Data_analysis_Psoriasis_study_revisions.Rmd

---
title: "Psoriasis capitis Metagenomics data analysis"
author: "Britta De Pessemier"
date: '2022-07-13'
output: html_document
---

#install packages
```{r setup, include=FALSE} 
library(dplyr)
library(ggplot2)
library(plyr)
library(doBy)
library(survival)
library(coin)
library(ggplot2)
library(lattice)
library(hexbin)
library(randomForest)
library(ggthemes)
library(reshape2)
library(ca)
library(FactoMineR)
library(MASS)
library(grid)
library(missMDA)
library(plotrix)
library(stats)
library(gdata)
library(psych)
library(corrplot)
library(ggsignif)
library(cowplot)
require(reshape2)
library(ggpubr)
library(readr)
library(dplyr)
library(tibble)
library(dplyr)
library(data.table)
library(ggplot2)
library(cowplot)
library(vegan)
library(readr)
library(Maaslin2)
library(tidyr)
library(circlize)
library(ComplexHeatmap)
library(phyloseq)
```

#Fig. 4 and Fig. S4
#1. Load in Metaphlan4 output
#relative abundance of the species

```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )
```


#absolute abundance estimated number of reads species
```{r}
Bacterialspecies <-read.csv2("Datasets/metaphlan4_merged_species_readstats.csv", sep=",",row.names=1, dec=".", header=TRUE)
```

```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```


# In total 738 species were found with Metaphlan4 (q stat =0.1 or 10%)

#2. select 15 most abundant species
```{r}
Bacterialspecies_sum <- Bacterialspecies
selecttop20par <- rowSums(Bacterialspecies)
Bacterialspecies <- rownames_to_column(Bacterialspecies)
Bacterialspecies <- as.data.frame(Bacterialspecies %>% top_n(15,selecttop20par))
#other <- 100-colSums(Bacterialspecies[,-1])
other <- colSums(Bacterialspecies_sum)-colSums(Bacterialspecies[,-1])
rownames(Bacterialspecies) <- Bacterialspecies$rowname
Bacterialspecies <- Bacterialspecies[,-1]
Bacterialspecies <- rbind(Bacterialspecies,other)
rownames(Bacterialspecies)[nrow(Bacterialspecies)] <- "Other"
Bacterialspecies2 <- t(Bacterialspecies)
Bacterialspecies2<-as.data.frame(Bacterialspecies2)

Bacterialspecies2$Sample_name<-rownames(t(Bacterialspecies))
metadata$Sample_name<-rownames(metadata)

Bacterialspecies2 <- inner_join(Bacterialspecies2,metadata, by = c("Sample_name"))

rownames(Bacterialspecies2)<-Bacterialspecies2$Sample_name


Bacterialspecies2  <- melt(Bacterialspecies2,id.vars=colnames(metadata))


Bacterialspecies2$value <- as.numeric(Bacterialspecies2$value)
```

#3. Stacked bar graphs of healthy, lesional and non-lesional participants (relative abundance or absolute estimated read stats)


# relative abundance
```{r}
colors_fix_bacteria <- c("s__Cutibacterium_acnes"="#003c30","s__Cutibacterium_granulosum"="#01665e", "s__Cutibacterium_namnetense"="#35978f", "s__Corynebacterium_aurimucosum"="#80cdc1","s__Corynebacterium_otitidis"="#c7eae5", "s__Staphylococcus_capitis" = "#B0C4DE", "s__Staphylococcus_saccharolyticus"="#8B8878", "s__Staphylococcus_epidermidis"="#f5f5f5",  "s__Staphylococcus_aureus"="#f6e8c3","s__GGB2722_SGB3663"= "#FFEC8B","s__Moraxella_osloensis"="#FFA54F", "s__Kocuria_rhizophila" ="#dfc27d", "s__Malassezia_globosa"="#bf812d","s__Malassezia_restricta"="#8c510a","s__Micrococcus_luteus" ="#543005", "Other"="black" )
```

#adjusted for absolute values
```{r}
colors_fix_bacteria <- c("s__Cutibacterium_acnes"="#003c30","s__Cutibacterium_granulosum"="#01665e", "s__Cutibacterium_namnetense"="#35978f","s__Paracoccus_yeei"="#80cdc1", "s__Staphylococcus_saccharolyticus"="#c7eae5", "s__Staphylococcus_capitis" = "#B0C4DE", "s__Staphylococcus_hominis"="#8B8878", "s__Staphylococcus_epidermidis"="#f5f5f5",  "s__Malassezia_sympodialis"="#f6e8c3","s__GGB2722_SGB3663"= "#FFEC8B","s__Moraxella_osloensis"="#FFA54F", "s__Kocuria_rhizophila" ="#dfc27d", "s__Malassezia_globosa"="#bf812d","s__Malassezia_restricta"="#8c510a","s__Micrococcus_luteus" ="#543005", "Other"="black" )
```


#order
```{r}
variable_levels <- c("s__Cutibacterium_acnes","s__Cutibacterium_granulosum", "s__Cutibacterium_namnetense", "s__Corynebacterium_aurimucosum","s__Corynebacterium_otitidis", "s__Staphylococcus_capitis" ,"s__Staphylococcus_saccharolyticus", "s__Staphylococcus_epidermidis",  "s__Staphylococcus_aureus","s__GGB2722_SGB3663","s__Moraxella_osloensis","s__Kocuria_rhizophila" , "s__Malassezia_globosa","s__Malassezia_restricta","s__Micrococcus_luteus" , "Other")
```
#adjusted for absolute values

```{r}
variable_levels <- c("s__Cutibacterium_acnes","s__Cutibacterium_granulosum", "s__Cutibacterium_namnetense", "s__Paracoccus_yeei","s__Staphylococcus_saccharolyticus", "s__Staphylococcus_capitis" ,"s__Staphylococcus_hominis", "s__Staphylococcus_epidermidis",  "s__Staphylococcus_aureus","s__GGB2722_SGB3663","s__Moraxella_osloensis","s__Kocuria_rhizophila" , "s__Malassezia_globosa","s__Malassezia_restricta","s__Micrococcus_luteus" , "Other")
```


# Correct order healthy samples
```{r}
Bacterialspecies_h<-Bacterialspecies2[Bacterialspecies2$Pool=="Healthy",]

```

```{r}
lvls <- order(Bacterialspecies_h$variable=="s__Cutibacterium_acnes", Bacterialspecies_h$value, decreasing=TRUE)
```
```{r}
Bacterialspecies_h <- Bacterialspecies_h[order(Bacterialspecies_h$variable=="s__Cutibacterium_acnes", Bacterialspecies_h$value, decreasing=TRUE),]

k <- unique(Bacterialspecies_h$Sample_name)

```



#Order 
```{r}
variable_levels <- c("s__Cutibacterium_acnes","s__Cutibacterium_granulosum", "s__Cutibacterium_namnetense", "s__Corynebacterium_aurimucosum","s__Corynebacterium_otitidis", "s__Staphylococcus_capitis" ,"s__Staphylococcus_saccharolyticus", "s__Staphylococcus_epidermidis",  "s__Staphylococcus_aureus","s__GGB2722_SGB3663","s__Moraxella_osloensis","s__Kocuria_rhizophila" , "s__Malassezia_globosa","s__Malassezia_restricta","s__Micrococcus_luteus" , "Other")
```

#Relative abundance
```{r}
healthy_rel <- Bacterialspecies_h %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Pool) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) +  ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'), legend.position = "right") 
```

#Absolute abundance
```{r}
variable_levels <- c("s__Cutibacterium_acnes","s__Cutibacterium_granulosum", "s__Cutibacterium_namnetense", "s__Paracoccus_yeei","s__Staphylococcus_saccharolyticus", "s__Staphylococcus_capitis" ,"s__Staphylococcus_hominis", "s__Staphylococcus_epidermidis",  "s__Malassezia_sympodialis","s__GGB2722_SGB3663","s__Moraxella_osloensis","s__Kocuria_rhizophila" , "s__Malassezia_globosa","s__Malassezia_restricta","s__Micrococcus_luteus" , "Other")
```
```{r}
healthy_rel <- Bacterialspecies_h %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) 
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Pool) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) +  ylab(expression(Log[10]~Absolute~abundance)) + theme(legend.text=element_text(size=18,face='italic'), legend.position = "right") + scale_y_continuous(trans='log10') 
```



# Lesional samples 

# Correct order lesional samples

```{r}
lvls <- order(Bacterialspecies_L$variable=="s__Cutibacterium_acnes", Bacterialspecies_L$value, decreasing=TRUE)
```
```{r}
Bacterialspecies_L <- Bacterialspecies_L[order(Bacterialspecies_L$variable=="s__Cutibacterium_acnes", Bacterialspecies_L$value, decreasing=TRUE),]

k <- unique(Bacterialspecies_L$Sample_name)

```

#relative abundance
```{r}
lesional_rel <- Bacterialspecies_L %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'), legend.position= "right") 
```

#absolute abundance
```{r}
lesional_rel <- Bacterialspecies_L %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab(expression(Log[10]~Absolute~abundance))  + theme(legend.text=element_text(size=18,face='italic'), legend.position= "right") +scale_y_continuous(trans='log10')
```

```{r}
ggsave("barchart_lesional_pp_qstat10_metaphlan4_relative.png", units="in", width=13, height=11, dpi=400, bg="white")
```


# Non-lesional samples

