$OTU <- gsub("p__", "", bar_data_melted$OTU)
#bar_data_melted$OTU <- gsub(" 1", "", bar_data_melted$OTU)
bar_data_melted$OTU <- bar_data_melted$OTU
# Individual Horses - 5DG
bar_data_5dg <- bar_data_melted[bar_data_melted$AB_GROUP == "5DG",]
png("results/tax_bar_horses_5dg.png", width = 30, height = 15, units = "cm", res = 500)
ggplot(bar_data_5dg, aes(fill = OTU, y = Abundance, x = TIMEPOINT)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colours.phyla) +
labs(title = "Relative Abundance (5DG)",
x = "Horses",
y = "Relative Abundance (%)",
fill = "Top 10 Phyla"
) +
facet_grid(~ HORSE) +
theme(plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
dev.off()
# Individual Horses - SSG
bar_data_ssg <- bar_data_melted[bar_data_melted$AB_GROUP == "SSG",]
png("results/tax_bar_horses_ssg.png", width = 30, height = 15, units = "cm", res = 500)
ggplot(bar_data_ssg, aes(fill = OTU, y = Abundance, x = TIMEPOINT)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colours.phyla) +
labs(title = "Relative Abundance (SSG)",
x = "Horses",
y = "Relative Abundance (%)",
fill = "Top 10 Phyla"
) +
facet_grid(~ HORSE) +
theme(plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
dev.off()
# Individual Horses - Control
bar_data_control <- bar_data_melted[bar_data_melted$AB_GROUP == "CONTROL",]
png("results/tax_bar_horses_control.png", width = 30, height = 15, units = "cm", res = 500)
ggplot(bar_data_control, aes(fill = OTU, y = Abundance, x = TIMEPOINT)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colours.phyla) +
labs(title = "Relative Abundance (Control)",
x = "Horses",
y = "Relative Abundance (%)",
fill = "Top 10 Phyla"
) +
facet_grid(~ HORSE) +
theme(plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
dev.off()
# Summarized groups (5DG/SSG)
png("results/tax_bar_sum_5dg_ssg.png", width = 15, height = 15, units = "cm", res = 500)
ggplot(bar_data_melted[bar_data_melted$AB_GROUP != "CONTROL", ], aes(fill = OTU, y = Abundance, x = TIMEPOINT)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colours.phyla) +
labs(title = "Mean Abundance (Groups)",
x = "AB Groups",
y = "Relative Abundance (%)",
fill = "Top 10 Phyla"
) +
facet_grid(~ AB_GROUP) +
theme(plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
dev.off()
# Summarized groups (CONTROL)
png("results/tax_bar_sum_control.png", width = 10, height = 15, units = "cm", res = 500)
ggplot(bar_data_melted[bar_data_melted$AB_GROUP == "CONTROL", ], aes(fill = OTU, y = Abundance, x = TIMEPOINT)) +
geom_bar(position = "fill", stat = "identity") +
scale_fill_manual(values = colours.phyla) +
labs(title = "Mean Abundance (Groups)",
x = "AB Groups",
y = "Relative Abundance (%)",
fill = "Top 10 Phyla"
) +
facet_grid(~ AB_GROUP) +
theme(plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
dev.off()
# Percentages of individual taxa
per_taxa_total = sum(bar_data_melted$Abundance)/length(sample_depth)
per_bact_total = sum(bar_data_melted[bar_data_melted$OTU == "Bacteroidota", ]$Abundance)/length(sample_depth)
per_bact_norm = (per_bact_total/per_taxa_total) * 100
per_firm_total = sum(bar_data_melted[bar_data_melted$OTU == "Bacillota", ]$Abundance)/length(sample_depth)
per_firm_norm = (per_firm_total/per_taxa_total) * 100
per_prot_total = sum(bar_data_melted[bar_data_melted$OTU == "Pseudomonadota", ]$Abundance)/length(sample_depth)
per_prot_norm = (per_prot_total/per_taxa_total) * 100
per_spir_total = sum(bar_data_melted[bar_data_melted$OTU == "Spirochaetota", ]$Abundance)/length(sample_depth)
per_spir_norm = (per_spir_total/per_taxa_total) * 100
per_verr_total = sum(bar_data_melted[bar_data_melted$OTU == "Verrucomicrobiota", ]$Abundance)/length(sample_depth)
per_verr_norm = (per_verr_total/per_taxa_total) * 100
per_bact_norm
per_firm_norm
per_prot_norm
per_spir_norm
per_verr_norm
# Abundance heatmap
abundance_matrix <- matrix(0, 8, length(unique(bar_data_melted$OTU)))
abundance_columns <- unique(bar_data_melted$OTU)
abundance_rows <- c("SSG_t0", "5DG_t0", "SSG_t1", "5DG_t1", "SSG_t2", "5DG_t2", "CONTROL_t0", "CONTROL_t1")
j = 1
for (c in abundance_columns){
i = 1
for (r in abundance_rows){
group <- strsplit(r, "_")[[1]][1]
timepoint <- strsplit(r, "_")[[1]][2]
abundance_matrix[i, j] <- mean(bar_data_melted[bar_data_melted$TIMEPOINT == timepoint & bar_data_melted$AB_GROUP == group & bar_data_melted$OTU == c,]$Abundance)
i = i + 1
}
j = j + 1
}
abundance_matrix <- t(abundance_matrix)
