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2. Alpha_diversity_and_box plot_Kruskal wallis.R
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2. Alpha_diversity_and_box plot_Kruskal wallis.R
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########### Alpha diversity ###################
library(microbiome)
library(knitr)
##rarefy
bac.rarefy <- rarefy_even_depth(bac.clean.ss, rngseed = FALSE, replace = TRUE, trimOTUs = TRUE, verbose = TRUE)
bac.clean.ss.sub <- subset_samples(bac.clean.ss, MTX == "Control"|MTX=="0"& TNF_inhibitor == "0")
bac.clean.ss.sub <- phyloseq::filter_taxa(bac.clean.ss.sub, function(x) sum(x) != 0, TRUE)
bac.rarefy.sub <-rarefy_even_depth(bac.clean.ss.sub, rngseed = FALSE, replace = TRUE, trimOTUs = TRUE, verbose = TRUE)
fun.rarefy <- rarefy_even_depth(fun.clean.ss, rngseed = FALSE, replace = TRUE, trimOTUs = TRUE, verbose = TRUE)
fun.clean.ss.sub <- subset_samples(fun.clean.ss, MTX == "Control"|MTX=="0"& TNF_inhibitor == "0")
fun.clean.ss.sub <- phyloseq::filter_taxa(fun.clean.ss.sub, function(x) sum(x) != 0, TRUE)
fun.rarefy.sub <-rarefy_even_depth(fun.clean.ss.sub, rngseed = FALSE, replace = TRUE, trimOTUs = TRUE, verbose = TRUE)
sample_data(bac.rarefy) <- sample_data(b.meta)
sample_data(fun.rarefy) <- sample_data(f.meta)
########## Baterial community ###############
tab_all <- microbiome::alpha(bac.rarefy, index = "all")
write.table(tab_all, "Alpha diversity_bac.txt", sep = "\t", row.names = TRUE, quote = TRUE, na = "NA")
ps1.meta <- b.meta
## using Observed OTUs
ps1.meta$Observed <- tab_all$observed
ps1.meta
ps1.meta$simp <- tab_all$evenness_simpson
ps1.meta$simp
ps1.meta$Shannon <- tab_all$diversity_shannon
ps1.meta$Shannon
########Diagnosis
##Richness (Observed OTUs)
max.diversity <- aggregate(ps1.meta$Observed, by = list(ps1.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps1.meta, Diagnosis=='control')$Observed)
histogram(subset(ps1.meta, Diagnosis=='RA')$Observed)
# wilcoxon test
x <- subset(ps1.meta, Diagnosis=='control')$Observed
y <- subset(ps1.meta, Diagnosis=='RA')$Observed
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.9068
p <- ggplot(data = ps1.meta, aes(x=Diagnosis, y=Observed, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Observed OTU \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Shannon
max.diversity <- aggregate(ps1.meta$Shannon, by = list(ps1.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps1.meta, Diagnosis=='control')$Shannon)
histogram(subset(ps1.meta, Diagnosis=='RA')$Shannon)
# wilcoxon test
x <- subset(ps1.meta, Diagnosis=='control')$Shannon
y <- subset(ps1.meta, Diagnosis=='RA')$Shannon
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.8826
p <- ggplot(data = ps1.meta, aes(x=Diagnosis, y=Shannon, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Shannon \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Simpson evenness
max.diversity <- aggregate(ps1.meta$simp, by = list(ps1.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps1.meta, Diagnosis=='control')$simp)
histogram(subset(ps1.meta, Diagnosis=='RA')$simp)
# wilcoxon test
x <- subset(ps1.meta, Diagnosis=='control')$simp
y <- subset(ps1.meta, Diagnosis=='RA')$simp
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.8826
p <- ggplot(data = ps1.meta, aes(x=Diagnosis, y=simp, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Simpson \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
#########Active disease
max.diversity <- aggregate(ps1.meta$Observed, by = list(ps1.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ active_disease, data = ps1.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Observed ~ active_disease,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=active_disease, y=Observed, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
##ANOVA
max.diversity <- aggregate(ps1.meta$Shannon, by = list(ps1.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ active_disease, data = ps1.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(Shannon ~ active_disease,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=active_disease, y=Shannon, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
ps1.meta$simp <- tab_all$evenness_simpson
ps1.meta$simp
##ANOVA
max.diversity <- aggregate(ps1.meta$simp, by = list(ps1.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ active_disease, data = ps1.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ active_disease,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=active_disease, y=simp, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Duration
ps1.meta$Duration <-as.factor(ps1.meta$Duration)
max.diversity <- aggregate(ps1.meta$Observed, by = list(ps1.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ Duration, data = ps1.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Observed ~ Duration,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=Duration, y=Observed, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