```{r}
Bacterialspecies_NL<-Bacterialspecies2[Bacterialspecies2$Body_site_2=="Non_Lesional",]

```
#Correct order of Non-lesional samples

```{r}
lvls <- order(Bacterialspecies_NL$variable=="s__Cutibacterium_acnes", Bacterialspecies_NL$value, decreasing=TRUE)
```
```{r}
Bacterialspecies_NL <- Bacterialspecies_NL[order(Bacterialspecies_NL$variable=="s__Cutibacterium_acnes", Bacterialspecies_NL$value, decreasing=TRUE),]

k <- unique(Bacterialspecies_NL$Sample_name)

```



```{r}
nonlesional_rel <- Bacterialspecies_NL %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'), legend.position="right") #+ scale_y_continuous(trans='log10')# add for Log10(Absolute abundance) ylab("Relative abundance") #ylab(Log[10]~Absolute~abundance)
```

#absolute abundance
```{r}
nonlesional_rel <- Bacterialspecies_NL %>% 
  dplyr::mutate(
    across(c(Sample_name, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample_name, ~ forcats::fct_relevel(.,k)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  group_by(Sample_name, value) %>% 
  ggplot(aes(x = Sample_name, y = value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =35, face="bold"), axis.title.y = element_text(size=26),axis.title.x = element_text(size=26)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab(Log[10]~Absolute~abundance) + theme(legend.text=element_text(size=18,face='italic'), legend.position="right") + scale_y_continuous(trans='log10')# add for Log10(Absolute abundance) ylab("Relative abundance") #ylab(Log[10]~Absolute~abundance)
```
```{r}
ggsave("barchart_nonlesional_pp_qstat10_metaphlan4_relative.png", units="in", width=13, height=11, dpi=400, bg="white")
```

```{r}
# Add labels to each plot
plot_H_labeled <- healthy_rel + labs(tag = "A") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_L_labeled <- lesional_rel + labs(tag = "B") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_NL_labeled <- nonlesional_rel + labs(tag = "C") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_empty <-ggplot()
```


```{r}
# Combine the legend of the three plots
combined_legend <- cowplot::get_legend(plot_H_labeled)  # Assuming plot_H_labeled has the legend

# Remove the legends from individual plots
plot_H_labeled <- plot_H_labeled + theme(legend.position = "none")
plot_L_labeled <- plot_L_labeled + theme(legend.position = "none")
plot_NL_labeled <- plot_NL_labeled + theme(legend.position = "none")
```



```{r}
# Create the grid with the aligned plots and the combined legend
grid <- plot_grid(plot_H_labeled,
                  plot_grid(plot_L_labeled, plot_NL_labeled, ncol = 2),
                  #combined_legend, #,align = "hv",
                  ncol = 1,
                  rel_heights = c(1, 1, 0.2))
```

#Save Fig. 4
```{r}
# Save the figure with adjusted dimensions and legend settings
ggsave("figure_taxonomy_healthy_lesional_nonlesional_relativeabundance_metaphlan_.png",
       plot = grid,
       units = "in",
       width = 10,  # Adjust the width as needed
       height = 15,  # Adjust the height as needed
       dpi = 300,
       bg = "white",
       scale = 1.5)  # Increase the scale factor to improve legend readability

```


# Beta-diversity Metaphlan4 matrix & differential abundance analysis using MaAsLin2
#Fig. 3 and Fig. 5

```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```

```{r}
Bacterialspecies_2 <- Bacterialspecies
```

```{r}
### Structure metagenomics table

# 1) Divide all values in metagenomics table by 100 to get relative abundances
Bacterialspecies_2 = Bacterialspecies[,1:ncol(Bacterialspecies)]/100

colSums(Bacterialspecies_2)

# 2) In the metagenomics table, rows should be sampleID's and columns taxa
# transpose metagenomics table
Bacterialspecies_2t = as.data.frame(t(Bacterialspecies_2))
#colnames(Bacterialspecies_2t) = Bacterialspecies_2t[1,]
#Bacterialspecies_2t = Bacterialspecies_2t[-1,]
# convert character data frame to numeric
Bacterialspecies_input = data.frame(lapply(Bacterialspecies_2t,as.numeric))
rownames(Bacterialspecies_input) = rownames(Bacterialspecies_2t)
# check all rows sum up to 1
rowSums(Bacterialspecies_input)

```
#INTER: Lesional 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray



```


```{r}
inter_dist_1 <- bray_dist[lower.tri(bray_dist)]
inter_dist_1_no_na <- na.omit(inter_dist_1)
mean_inter_1 <- mean(inter_dist_1_no_na)
```


#INTER: Non-Lesional 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray


```



```{r}
inter_dist_2 <- bray_dist[lower.tri(bray_dist)]
inter_dist_2_no_na <- na.omit(inter_dist_2)
mean_inter_2 <- mean(inter_dist_2_no_na)
```


#INTER: Top-scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray


```

```{r}
inter_dist_3 <- bray_dist[lower.tri(bray_dist)]
inter_dist_3_no_na <- na.omit(inter_dist_3)
mean_inter_3 <- mean(inter_dist_3_no_na)
```

#INTER: Back-scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray


```

```{r}
inter_dist_4 <- bray_dist[lower.tri(bray_dist)]
inter_dist_4_no_na <- na.omit(inter_dist_4)
mean_inter_4 <- mean(inter_dist_4_no_na)
```

#Lesional vs Non-Lesional 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray

# analyse multivariate homogeneity of group dispersions (variances)

anova(betadisper(bray_dist, metadata_filt$Body_site))

# permanova : What is the effect of the body site if you correct for the subject ID?
set.seed(123)
adonis2(bray_dist ~  subjectID + Body_site, data = metadata_filt, method = "bray")




# obtain R.adjusted values
varpart(bray_dist, ~ subjectID, ~ Body_site, data=metadata_filt)

set.seed(123)
adonis_result10 <-adonis2(bray_dist ~  subjectID+Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values5 <- adonis_result10$`Pr(>F)`

p_values5 <- p_values5[1:2]



```
```{r}
intra_dist_1 <- bray_dist[lower.tri(bray_dist)]
intra_dist_1_no_na <- na.omit(intra_dist_1)
mean_intra_1 <- mean(intra_dist_1_no_na)
```

```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_LesionalvsNon_Lesional", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Non_Lesional"))
```




#Top-scalp vs back-scalp 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```

```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray

# analyse multivariate homogeneity of group dispersions (variances)

anova(betadisper(bray_dist, metadata_filt$Body_site))

# permanova
adonis2(bray_dist ~  subjectID + Body_site, data = metadata_filt, method = "bray")


# obtain R.adjusted values
varpart(bray_dist, ~ subjectID, ~ Body_site, data=metadata_filt)



set.seed(123)
adonis_result11 <-adonis2(bray_dist ~  subjectID+Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values6 <- adonis_result11$`Pr(>F)`

p_values6 <- p_values6[1:2]



```

```{r}
intra_dist_2 <- bray_dist[lower.tri(bray_dist)]
intra_dist_2_no_na <- na.omit(intra_dist_2)
mean_intra_2 <- mean(intra_dist_2_no_na)

```
```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_TopscalpvsBackscalp", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Top_scalp"))
```

#Lesional vs top scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray

# analyse multivariate homogeneity of group dispersions (variances)
anova(betadisper(bray_dist, metadata_filt$Body_site))
set.seed(123)
# permanova
adonis2(bray_dist ~ Body_site, data = metadata_filt, method ="bray")



set.seed(123)
adonis_result12 <-adonis2(bray_dist ~  Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values7 <- adonis_result12$`Pr(>F)`

p_values7 <- p_values7[1]




```

```{r}
no_dist_1 <- bray_dist[lower.tri(bray_dist)]
no_dist_1_no_na <- na.omit(no_dist_1)
mean_no_dist_1 <- mean(no_dist_1_no_na)
```


```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_Lesionalvstopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```

#Lesional vs back scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


#Lesional vs Back_scalp
```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray

# analyse multivariate homogeneity of group dispersions (variances)
anova(betadisper(bray_dist, metadata_filt$Body_site))

# permanova
adonis2(bray_dist ~ Body_site, data = metadata_filt, method ="bray")


set.seed(123)
adonis_result13<-adonis2(bray_dist ~  Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values8 <- adonis_result13$`Pr(>F)`

p_values8 <- p_values8[1]



```

```{r}
no_dist_2 <- bray_dist[lower.tri(bray_dist)]
no_dist_2_no_na <- na.omit(no_dist_2)
mean_no_dist_2 <- mean(no_dist_2_no_na)
```


```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_Lesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```

#Non_Lesional vs top scalp


```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```

```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray

# analyse multivariate homogeneity of group dispersions (variances)
anova(betadisper(bray_dist, metadata_filt$Body_site))

# permanova
adonis2(bray_dist ~ Body_site, data = metadata_filt, method ="bray")


set.seed(123)
adonis_result14<-adonis2(bray_dist ~  Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values9 <- adonis_result14$`Pr(>F)`

p_values9 <- p_values9[1]


```

```{r}
no_dist_3 <- bray_dist[lower.tri(bray_dist)]
no_dist_3_no_na <- na.omit(no_dist_3)
mean_no_dist_3 <- mean(no_dist_3_no_na)
```



```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_NonLesionalvstopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```
#Non_lesional vs Back_scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_filt2 = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 




# calculate dissimilarities
bray_dist = vegdist(x = Bacterialspecies_input_filt2, method = "bray") # adjust method jaccard or bray #here you are