rownames(abundance_matrix) <- abundance_columns
colnames(abundance_matrix) <- c("SSG", "5DG", "SSG", "5DG", "SSG", "5DG", "t0", "t1")
png("results/tax_heatmap.png", width = 30, height = 20, units = "cm", res = 500)
Heatmap(log2(abundance_matrix + 1),
cluster_rows = TRUE,
cluster_columns = FALSE,
#col = c("black", "darkred", "red", "orange", "yellow"),
#col = c("black", "red"),
col = c("grey", "orange", "red", "darkred"),
column_split = c(rep("t0", 2), rep("t1", 2), rep("t2", 2), rep("CONTROL", 2)),
row_title = "Top 10 Phyla",
name = "log2(Abundance)",
row_names_gp = gpar(fontsize = 10),
column_names_gp = gpar(fontsize = 10)
)
dev.off()
# Boxplots for abundance of specific families
boxplot.families.biom <- aggregate_top_taxa2(data.rarefy, 22, "Rank5")
boxplot.families.melted <- psmelt(boxplot.families.biom)
box.ext.horse = c()
box.ext.group = c()
box.ext.time = c()
j = 1
for(f in boxplot.families.melted$Sample){
current.line = data.alpha.rarefy[rownames(data.alpha.rarefy) == f,]
box.ext.horse[j] <- current.line$HORSE
box.ext.group[j] <- as.character(current.line$AB_GROUP)
box.ext.time[j] <- current.line$TIMEPOINT
j = j + 1
}
boxplot.families.melted$HORSE <- box.ext.horse
boxplot.families.melted$AB_GROUP <- factor(box.ext.group, levels = groups.order)
boxplot.families.melted$TIMEPOINT <- box.ext.time
G1 <- boxplot.families.melted[boxplot.families.melted$Rank5 == "f__Ruminococcaceae", ]
png("results/tax_box_f_ruminococcaceae_time_group.png", width = 18, height = 10, units = "cm", res = 500)
ggplot(G1[G1$AB_GROUP != "CONTROL",], aes(x = TIMEPOINT, y = log2(Abundance + 1), fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(0, 15.5)) +
scale_y_continuous(breaks = c(0, 5, 10, 15)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(12.5, 13.5, 14.5),
size = 3,
paired = TRUE,
method.args = list(exact = FALSE)) +
ggtitle("Abundance - Ruminococcaceae (Family)") +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
png("results/tax_box_f_ruminococcaceae_time_control.png", width = 11, height = 10, units = "cm", res = 500)
ggplot(G1[G1$AB_GROUP == "CONTROL",], aes(x = TIMEPOINT, y = log2(Abundance + 1), fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(0, 15.5)) +
scale_y_continuous(breaks = c(0, 5, 10, 15)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints[1],
alternative = "two.sided",
method = "wilcox.test",
label.y = c(12.5, 13.5, 14.5),
size = 3,
paired = TRUE,
method.args = list(exact = FALSE)) +
ggtitle("Abundance - Ruminococcaceae (Family)") +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
png("results/tax_box_f_ruminococcaceae_groups.png", width = 18, height = 10, units = "cm", res = 500)
ggplot(G1[G1$AB_GROUP != "CONTROL",], aes(x = AB_GROUP, y = log2(Abundance + 1), fill = AB_GROUP)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~TIMEPOINT, scale = "free") +
coord_cartesian(ylim = c(0, 15.5)) +
scale_y_continuous(breaks = c(0, 5, 10, 15)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.groups) +
stat_compare_means(comparisons = boxplot.groups,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(12.5, 13.5, 14.5),
size = 3,
paired = FALSE,
method.args = list(exact = FALSE)) +
ggtitle("Abundance - Ruminococcaceae (Family)") +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
G2 <- boxplot.families.melted[boxplot.families.melted$Rank5 == "f__Enterobacteriaceae", ]
png("results/tax_box_f_enterobacteriaceae_time_group.png", width = 18, height = 10, units = "cm", res = 500)
ggplot(G2[G2$AB_GROUP != "CONTROL",], aes(x = TIMEPOINT, y = log2(Abundance + 1), fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +
geom_jitter(alpha = 0.5) +
facet_wrap(~AB_GROUP, scale = "free") +
coord_cartesian(ylim = c(0, 15.5)) +
scale_y_continuous(breaks = c(0, 5, 10, 15)) +
stat_boxplot(geom = "errorbar", width = 0.5) +
scale_fill_manual(values = colours.days) +
stat_compare_means(comparisons = boxplot.timepoints,
alternative = "two.sided",
method = "wilcox.test",
label.y = c(12.5, 13.5, 14.5),
size = 3,
paired = TRUE,
method.args = list(exact = FALSE)) +
ggtitle("Abundance - Enterobacteriaceae (Family)") +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
png("results/tax_box_f_enterobacteriaceae_time_control.png", width = 11, height = 10, units = "cm", res = 500)
ggplot(G2[G2$AB_GROUP == "CONTROL",], aes(x = TIMEPOINT, y = log2(Abundance + 1), fill = TIMEPOINT)) +
geom_boxplot(alpha = 0.9) +