max.diversity <- aggregate(ps1.meta$Shannon, by = list(ps1.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ Duration, data = ps1.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(Shannon ~ Duration,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=Duration, y=Shannon, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
max.diversity <- aggregate(ps1.meta$simp, by = list(ps1.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ Duration, data = ps1.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ Duration,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=Duration, y=simp, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
###### treatment2
ps1.meta$treatment2 <-as.factor(ps1.meta$treatment2)
ps1.meta$treatment2 <-factor(ps1.meta$treatment2, levels = c('Control','Not_treated',"MTX_treated", 'TNF_treated', 'Both'))
max.diversity <- aggregate(ps1.meta$Observed, by = list(ps1.meta$treatment2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ treatment2, data = ps1.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Observed ~ treatment2,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=treatment2, y=Observed, fill = treatment2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=0.6)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
max.diversity <- aggregate(ps1.meta$Shannon, by = list(ps1.meta$treatment2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ treatment2, data = ps1.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Shannon ~ treatment2,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=treatment2, y=Shannon, fill = treatment2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=0.6)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
max.diversity <- aggregate(ps1.meta$simp, by = list(ps1.meta$treatment2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ treatment2, data = ps1.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ treatment2,
data=ps1.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps1.meta, aes(x=treatment2, y=simp, fill = treatment2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=0.6)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
########## fungal community ###############
tab_all.f <- microbiome::alpha(fun.rarefy, index = "all")
write.table(tab_all.f, "Alpha diversity_fun.txt", sep = "\t", row.names = TRUE, quote = TRUE, na = "NA")
ps2.meta <- f.meta
## using Observed OTUs
ps2.meta$Observed <- tab_all.f$observed
ps2.meta
##Diagnosis
##Richness (Observed OTUs)
max.diversity <- aggregate(ps2.meta$Observed, by = list(ps2.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps2.meta, Diagnosis=='control')$Observed)
histogram(subset(ps2.meta, Diagnosis=='RA')$Observed)
# wilcoxon test
x <- subset(ps2.meta, Diagnosis=='control')$Observed
y <- subset(ps2.meta, Diagnosis=='RA')$Observed
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.7316
p <- ggplot(data = ps2.meta, aes(x=Diagnosis, y=Observed, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Observed OTU \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Shannon
ps2.meta$Shannon <- tab_all.f$diversity_shannon
ps2.meta$Shannon
max.diversity <- aggregate(ps2.meta$Shannon, by = list(ps2.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps2.meta, Diagnosis=='control')$Shannon)
histogram(subset(ps2.meta, Diagnosis=='RA')$Shannon)
# wilcoxon test
x <- subset(ps2.meta, Diagnosis=='control')$Shannon
y <- subset(ps2.meta, Diagnosis=='RA')$Shannon
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.8475
p <- ggplot(data = ps2.meta, aes(x=Diagnosis, y=Shannon, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Shannon \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Simpson evenness
ps2.meta$simp <- tab_all.f$evenness_simpson
ps2.meta$simp
max.diversity <- aggregate(ps2.meta$simp, by = list(ps2.meta$Diagnosis), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
histogram(subset(ps2.meta, Diagnosis=='control')$simp)
histogram(subset(ps2.meta, Diagnosis=='RA')$simp)
# wilcoxon test
x <- subset(ps2.meta, Diagnosis=='control')$simp
y <- subset(ps2.meta, Diagnosis=='RA')$simp
wilcox.test(x, y, conf.int = TRUE) #p-value = 0.5944
p <- ggplot(data = ps2.meta, aes(x=Diagnosis, y=simp, fill = Diagnosis)) + geom_boxplot(width = 0.8) +
theme_bw() + scale_fill_manual(values=c("control" = "#6699CC", "RA" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Simpson \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Active disease
max.diversity <- aggregate(ps2.meta$Observed, by = list(ps2.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ active_disease, data = ps2.meta)
kw$p.value #0.639939
#library(FSA)
DT = dunnTest(Observed ~ active_disease,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease, y=Observed, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