# analyse multivariate homogeneity of group dispersions (variances)
anova(betadisper(bray_dist, metadata_filt$Body_site))

# permanova
adonis2(bray_dist ~ Body_site, data = metadata_filt, method ="bray")



set.seed(123)
adonis_result15<-adonis2(bray_dist ~  Body_site, data = metadata_filt, method = "bray")

# Extract the p-values from the adonis result
p_values10 <- adonis_result15$`Pr(>F)`

p_values10 <- p_values10[1]




```
```{r}
no_dist_4 <- bray_dist[lower.tri(bray_dist)]
no_dist_4_no_na <- na.omit(no_dist_4)
mean_no_dist_4 <- mean(no_dist_4_no_na)
```
```{r}
fit_data = Maaslin2(
    input_data = Bacterialspecies_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_metaphlan4qstat10_NonLesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```


#fdr corrected p_values
```{r}

#store all p_values in vector

my_vector <- c(p_values5, p_values6, p_values7, p_values8, p_values9,p_values10)
# Apply FDR correction to the p-values
fdr_corrected_p_values <- p.adjust(my_vector, method = "fdr")

# Print the FDR-corrected p-values
fdr_corrected_p_values

```


```{r}

# Create a matrix with specified data elements and dimensions
matrix_data <- matrix(data = c(mean_inter_1, mean_intra_1, mean_no_dist_1,mean_no_dist_2, NA,mean_inter_2, mean_no_dist_3, mean_no_dist_4, NA, NA,   mean_inter_3,mean_intra_2, NA, NA, NA,  mean_inter_4), nrow = 4, ncol = 4)

# Add column names
col_names <- c("lesional", "non-lesional", "top-scalp", "back-scalp")
colnames(matrix_data) <- col_names

# Add row names
row_names <- c("lesional", "non-lesional", "top-scalp", "back-scalp")
rownames(matrix_data) <- row_names

# Convert matrix to data frame
df <- data.frame(matrix_data)
df$row_names <- row_names

# Reshape the data frame into long format
df_long <- reshape2::melt(df, id.vars = "row_names")

```

#Figure 3A
```{r}
library(ggplot2)
library(RColorBrewer)
# Create a data frame from the matrix
df <- data.frame(row = factor(rep(row_names, times = ncol(matrix_data)), levels = rev(row_names)),
                 col = factor(rep(col_names, each = nrow(matrix_data)), levels = col_names),
                 value = as.vector(matrix_data))

# Create the heatmap using ggplot
heatmap <- ggplot(df, aes(x = col, y = row, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradientn(colors = rev(brewer.pal(11, "BrBG")), na.value = "white") +
  #geom_text(aes(label = round(value, 2)), color = "white") +
  theme_minimal() +
  theme(axis.text.x = element_text(size=18, angle = 45, vjust = 1, hjust = 1),axis.text.y = element_text(size=18),
        panel.grid = element_blank(), legend.title= element_text(size=20), legend.text = element_text(size=15),
        legend.position = "right") +
  labs(fill = "Mean Bray-curtis distance", x = "", y = "")

# Display the heatmap
print(heatmap)

```
#save Fig. 3
```{r}
ggsave("beta-diversity_matrix_rev.png", units="in", width=12, height=7, dpi=400, bg="white")
```


#Fig. 3B: Beta-diversity boxplots
```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```

```{r}
Bacterialspecies_2 <- Bacterialspecies
```

```{r}
### Structure metagenomics table

# 1) Divide all values in metagenomics table by 100 to get relative abundances
Bacterialspecies_2 = Bacterialspecies[,1:ncol(Bacterialspecies)]/100
#Bacterialspecies_2 = cbind(Bacterialspecies$bug, Bacterialspecies_2)
# check
colSums(Bacterialspecies_2)

# 2) In the metagenomics table, rows should be sampleID's and columns taxa
# transpose metagenomics table
Bacterialspecies_2t = as.data.frame(t(Bacterialspecies_2))
#colnames(Bacterialspecies_2t) = Bacterialspecies_2t[1,]
#Bacterialspecies_2t = Bacterialspecies_2t[-1,]
# convert character data frame to numeric
Bacterialspecies_input = data.frame(lapply(Bacterialspecies_2t,as.numeric))
rownames(Bacterialspecies_input) = rownames(Bacterialspecies_2t)
# check all rows sum up to 1
rowSums(Bacterialspecies_input)

```

#INTER: Lesional 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_lesional = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist_lesional = vegdist(x = Bacterialspecies_input_lesional, method = "bray") # adjust method jaccard or bray


```

#INTER: Non_Lesional 

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_nonlesional = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist_nonlesional = vegdist(x = Bacterialspecies_input_nonlesional, method = "bray") # adjust method jaccard or bray


```


#INTER: Top_scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_topscalp = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist_topscalp= vegdist(x = Bacterialspecies_input_topscalp, method = "bray") # adjust method jaccard or bray


```

#INTER: Back_scalp

```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
Bacterialspecies_input_filt = Bacterialspecies_input
```


```{r}
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)


# select sample ids in metagenomics table
Bacterialspecies_input_backscalp = Bacterialspecies_input_filt[rownames(Bacterialspecies_input_filt) %in% keep, ] 

# calculate dissimilarities
bray_dist_backscalp = vegdist(x = Bacterialspecies_input_backscalp, method = "bray") # adjust method jaccard or bray


```

```{r}
# Flatten the dissimilarity matrices into vectors
vec_group1 <- as.vector(bray_dist_lesional)
vec_group2 <- as.vector(bray_dist_nonlesional)
vec_group3 <- as.vector(bray_dist_topscalp)
vec_group4 <- as.vector(bray_dist_backscalp)
```


```{r}
# Assuming you have the following dissimilarity matrices
dissimilarity_matrices <- list(
  Lesional = bray_dist_lesional,
  Non_Lesional = bray_dist_nonlesional,
  Top_scalp= bray_dist_topscalp,
  Back_scalp = bray_dist_backscalp
)

# Create a list to store flattened dissimilarity vectors and group labels
dissimilarity_list <- list()
group_labels <- character()

# Flatten the dissimilarity matrices and store in the list
for (group in names(dissimilarity_matrices)) {
  dissimilarity_vector <- as.vector(dissimilarity_matrices[[group]])
  dissimilarity_list[[group]] <- dissimilarity_vector
  group_labels <- c(group_labels, rep(group, length(dissimilarity_vector)))
}

# Create a dataframe
dissimilarity_df <- data.frame(
  Dissimilarity = unlist(dissimilarity_list),
  Body_site= group_labels
)

# Print the first few rows of the dataframe
print(head(dissimilarity_df))

```
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```


```{r}
dissimilarity_df$Body_site <- factor(dissimilarity_df$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))
```

# Fig. 3B - Beta diversity
```{r}

log_decontam<-ggplot(subset(dissimilarity_df ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = Dissimilarity, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) +#scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Bray-Curtis Dissimilarity") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=18),axis.text.y=element_text(size=14),axis.text.x=element_text(size=14))+guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=4)
  
```


#save Fig. 3B
```{r}
ggsave("Psoriasiscapitis_bray_curtisdissimilarity_linear.png", units="in", width=6, height=7, dpi=400, bg="white")
```

```{r}
dissimilarity_df$Body_site <- factor(dissimilarity_df$Body_site,     # Reorder factor levels
                         c("Lesional", "Non_Lesional", "Top_scalp", "Back_scalp"))
```
```{r}

log_decontam<-ggplot(subset(dissimilarity_df ,  Body_site %in% c("Lesional", "Non_Lesional", "Top_scalp", "Back_scalp")),aes(y = Dissimilarity, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  #geom_point(position = position_jitter(width = 0.25), pch=21) +#scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Bray-Curtis Dissimilarity") + xlab("") +  scale_fill_manual(values=c("#a6611a", "#dfc27d","#018571", "#80cdc1"))  +  theme(axis.title=element_text(size=18),axis.text.y=element_text(size=14),axis.text.x=element_text(size=14))+guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=4)
  
```

#Fig.3 a Revisions
```{r}
ggsave("Fig.3a_revisions.png", units="in", width=6, height=7, dpi=400, bg="white")
```

# Fig S.5: Beta-diversity PCoA plots


```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )
mpa_table<- Bacterialspecies
```
```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```

```{r}
colSums(mpa_table)

```
```{r}
### Data transformation
mpa_table <- mpa_table / 100

```


```{r cars}

bug_mat = mpa_table|> 
  as.matrix() |>
  t()

```


# Change method to jaccard vs bray-curtis
```{r pressure, echo=FALSE}
#dist_mat = vegdist(bug_mat)
dist_mat = vegdist(bug_mat, method="jaccard")
cmd_res = cmdscale(dist_mat, 
                   k = (nrow(bug_mat) - 1),
                   eig = TRUE)

str(cmd_res)
```



```{r}
pcoa_df = tibble(PC1 = cmd_res$points[,1], 
                 PC2 = cmd_res$points[,2])
```


```{r}
eigenvalues <- cmd_res$eig
total_variance <- sum(eigenvalues)
variance_PC1 <- eigenvalues[1]
variance_PC2 <- eigenvalues[2]
percent_explained_PC1 <- (variance_PC1 / total_variance) * 100
percent_explained_PC2 <- (variance_PC2 / total_variance) * 100
```

```{r}
# Create a label for PC1 and PC2
pc1_label <- paste("PCoA[1]", sprintf("%.2f%%", percent_explained_PC1))
pc2_label <- paste("PCoA[2]", sprintf("%.2f%%", percent_explained_PC2))

```


```{r}
pcoa_meta = bind_cols(pcoa_df, metadata)

pcoa_meta
```



```{r}
pcoa_meta_new <- pcoa_meta                             # Duplicate data
pcoa_meta_new$Body_site <- factor(pcoa_meta_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))
```

```{r}
p_diag = ggplot(pcoa_meta_new,
                aes(x = PC1, y = PC2, color = Body_site)) + geom_point(aes(shape = Pool, fill=Body_site), size = 3) +  scale_colour_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))+ labs(title = "PCoA - Jaccard") +labs(x = pc1_label, y = pc2_label)

p_diag
```
```{r}
p_jaccard <- p_diag

p_bray <- p_diag
```

```{r}
figure <- ggarrange(p_jaccard, p_bray, ncol = 2, nrow = 1,common.legend = TRUE, legend ="right",align="hv", labels=c("A","B"), font.label=list(size=17))
```

save Fig S.5 
```{r}
ggsave("bray_curtis_jaccard_PCoA.png", units="in", width=10, height=5, dpi=400, bg="white")
```

# Revised PCoA plot - Revisions 
```{r}


# Define the plot with adjusted label sizes
p_diag <- ggplot(pcoa_meta_new,
                 aes(x = PC1, y = PC2, color = Body_site)) +
          geom_point(aes(shape = Pool, fill = Body_site), size = 3) +
          scale_colour_manual(values = c("#018571", "#80cdc1", "#a6611a", "#dfc27d")) +
          labs(x = pc1_label, y = pc2_label) +
          theme(
            panel.background = element_rect(fill = "white", color = NA),
            panel.grid.major = element_line(color = "white"),
            panel.grid.minor = element_line(color = "white"),
            axis.line = element_line(color = "black"),
            axis.ticks = element_line(color = "black"),
            axis.text = element_text(color = "black", size = 7),
            axis.title = element_text(color = "black", size = 7),
            legend.position = "none"
            #plot.title = element_text(size = 7, hjust = 0.5),
            #legend.text = element_text(size = 7),
            #legend.title = element_text(size = 7)
          )

# Display the plot
print(p_diag)

# Save the plot with the specified dimensions and resolution
ggsave("bray_curtis_PCoA_revisions14May20242.png", plot = p_diag, units = "in", width = 3.54, height = 4.33, dpi = 600, bg = "white")

```

```{r setup, include=FALSE}
library(tidyr)
library(ComplexHeatmap)
library(circlize)
```

## Fig.5 Complex heatmap on the output of the Metaphlan4 species output
# Heatmap - species over and under-represented in lesional and/or non-lesional skin compared to top and back scalp

```{r}
data_plot_arranged = data.table::fread("Datasets/maaslin2_18082023.csv", dec=",")
myplot = data_plot_arranged

```


```{r pressure, echo=FALSE}
# create matrix for heatmap
plot_matrix = as.data.frame(cbind(myplot$feature, myplot$value, myplot$heatmap_value))
names(plot_matrix) = c("feature", "value", "Heatmap_value")

```


```{r}
# transform from long format to wide format
plot_matrix_wide <- spread(plot_matrix, value, Heatmap_value)
plot_matrix_wide <- plot_matrix_wide %>% remove_rownames %>% column_to_rownames(var="feature")

plot_matrix_wide2 = apply(as.matrix(plot_matrix_wide), 2, as.numeric)
rownames(plot_matrix_wide2) = rownames(plot_matrix_wide)
```

```{r}
# arrange column order for plot
col.order <- c( "Lesional vs Back_scalp", "Lesional vs Top_scalp", "Lesional vs Non_Lesional", "Non_Lesional vs Back_scalp", "Non_Lesional vs Top_scalp", "Top_scalp vs Back_scalp")
plot_matrix_wide2 = plot_matrix_wide2[,col.order]
```

library(circlize)

```{r}
col_fun = colorRamp2(c(-7, 0, 7), c("blue", "#EEEEEE", "red"), space = "RGB")
```

```{r}
# replace NA by 0 in df for clustering
plot_matrix_wide3 = plot_matrix_wide2
plot_matrix_wide3[is.na(plot_matrix_wide3)] <- 0
```
```{r}
plot_matrix2 = as.data.frame(cbind(myplot$feature,myplot$value, myplot$qval))
names(plot_matrix2) = c("feature","value", "qval")
```

```{r}
# transform from long format to wide format
matrix_anno_wide <- spread(plot_matrix2, value, qval)
matrix_anno_wide <- matrix_anno_wide %>% remove_rownames %>% column_to_rownames(var="feature")
matrix_anno_wide2 = apply(as.matrix(matrix_anno_wide), 2, as.numeric)
rownames(matrix_anno_wide2) = rownames(matrix_anno_wide)
```
```{r}
matrix_anno_wide2 = matrix_anno_wide2[,col.order]
matrix_anno_wide3 = matrix_anno_wide2
matrix_anno_wide3[is.na(matrix_anno_wide3)] <- 1
```
library(ComplexHeatmap)

```{r}
# adjust main legend here with heatmap_legend_param
p = Heatmap(plot_matrix_wide3, name = "-sign(beta)*log(qval)", col = col_fun, na_col = "white", 
            #clustering_distance_rows = "pearson",
            row_dend_side = "left", row_dend_width = unit(1, "cm"), 
            show_column_dend = FALSE,
            row_names_side = "right", 
            rect_gp = gpar(col = "white", lwd = 2),
            border_gp = gpar(col = "black", lty = 1),
            row_names_gp = gpar(fontsize = 8, fontface = "italic"),
            column_names_gp = gpar(fontsize = 8), column_names_rot = -60,  # this includes the annotation of the graph!
            width = ncol(plot_matrix_wide3)*unit(8, "mm"), # adjust cell size in heatmap
            height = nrow(plot_matrix_wide3)*unit(4, "mm"), # adjust cell size in heatmap
            row_title = "Species (N=15)",
            row_title_gp = gpar(fontsize = 8, fontface = 2),
            row_title_side = "left",
            heatmap_legend_param = list(title_gp = gpar(fontsize = 8,fontface = 2), labels_gp = gpar(fontsize = 8), at = c(-7, -3.5, 0, 3.5, 7),legend_direction = "horizontal", border='black') ,
            column_order = colnames(plot_matrix_wide2),
            cell_fun = function(j, i, x, y, w, h, fill) {
              if(matrix_anno_wide3[i, j] < 0.001) {
                grid.text('***', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.01){
                grid.text('**', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.05) {
                grid.text('*', x, y-h*0.01, gp = gpar(fontsize = 6))
              }}
)
p = draw(p, heatmap_legend_side="left", annotation_legend_side="left") 
p 

```
#save Fig. 5

```{r}
tiff("Figure5.tiff", units='mm', width=140, height=100, res=450, compression="lzw")
p
dev.off()
```



# Fig. S7. Create pie-charts of Metaphlan4 data 

# Metadata
```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```

#Load in metaphlan species data with q stat = 10%

#Species level: 738 species were found 

```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )
```

# Metaphlan4 Genera data 
```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_genera_relab.csv", sep=",",dec=".", row.names =1, header= TRUE )
```
# Metaphlan4 Kingdom data
```{r}

Bacterialspecies <- read.csv2("Datasets/metaphlan4__kingdom_relab.csv", sep=",",dec=".", row.names =1, header= TRUE )

```
# Metaphlan4 Kingdom data including unclassified fraction
```{r}

Bacterialspecies <- read.csv2("Datasets/metaphlan4_kingdom_unclassified.csv", sep=",",dec=".", row.names =1, header= TRUE )

```
#Metaphlan3 v31 Kingdom data including unclassified data
```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan3_unclassified_v31_kingdom.csv", sep=",",dec=".", row.names =1, header= TRUE )
```


#Metaphlan3 v30 Kingdom data including unclassified data 
```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan3_unclassified_v30_kingdom.csv", sep=",",dec=".", row.names =1, header= TRUE )
```

```{r}

# not necessary to select top20 when working with kingdom data!
selecttop20par <- rowSums(Bacterialspecies)

Bacterialspecies <- rownames_to_column(Bacterialspecies)
Bacterialspecies <- as.data.frame(Bacterialspecies %>% top_n(15,selecttop20par)) #15 in case of species level, 10 in case of genus level
other <- 100-colSums(Bacterialspecies[,-1])
rownames(Bacterialspecies) <- Bacterialspecies$rowname
Bacterialspecies <- Bacterialspecies[,-1]
Bacterialspecies <- rbind(Bacterialspecies,other)
rownames(Bacterialspecies)[nrow(Bacterialspecies)] <- "Other"

#when working with kingdom data start here
Bacterialspecies2 <- as.data.frame(t(Bacterialspecies))


Bacterialspecies2$Staalnummer<-rownames(t(Bacterialspecies))
metadata$Staalnummer <- rownames(metadata)

metadata_subset <- metadata
metadata_subset <- subset(metadata, select = -c(Study:DNAExtraction_method))

Bacterialspecies2 <- inner_join(Bacterialspecies2, metadata_subset, by = c("Staalnummer"))

rownames(Bacterialspecies2)<-Bacterialspecies2$Staalnummer


Bacterialspecies3<-Bacterialspecies2


OTUtable_av <- ddply(Bacterialspecies2,.(Body_site),numcolwise(mean,na.rm = TRUE))
    rownames(OTUtable_av) <- OTUtable_av[,1]

OTUtable_av2  <- reshape2::melt(OTUtable_av,id.vars="Body_site")
OTUtable_av2$value <- as.numeric(OTUtable_av2$value)