max.diversity <- aggregate(ps2.meta$Shannon, by = list(ps2.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ active_disease, data = ps2.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Shannon ~ active_disease,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease, y=Shannon, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
max.diversity <- aggregate(ps2.meta$simp, by = list(ps2.meta$active_disease), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ active_disease, data = ps2.meta)
kw$p.value # 0.1181907
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ active_disease,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease, y=simp, fill = active_disease)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=0.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Active disease 2
ps2.meta$active_disease_2 <-as.factor(ps2.meta$active_disease_2)
max.diversity <- aggregate(ps2.meta$Observed, by = list(ps2.meta$active_disease_2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ active_disease_2, data = ps2.meta)
kw$p.value #0.7278378
#library(FSA)
DT = dunnTest(Observed ~ active_disease_2,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease_2, y=Observed, fill = active_disease_2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
max.diversity <- aggregate(ps2.meta$Shannon, by = list(ps2.meta$active_disease_2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ active_disease_2, data = ps2.meta)
kw$p.value #0.9553377
#library(FSA)
DT = dunnTest(Shannon ~ active_disease_2,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease_2, y=Shannon, fill = active_disease_2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
max.diversity <- aggregate(ps2.meta$simp, by = list(ps2.meta$active_disease_2), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ active_disease_2, data = ps2.meta)
kw$p.value # 0.8586115
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ active_disease_2,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=active_disease_2, y=simp, fill = active_disease_2)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Duration
ps2.meta$Duration <-as.factor(ps2.meta$Duration)
max.diversity <- aggregate(ps2.meta$Observed, by = list(ps2.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Observed ~ Duration, data = ps2.meta)
kw$p.value
#library(FSA)
DT = dunnTest(Observed ~ Duration,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=Duration, y=Observed, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Observed OTU\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using Shannon
##ANOVA
max.diversity <- aggregate(ps2.meta$Shannon, by = list(ps2.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(Shannon ~ Duration, data = ps2.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(Shannon ~ Duration,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=Duration, y=Shannon, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Shannon\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
## using evenness Simpson
max.diversity <- aggregate(ps2.meta$simp, by = list(ps2.meta$Duration), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
##Kruskal-Wallis test
kw<-kruskal.test(simp ~ Duration, data = ps2.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ Duration,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=Duration, y=simp, fill = Duration)) + geom_boxplot(width = 0.8) +
theme_bw() +
geom_point(position='jitter',shape=1, alpha=.5)+
xlab(paste('Kruskal-Wallis, P-value = ', kw$p.value))+
ylab("Simpson\n") + theme(aspect.ratio=1.5)+
theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##MTX
##Richness (Observed OTUs)
max.diversity <- aggregate(ps2.meta$Observed, by = list(ps2.meta$MTX), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
ps2.meta$MTX <-as.factor(ps2.meta$MTX)
kw<-kruskal.test(Observed ~ MTX, data = ps2.meta)
kw$p.value
kw$p.value<- round(kw$p.value, 10)
#library(FSA)
DT = dunnTest(simp ~ MTX,
data=ps2.meta,
method="bh")
PT = DT$res
#library(rcompanion)
dunn<-cldList(P.adj ~ Comparison,
data = PT,
threshold = 0.05)
hsd1 <- merge(dunn,max.diversity, by = 'Group')
p <- ggplot(data = ps2.meta, aes(x=MTX, y=Observed, fill = MTX)) + geom_boxplot(width = 0.8) +
theme_bw() + #scale_fill_manual(values=c("0" = "#6699CC", "1" = "#CC9900"))+
geom_point(position='jitter',shape=1, alpha=.5)+theme(aspect.ratio=2)+
xlab(paste('P = ', wilcox.test(unlist(x), unlist(y), conf.int = TRUE)$p.value))+
ylab("Observed OTU \n") + theme(plot.title = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.title.x = element_text(size = 13,hjust = 0.5, face='bold')) +
theme(axis.title.y = element_text(size = 15,hjust = 0.5, face='bold')) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust=0.4,size=12, face='bold',color='black'))+
theme(axis.text.y = element_text(size=15, face='bold',color='black'))+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.position = "none")
p
##Shannon
max.diversity <- aggregate(ps2.meta$Shannon, by = list(ps2.meta$MTX), max)
colnames(max.diversity) <- c("Group", "MaxDiversity")
kw<-kruskal.test(Shannon ~ MTX, data = ps2.meta)