```



# to obtain the standard deviation
```{r}
OTUtable_sd <- ddply(Bacterialspecies2, .(Body_site), numcolwise(sd, na.rm = TRUE))


```

# Save the DataFrame to a CSV file with a semicolon separator
```{r}
write.csv2(OTUtable_av, file = "averages_species.csv", row.names = FALSE)
write.csv2(OTUtable_sd, file = "sd_species.csv", row.names = FALSE)
```


# check for the unclassified fraction if it is significantly different between all four groups

```{r}
metadata_3_new <- Bacterialspecies2                             # Duplicate data
metadata_3_new$Body_site <- factor(metadata_3_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))

rownames(metadata_3_new) <- metadata_3_new$Sample_name
```
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```
```{r}
a_micr<-ggplot(subset(metadata_3_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = UNCLASSIFIED, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + #scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("% Unclassified") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))   +theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)
```
```{r}
ggsave("unclassified_percentage.png", units="in", width=16, height=10, dpi=400, bg="white")
```


# kingdom level

```{r}
colors_fix_bacteria <- c("k__Bacteria"="#5ab4ac","k__Archaea"="#f5f5f5", "k__Eukaryota"="#d8b365")
```
# kingdom level with an unclassified fraction
```{r}
colors_fix_bacteria <- c("k__Bacteria"="#5ab4ac","UNCLASSIFIED"="black", "k__Eukaryota"="#d8b365")
```

# Kingdom level
```{r}

bacteria_top<-OTUtable_av2[OTUtable_av2$Body_site=="Top_scalp",]
bacteria_top$value <- round(bacteria_top$value, digits=1)

h2<- ggplot(bacteria_top, aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) +
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Top scalp") +theme(legend.text = element_text(size=15), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 

bacteria_back<-OTUtable_av2[OTUtable_av2$Body_site=="Back_scalp",]
bacteria_back$value <- round(bacteria_back$value, digits=1)

h1<- ggplot(bacteria_back, aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Back scalp") +theme(legend.text = element_text(size=15), title =element_text(size=20), plot.title = element_text(hjust = 0.5))
bacteria_les<-OTUtable_av2[OTUtable_av2$Body_site=="Lesional",]
bacteria_les$value <- round(bacteria_les$value, digits=1)

P1<- ggplot(bacteria_les, aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Lesional") +theme(legend.text = element_text(size=15), title =element_text(size=20), plot.title = element_text(hjust = 0.5))

bacteria_nonles<-OTUtable_av2[OTUtable_av2$Body_site=="Non_Lesional",]
bacteria_nonles$value <- round(bacteria_nonles$value, digits=1)

P2<- ggplot(bacteria_nonles, aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Non-Lesional") +theme(legend.text = element_text(size=15), title =element_text(size=20), plot.title = element_text(hjust = 0.5))


figure <- ggarrange(h2, h1, P2, P1,  
                    ncol = 4, nrow = 1, legend = "right",common.legend = TRUE)
```

```{r}
ggsave("kingdom_piechart_metaphlan4_unclass.png", units="in", width=40, height=10, dpi=400, bg="white")
```


# genus level

```{r}
colors_fix_bacteria <- c("g__Cutibacterium"="#003c30","g__Corynebacterium"="#01665e", "g__Porphyromonas"="#80cdc1", "g__Acinetobacter" = "#c7eae5",  "g__Staphylococcus"="#f6e8c3","g__GGB2722"= "#FFEC8B", "g__Kocuria" ="#dfc27d","g__Malassezia"="#bf812d","g__Moraxella"="#8c510a","g__Micrococcus" ="#543005" , "Other"="black" )
```

```{r}
variable_levels <- c("g__Cutibacterium","g__Corynebacterium", "g__Porphyromonas", "g__Acinetobacter",  "g__Staphylococcus","g__GGB2722", "g__Kocuria","g__Malassezia","g__Moraxella","g__Micrococcus" , "Other")
```

```{r}
bacteria_top<-OTUtable_av2[OTUtable_av2$Body_site=="Top_scalp",]
bacteria_top$value <- round(bacteria_top$value, digits=1)


h2 <- bacteria_top %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Top scalp") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 

bacteria_back<-OTUtable_av2[OTUtable_av2$Body_site=="Back_scalp",]
bacteria_back$value <- round(bacteria_back$value, digits=1)

h1 <- bacteria_back %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Back scalp") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 



bacteria_les<-OTUtable_av2[OTUtable_av2$Body_site=="Lesional",]
bacteria_les$value <- round(bacteria_les$value, digits=1)

P1<-  bacteria_les %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Lesional") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 
bacteria_nonles<-OTUtable_av2[OTUtable_av2$Body_site=="Non_Lesional",]
bacteria_nonles$value <- round(bacteria_nonles$value, digits=1)

P2<-  bacteria_nonles %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Non-Lesional") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 
```

```{r}
figure <- ggarrange(h2, h1, P2, P1,  
                    ncol = 4, nrow = 1, legend = "right", common.legend = TRUE)
```

```{r}
ggsave("genera_piechart_qstat10_metaphlan4.png", units="in", width=17, height=11, dpi=300, bg="white")
```


#species level

#color code relative abundance species
```{r}
colors_fix_bacteria <- c("s__Cutibacterium_acnes"="#003c30","s__Cutibacterium_granulosum"="#01665e", "s__Cutibacterium_namnetense"="#35978f", "s__Corynebacterium_aurimucosum"="#80cdc1","s__Corynebacterium_otitidis"="#c7eae5", "s__Staphylococcus_capitis" = "#B0C4DE", "s__Staphylococcus_saccharolyticus"="#8B8878", "s__Staphylococcus_epidermidis"="#f5f5f5",  "s__Staphylococcus_aureus"="#f6e8c3","s__GGB2722_SGB3663"= "#FFEC8B","s__Moraxella_osloensis"="#FFA54F", "s__Kocuria_rhizophila" ="#dfc27d", "s__Malassezia_globosa"="#bf812d","s__Malassezia_restricta"="#8c510a","s__Micrococcus_luteus" ="#543005", "Other"="black" )
```


#order relative abundance species
```{r}
variable_levels <- c("s__Cutibacterium_acnes","s__Cutibacterium_granulosum", "s__Cutibacterium_namnetense", "s__Corynebacterium_aurimucosum","s__Corynebacterium_otitidis", "s__Staphylococcus_capitis" ,"s__Staphylococcus_saccharolyticus", "s__Staphylococcus_epidermidis",  "s__Staphylococcus_aureus","s__GGB2722_SGB3663","s__Moraxella_osloensis","s__Kocuria_rhizophila" , "s__Malassezia_globosa","s__Malassezia_restricta","s__Micrococcus_luteus" , "Other")
```

```{r}
bacteria_top<-OTUtable_av2[OTUtable_av2$Body_site=="Top_scalp",]
bacteria_top$value <- round(bacteria_top$value, digits=1)


h2 <- bacteria_top %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Top scalp") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 


bacteria_back<-OTUtable_av2[OTUtable_av2$Body_site=="Back_scalp",]
bacteria_back$value <- round(bacteria_back$value, digits=1)


h1 <- bacteria_back %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Back scalp") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 


bacteria_les<-OTUtable_av2[OTUtable_av2$Body_site=="Lesional",]
bacteria_les$value <- round(bacteria_les$value, digits=1)

P1<-  bacteria_les %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Lesional") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 
bacteria_nonles<-OTUtable_av2[OTUtable_av2$Body_site=="Non_Lesional",]
bacteria_nonles$value <- round(bacteria_nonles$value, digits=1)

P2<-  bacteria_nonles %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  ggplot(aes(x="", y=value, fill=variable)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)  + 
  scale_fill_manual(name='',values=colors_fix_bacteria)+
  theme_void() + ggtitle("Non-Lesional") +theme(legend.text = element_text(size=15, face="italic"), title =element_text(size=20), plot.title = element_text(hjust = 0.5)) 
```

```{r}
figure <- ggarrange(h2, h1, P2, P1,  
                    ncol = 4, nrow = 1, legend = "right", common.legend = TRUE)
```



```{r}
ggsave("species_piechart_qstat10_metaphlan4.png", units="in", width=17, height=11, dpi=300, bg="white")
```

#Fig S3. Commpare absolute number of reads between kingdom


```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, sep=",", dec =";",row.names=1, stringsAsFactors=FALSE)
metadata$Sample <- rownames(metadata)
```


```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_merged_kingdom_readstats.csv", sep=",",dec=".", row.names=1, header= TRUE )

Bacterialspeciest <- (as.data.frame(t(Bacterialspecies)))
Bacterialspeciest$Sample <- rownames(Bacterialspeciest)

```

```{r}
metadata_2 <- inner_join(Bacterialspeciest,metadata, by="Sample")
```

```{r}
metadata_2_new <- metadata_2                             # Duplicate data
metadata_2_new$Body_site <- factor(metadata_2_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))
```


```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```
#Bacteria
```{r}
p<-ggplot(subset(metadata_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = k__Bacteria, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Bacteria)) + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20)) + theme(legend.position="none") + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)

```
#Eukaryota
```{r}
d<-ggplot(subset(metadata_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = k__Eukaryota, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Eukaryota)) + xlab("") +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  + theme(legend.position="none") + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)

```

```{r}
figure <- ggarrange(p,d,ncol=2, nrow=1, align="hv", labels=c("A","B"), font.label=list(size=17))
```
```{r}
ggsave("Psoriasiscapitis_Log10BacteriaFungi.png", units="in", width=18, height=7, dpi=400, bg="white")
```


#Host reads
#Fig S2.

```{r}
hostreads_kneaddata <- read.csv("Datasets/Hostreads_kneaddata.csv", header=TRUE, dec=",", sep=",", stringsAsFactors=FALSE)
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample_name <- rownames(metadata)

hostreads_2 <- inner_join(hostreads_kneaddata,metadata, by="Sample_name")

hostreads_2$Humann_reads <- hostreads_2$trimmed.pair1 - hostreads_2$decontaminated.hg37dec_v0.1.pair1

hostreads_2_new <- hostreads_2                             # Duplicate data
hostreads_2_new$Body_site <- factor(hostreads_2_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))



```

#Compare means
```{r}
input_data_filt <- hostreads_2_new

rownames(input_data_filt) <- input_data_filt$Sample_name
```

```{r}
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)

metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 
```
```{r}
mean_lesional_micr <- mean(input_data_filt$final.pair1)
median_lesional_micr <- median(input_data_filt$final.pair1)
max_lesional_micr <- max(input_data_filt$final.pair1)
min_lesional_micr <- min(input_data_filt$final.pair1)


mean_lesional_Humann_reads <- mean(input_data_filt$Humann_reads)
median_lesional_Humann_reads <- median(input_data_filt$Humann_reads)
max_lesional_Humann_reads <- max(input_data_filt$Humann_reads)
min_lesional_Humann_reads <- min(input_data_filt$Humann_reads)
```
```{r}
input_data_filt <- hostreads_2_new

rownames(input_data_filt) <- input_data_filt$Sample_name
```

```{r}
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 
```
```{r}
mean_nonlesional_micr <- mean(input_data_filt$final.pair1)
median_nonlesional_micr <- median(input_data_filt$final.pair1)
max_nonlesional_micr <- max(input_data_filt$final.pair1)
min_nonlesional_micr <- min(input_data_filt$final.pair1)


mean_nonlesional_Humann_reads <- mean(input_data_filt$Humann_reads)
median_nonlesional_Humann_reads <- median(input_data_filt$Humann_reads)
max_nonlesional_Humann_reads <- max(input_data_filt$Humann_reads)
min_nonlesional_Humann_reads <- min(input_data_filt$Humann_reads)
```

```{r}
input_data_filt <- hostreads_2_new

rownames(input_data_filt) <- input_data_filt$Sample_name
```

```{r}
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 
```
```{r}
mean_topscalp_micr <- mean(input_data_filt$final.pair1)
median_topscalp_micr <- median(input_data_filt$final.pair1)
max_topscalp_micr <- max(input_data_filt$final.pair1)
min_topscalp_micr <- min(input_data_filt$final.pair1)


mean_topscalp_Humann_reads <- mean(input_data_filt$Humann_reads)
median_topscalp_Humann_reads <- median(input_data_filt$Humann_reads)
max_topscalp_Humann_reads <- max(input_data_filt$Humann_reads)
min_topscalp_Humann_reads <- min(input_data_filt$Humann_reads)
```

```{r}
input_data_filt <- hostreads_2_new

rownames(input_data_filt) <- input_data_filt$Sample_name
```

```{r}
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 
```
```{r}
mean_backscalp_micr <- mean(input_data_filt$final.pair1)
median_backscalp_micr <- median(input_data_filt$final.pair1)
max_backscalp_micr <- max(input_data_filt$final.pair1)
min_backscalp_micr <- min(input_data_filt$final.pair1)



mean_backscalp_Humann_reads <- mean(input_data_filt$Humann_reads)
median_backscalp_Humann_reads <- median(input_data_filt$Humann_reads)
max_backscalp_Humann_reads <- max(input_data_filt$Humann_reads)
min_backscalp_Humann_reads <- min(input_data_filt$Humann_reads)
```
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```

```{r}

log_decontam<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = final.pair1, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21, size=3) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Decontaminated~Reads)) + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8, size=6, bracket.size = 0.3, vjust=0)
print(log_decontam)
```
```{r}
log_human<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = Humann_reads, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21, size = 3) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Contaminant~hg37~Reads)) + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8, size=6, bracket.size = 0.3, vjust=0)
```


```{r}
log_trimmed<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = trimmed.pair1, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21, size = 3) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Trimmed~Reads)) + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8, size=6, bracket.size = 0.3, vjust=0)
```

```{r}
percent_nonhuman<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = X.Non.Human, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21, size=3) + 
  theme(panel.grid = element_blank()) + 
  ylab("% Non-Human") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH",  tip.length=0.01, textsize=8, size=6, bracket.size = 0.3, vjust=0)
```

```{r}
percen_human<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = X.Human, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() + 
  geom_point(position = position_jitter(width = 0.25), pch=21, size=3) +
  theme(panel.grid = element_blank()) + 
  ylab("%reads mapped to human") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8, size=6, bracket.size = 0.3, vjust=0)
```

```{r}
figureLOG <- ggarrange(log_trimmed, log_human, percen_human, percent_nonhuman,ncol=2, nrow=2, align="hv", labels=c("A","B", "C", "D"), font.label=list(size=17))
```

#save Fig. S2
```{r}
ggsave("Psoriasiscapitis_kneaddata.png", units="in", width=20, height=15, dpi=400, bg="white")
```


# 16S qPCR data-analysis
# Protocol
qPCR assays were performed using StepOnePlus real-time PCR system. Reactions were performed in a volume of 20 μl consisting of 10.0 μl of 2x iTaq universal SYBR Green supermix (Bio-Rad Laboratories, Hercules, CA, USA), 2.0 μl DNA template, 0.8 μl (10 µM stock) of each primer, and 6.4 μl nuclease-free water. Amplifications were ran as follows: initial denaturation for 2 min at 95°C, followed by 40 cycles of 15 s denaturation at 95°C and combined anneal/extension 1’ at 60°C

At the end of the qPCR run, a melting curve analysis was performed to confirm product specificity (60-95°C, ΔT per 15 s = 0.3°C). Quantification was performed using a standard curve based on known concentrations of DNA standard dilutions from 107 copies µl-1 down to 10 copies µl-1. All qPCR analyses were conducted in triplicate.
```{r Loading libraries,cache=TRUE,warning=FALSE,message=FALSE,echo=FALSE}
library("ggplot2") # plots
library("dplyr") # manipulate data
library("tidyr")
library("knitr")
library("ggsignif")

# library("RColorBrewer") # colors
# library("readxl") # read metadata
```



```{r}
### When downloading the qPCR data, best is to remove the first N lines of system data and save the file as CSV.
### Then upload the .csv to the /data folder on the server.

# Read data
data.plate1 <- read.csv("Datasets/16S_plate1.csv",header = TRUE, row.names = 1, sep=",", dec=".")
data.plate1 <- data.plate1[,1:19]  # select usefull cols
data.plate2 <- read.csv("Datasets/16S_plate2.csv",header = TRUE, row.names = 1, sep=";")
data.plate2 <- data.plate2[,1:19] # select usefull cols
data.plate3 <- read.csv("Datasets/16S_plate3.csv",header = TRUE, row.names = 1, sep=";")
data.plate3 <- data.plate3[,1:19]

data <- rbind(data.plate1, data.plate2, data.plate3) # merge data


# Rename Ct columns
colnames(data)[which(names(data) == "C.")] <- "Ct"
colnames(data)[which(names(data) == "C..Mean")] <- "Ct.Mean"
colnames(data)[which(names(data) == "C..SD")] <- "Ct.SD"

# Crop dataset
data.clean <- data %>% dplyr::select(Sample.Name, Target.Name, Quantity.Mean, Quantity.SD)


# Sort by sample ID
data.clean <- data.clean[order(data.clean$Sample.Name),]

# Convert to numeric
data.clean$Quantity.Mean <- as.numeric(sub(",", ".", data.clean$Quantity.Mean, fixed = TRUE))
data.clean$Quantity.SD <- as.numeric(sub(",", ".", data.clean$Quantity.SD, fixed = TRUE))

# Split of the dilution
data.clean <- data.clean %>%
  separate(Sample.Name, c("ID", "Dilution"), "-")

# Replace dilution factor with actual dilution
data.clean$Dilution <- 10

# Remove samples without a value for concentration
data.clean.samples <- data.clean[complete.cases(data.clean$Quantity.Mean), ]


metadata.tibble <- read.csv("Datasets/16S_metadata.csv",header = TRUE, row.names = 1, sep=",")
metadata.tibble$ID <- rownames(metadata.tibble)

# Print metadata
kable(metadata.tibble, caption = "Metadata QSP samples")


# Merge metadata
meta.samples <- merge(data.clean.samples, metadata.tibble, by = "ID")


# Sort table
sort <- as.vector(metadata.tibble$ID)
meta.samples$ID <- factor(meta.samples$ID, levels = sort)
```
```{r}
p<- ggplot(meta.samples, aes(x=ID, y=Quantity.Mean, group=Sample_type, color=Sample_type)) + 
  geom_point(alpha = 1)+
  geom_errorbar(alpha = 0.3, aes(ymin=Quantity.Mean-Quantity.SD, ymax=Quantity.Mean+Quantity.SD), width=.5)
p <- p + scale_y_continuous(trans='log10')
p <- p + theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title="qPCR analysis QSP", subtitle = "qPCR copies as measured in the DNA extract", x="sample ID", y = "Copies/μl")
p + geom_hline(yintercept=10, linetype="dashed", color = "red") + annotate(geom="text", label="LOD", x=1, y=1, vjust=-6, hjust=0.3, color= "red")

```

```{r}
# Dilution factor 10
# correction factor 50 µL extract --> *50
# 100 cm^2 swabbed --> /100  
# correction factor = 0.5

correctionfactor <- 0.5

meta.samples$Quantity.Mean.Corr <- meta.samples$Quantity.Mean * (meta.samples$Dilution * correctionfactor)
meta.samples$Quantity.SD.Corr <- meta.samples$Quantity.SD * (meta.samples$Dilution * correctionfactor)

p<- ggplot(meta.samples, aes(x=ID, y=Quantity.Mean.Corr, group=Sample_type, color=Sample_type)) + 
  geom_point(alpha = 1)+
  geom_errorbar(alpha = 0.3, aes(ymin=Quantity.Mean.Corr-Quantity.SD.Corr, ymax=Quantity.Mean.Corr+Quantity.SD.Corr), width=.3)
p <- p + scale_y_continuous(trans='log10')
p <- p + theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title="qPCR analysis QSP",subtitle = "qPCR copies recalculated per cm² of swabbed surface", x="sample ID", y = "Copies/cm² scalp")
p + geom_hline(yintercept=100000, linetype="dashed", color = "red") + annotate(geom="text", label="LOD", x=1, y=10000, vjust=-6, hjust=0.3, color= "red")



```


```{r}
library(ggplot2)

# Your ggplot code
qPCR_result <- ggplot(subset(meta.samples, Sample_type %in% c("Lesional", "Non_Lesional")), aes(y = Quantity.Mean.Corr, x = Sample_type, fill = Sample_type)) +  
  scale_y_continuous(trans = 'log10') +
  geom_boxplot(outlier.shape = NA) +  
  theme_bw() +  
  geom_point(position = position_jitter(width = 0.25), pch = 21, size=3) +
  theme(panel.grid = element_blank(), axis.text.x = element_text(size = 20), axis.text.y = element_text(size = 14), axis.title = element_text(size = 21)) +   
  ylab(expression(Log[10] ~ "16S Copies/cm²")) + xlab("") + scale_fill_manual(values = c("#a6611a", "#dfc27d"))  +guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = list(c("Lesional", "Non_Lesional")),method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8,size=6, bracket.size = 0.3, vjust=0)


```

```{r}

log_decontam<-ggplot(subset(hostreads_2_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = final.pair1, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21, size=3) + scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab(expression(Log[10]~Decontaminated~Reads)) + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", tip.length=0, textsize=6, bracket.size = 0.3, vjust=0)
print(log_decontam)
```

```{r}
figure_count <- ggarrange(log_decontam, qPCR_result,ncol=2, nrow=1, align="hv", labels=c("A","B"), widths = c(2, 1), font.label=list(size=17))
```


```{r}
ggsave("Psoriasiscapitis_16S_decontamdata.png", units="in", width=14, height=7, dpi=400, bg="white")
```


# Fig. S6. Alpha-diversity 


# non-rarefied data
```{r}
uparse_otus<-read.csv2("Datasets/metaphlan4_merged_species_readstats.csv", sep=",",row.names=1, dec=",", header=TRUE)
```


```{r}
divdf_metaphlan2 <- data.frame(microbiome::alpha(uparse_otus, index = c("richness_observed")),microbiome::alpha(uparse_otus, index = c("diversity_shannon")), microbiome::alpha(uparse_otus, index = c("diversity_gini_simpson")),microbiome::alpha(uparse_otus, index = c("dominance_gini")))
divdf_metaphlan2 <- as.data.frame(divdf_metaphlan2)

divdf_metaphlan2$Sample_name <- rownames(divdf_metaphlan2)
```

```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample_name <- rownames(metadata)
```

```{r}
metadata_divdf <- inner_join(divdf_metaphlan2,metadata, by="Sample_name")

rownames(metadata_divdf) <- metadata_divdf$Sample_name
```

```{r}
metadata_divdf_new <- metadata_divdf                            # Duplicate data
metadata_divdf_new$Body_site <- factor(metadata_divdf$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))
```
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```

```{r}
a_micr<-ggplot(subset(metadata_divdf_new,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = observed, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + 
  theme(panel.grid = element_blank()) + 
  ylab("Richness observed") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)


b_micr<-ggplot(subset(metadata_divdf_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = diversity_shannon, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + 
  theme(panel.grid = element_blank()) + 
  ylab("Shannon") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)


c_micr<-ggplot(subset(metadata_divdf_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = diversity_gini_simpson, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + 
  theme(panel.grid = element_blank()) + 
  ylab("Simpson") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)


d_micr<-ggplot(subset(metadata_divdf_new,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = dominance_gini, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + 
  theme(panel.grid = element_blank()) + 
  ylab("Dominance Gini") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)
```
```{r}
figure_notrare <- ggarrange(a_micr,b_micr, c_micr, d_micr, ncol=2, nrow=2, align="hv", labels=c("A","B", "C", "D"), font.label=list(size=17))
```
```{r}
ggsave("alphadiverisity_notrarefied_metaphlan4.png", units="in", width=20, height=15, dpi=400, bg="white")
```

# Fig. S8. Humann3.6 mapped average plots

```{r}
input_data <-read.csv2("humann36_pathabundance_relab.csv", header = TRUE, sep = ",",  dec = ".", stringsAsFactors = FALSE, row.names=1)
```
```{r}
input_metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```



```{r}
unstrat_pathways <-function(dat_path){
  temp = dat_path[!grepl("\\|",rownames(dat_path)),]
  return(temp)
}

input_data = unstrat_pathways(input_data)
```

# 610 unstratified pathways 
```{r}
colSums(input_data)
```

```{r}
colSums(input_data) == 0 
```

```{r}
selecttop20par <- rowSums(input_data)
input_data <- rownames_to_column(input_data)
input_data <- as.data.frame(input_data %>% top_n(30,selecttop20par)) #100
other <- 1-colSums(input_data[,-1])
rownames(input_data) <- input_data$rowname
input_data <- input_data[,-1]
input_data <- rbind(input_data,other)
rownames(input_data)[nrow(input_data)] <- "Other"
input_data2 <- t(input_data)
input_data2<-as.data.frame(input_data2)

input_data2$Sample_name<-rownames(t(input_data))
input_metadata$Sample_name<-rownames(input_metadata)


#subset metadata

metadata_subset <- input_metadata
metadata_subset <- subset(input_metadata, select = -c(Study:DNAExtraction_method))

input_data2 <- inner_join(input_data2,metadata_subset, by = c("Sample_name"))

rownames(input_data2)<-input_data2$Sample_name




OTUtable_av <- ddply(input_data2,.(Body_site),numcolwise(mean,na.rm = TRUE))
    rownames(OTUtable_av) <- OTUtable_av[,1]
 #   OTUtable_av <- OTUtable_av[,-c(1)]

OTUtable_av2  <- reshape2::melt(OTUtable_av,id.vars="Body_site")
OTUtable_av2$value <- as.numeric(OTUtable_av2$value)
```
# to obtain the standard deviation
```{r}
OTUtable_sd <- ddply(input_data2, .(Body_site), numcolwise(sd, na.rm = TRUE))


```

```{r}
library(ggplot2)
library(RColorBrewer)

n_colors <- 31
brbg_palette <- colorRampPalette(brewer.pal(11, "BrBG"))(n_colors)


bacteria_top<-OTUtable_av2[OTUtable_av2$Body_site=="Top_scalp",]
topscalp <- ggplot(bacteria_top, aes(x = "", y = value, fill = variable)) +
  geom_bar(stat = "identity", width = 1) +
  scale_fill_manual(values = brbg_palette) +
  ylab("Relative abundance") +
  xlab("Top scalp") +
  theme(
    legend.text = element_text(size = 15),
    axis.title.x = element_text(size = 15),
    axis.text.y = element_text(size = 10),
    title = element_text(size = 18),
    panel.background = element_rect(fill = "white"),
    panel.grid.major = element_blank(),
    axis.line.y = element_blank()
  )

topscalp
ggsave("topscalp_RbY.png", units="in", width=20, height=12, dpi=400, bg="white")

bacteria_back<-OTUtable_av2[OTUtable_av2$Body_site=="Back_scalp",]
backscalp <- ggplot(bacteria_back, aes(x = "", y = value, fill = variable)) +
  geom_bar(stat = "identity", width = 1) +
  scale_fill_manual(values = brbg_palette) +
  ylab("Relative abundance") +
  xlab("Back scalp") +
  theme(
    legend.text = element_text(size = 15),
    axis.title.x = element_text(size = 15),
    axis.text.y = element_text(size = 10),
    title = element_text(size = 18),
    panel.background = element_rect(fill = "white"),
    panel.grid.major = element_blank(),
    axis.line.y = element_blank()
)
backscalp

bacteria_les<-OTUtable_av2[OTUtable_av2$Body_site=="Lesional",]
lesionalscalp<- ggplot(bacteria_les, aes(x = "", y = value, fill = variable)) +
  geom_bar(stat = "identity", width = 1) +
  scale_fill_manual(values = brbg_palette) +
  ylab("Relative abundance") +
  xlab("Lesional") +
  theme(
    legend.text = element_text(size = 15),
    axis.title.x = element_text(size = 15),
    axis.text.y = element_text(size = 10),
    title = element_text(size = 18),
    panel.background = element_rect(fill = "white"),
    panel.grid.major = element_blank(),
    axis.line.y = element_blank()
)

bacteria_nonles<-OTUtable_av2[OTUtable_av2$Body_site=="Non_Lesional",]
nonlesionalscalp<- ggplot(bacteria_nonles, aes(x = "", y = value, fill = variable)) +
  geom_bar(stat = "identity", width = 1) +
  scale_fill_manual(values = brbg_palette) +
  ylab("Relative abundance") +
  xlab("Non_Lesional") +
  theme(
    legend.text = element_text(size = 15),
    axis.title.x = element_text(size = 15),
    axis.text.y = element_text(size = 10),
    title = element_text(size = 18),
    panel.background = element_rect(fill = "white"),
    panel.grid.major = element_blank(),
    axis.line.y = element_blank()
)

figure <- ggarrange(topscalp, backscalp, lesionalscalp, nonlesionalscalp, ncol = 4, nrow = 1,common.legend = TRUE, legend ="bottom",align="hv", labels=c("A","B", "C", "D"), font.label=list(size=17))
```
#save Fig. S8.
```{r}
ggsave("Psoriasiscapitis_humann3.6averages_RByn.png", units="in", width=20, height=12, dpi=400, bg="white")
```

#Fig. 6 and Fig.S9: Humann3.6 mapped
```{r}
input_data <-read.csv2("Datasets/humann36_pathabundance_relab.csv", header = TRUE, sep = ",",  dec = ".", stringsAsFactors = FALSE, row.names=1)
```

```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, sep=",", dec =".",row.names=1, stringsAsFactors=FALSE)
```


#unstratify the input data
```{r}
unstrat_pathways <-function(dat_path){
  temp = dat_path[!grepl("\\|",rownames(dat_path)),]
  return(temp)
}

input_data = unstrat_pathways(input_data)
```

```{r}
# sum values in columns
colSums(input_data[,-1])

#In the metagenomics table, rows should be sampleID's and columns taxa
# transpose metagenomics table
input_data_t = as.data.frame(t(input_data))
# check all rows sum up to 1
rowSums(input_data_t)
```
```{r}
input_data_filt <- input_data_t
```


### Maaslin2 pair-wise comparisons

# Lesional vs non-lesional: inflammation marker pathways present? 
```{r}

metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```
```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysv36qstat10_LesionalvsNon_Lesional", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Non_Lesional"))
```

# Lesional vs top-scalp: disease marker / health marker pathways present? 
```{r}
input_data_filt <- input_data_t
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```


```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysv36qstat10_LesionalvsTopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Top_scalp"))

```



# Lesional vs back-scalp: disease marker / health marker pathways present? 
```{r}
input_data_filt <- input_data_t
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```


```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysv36qstat10_LesionalvsBackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Back_scalp"))

```


# Non_Lesional vs Back-scalp: disease marker / health marker pathways present? 
```{r}
input_data_filt <- input_data_t
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```


```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysv36qstat10_NonLesionalvsBackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Back_scalp"))

```


# No differential abundant pathways between Non-lesional scalp areas and the back of the scalp. 

# Non_Lesional vs Top-scalp: disease marker / health marker pathways present? 
```{r}
input_data_filt <- input_data_t
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```

-

```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysv36qstat10_NonLesionalvsTopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Top_scalp"))

```

#No differential abundant pathways between non-lesional and top scalp areas 

# Top vs back: differential abundant pathways? 
```{r}
input_data_filt <- input_data_t
metadata_filt <- metadata
metadata_filt$sampleID = row.names(metadata_filt)
#metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$sampleID)

# select sample ids in metagenomics table
input_data_filt = input_data_filt[rownames(input_data_filt) %in% keep, ] 

```


```{r}
fit_data = Maaslin2(
    input_data = input_data_filt, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_pathwaysqstat10_TopscalpvsBackscalp", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Top_scalp"))
```

```{r setup, include=FALSE}
library(tidyr)
library(ComplexHeatmap)
library(circlize)
library(tibble)
```

#Fig. 6 and Fig S9
#Complex Heatmap 
```{r}
data_plot_arranged = data.table::fread("Datasets/maaslin2_18082023_humannv36.csv", dec=",")
myplot = data_plot_arranged

```

```{r pressure, echo=FALSE}
# create matrix for heatmap
plot_matrix = as.data.frame(cbind(myplot$feature, myplot$value, myplot$Heatmap_value, myplot$Superclass_1, myplot$Superclass_2))
names(plot_matrix) = c("feature", "value", "Heatmap_value", "Superclass_1", "Superclass_2")
```
```{r}
# transform from long format to wide format
plot_matrix_wide <- spread(plot_matrix, value, Heatmap_value)
plot_matrix_wide <- plot_matrix_wide %>% remove_rownames %>% column_to_rownames(var="feature")
plot_matrix_wide4 <- subset(plot_matrix_wide, select=-c(Superclass_1, Superclass_2))
plot_matrix_wide2 = apply(as.matrix(plot_matrix_wide4), 2, as.numeric)
rownames(plot_matrix_wide2) = rownames(plot_matrix_wide)
```


```{r}
plot_matrix2 = as.data.frame(cbind(myplot$feature,myplot$value, myplot$qval))

names(plot_matrix2) = c("feature","value", "qval")
plot_matrix2$qval<-as.numeric(plot_matrix2$qval)
```

```{r}
# transform from long format to wide format
matrix_anno_wide <- spread(plot_matrix2, value, qval)
matrix_anno_wide <- matrix_anno_wide %>% remove_rownames %>% column_to_rownames(var="feature")
matrix_anno_wide2 = apply(as.matrix(matrix_anno_wide), 2, as.numeric)
rownames(matrix_anno_wide2) = rownames(matrix_anno_wide)
```

```{r}
# extract info from Superclass_1
#Superclass_1 = as.data.frame(cbind(myplot$feature, myplot$Superclass_1))
Superclass_1= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, Superclass_2)) #Back scalp or Back_scalp
Superclass_1_list <- Superclass_1

```

```{r}
# extract info from Superclass_1
#Superclass_1 = as.data.frame(cbind(myplot$feature, myplot$Superclass_1))
Superclass_2= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, Superclass_1)) #Back scalp or Back_scalp
Superclass_2_list <- Superclass_2

```


```{r}
# bind for annotation: Superclass_1 and Superclass_2"
anno = as.data.frame(cbind(Superclass_1_list, Superclass_2_list))
```



```{r}
# replace NA by 0 in df for clustering
plot_matrix_wide3 = plot_matrix_wide2
plot_matrix_wide3[is.na(plot_matrix_wide3)] <- 0
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows <- complete.cases(plot_matrix_wide2)

# Subset the original dataframe to keep only complete rows
plot_matrix_wide3 <- plot_matrix_wide2[complete_rows,]

```

```{r}
matrix_anno_wide3 = matrix_anno_wide2
matrix_anno_wide3[is.na(matrix_anno_wide3)] <- 1
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows2 <- complete.cases(matrix_anno_wide2)

# Subset the original dataframe to keep only complete rows
matrix_anno_wide3 <- matrix_anno_wide2[complete_rows,]
```

```{r}

# Get the row names of both dataframes
df1_row_names <- rownames(anno)

df2_row_names <- rownames(plot_matrix_wide3)
# Find the row names that are present in both dataframes
common_row_names <- intersect(df1_row_names, df2_row_names)

# Subset both dataframes based on the common row names
plot_matrix_wide3 <- plot_matrix_wide3[common_row_names, ]
anno <- anno[common_row_names, ]
```


```{r}
col_fun = colorRamp2(c(-7, 0, 7), c("blue", "#EEEEEE", "red"), space = "RGB")


# adjust legend in annotation here > fontface = 2 is bold
row_ha = rowAnnotation(df = anno, annotation_name_gp= gpar(fontsize = 8, fontface = 2), annotation_name_rot = -60, 
                       gp = gpar(col ="white", lwd = 2), border = TRUE,
                       col = list(Superclass_1 = c("Biosynthesis" =  "#662700", "Degradation/Utilization/Assimilation" = "#FFAD65", "Generation of Precursor Metabolites and Energy" ="#A73030","Macromolecule Modification"="#7AA6DC"),  
                                  Superclass_2 = c("Glycolysis" = "#7AA6DC", "Amino Acid Biosynthesis" = "#A73030", "Aromatic Compound Biosynthesis" = "#3B3B3B",  "Carbohydrate Biosynthesis" = "#003C67", "Aromatic Compound Degradation"="#8FBC8F", "Fermentation" ="#C1FFC1","Cofactor, Carrier, and Vitamin Biosynthesis" = "#B23AEE","Amino Acid Degradation" = "#FFD700", "Secondary Metabolite Degradation" = "darkorange4","Aldehyde Degradation" = "#1C86EE", "Secondary Metabolite Biosynthesis" = "#00CD00", "C1 Compound Utilization and Assimilation"= "#FF7F00", "Carboxylate Degradation" = "#CD661D", "Cell Structure Biosynthesis" = "#CDAA7D", "Electron Transfer Chains" = "#76EEC6", "Fatty Acid and Lipid Biosynthesis" = "#00EEEE", "Tetrapyrrole Biosynthesis"= "#0000CD","Nucleoside and Nucleotide Biosynthesis" = "#8B864E", "Nucleoside and Nucleotide Degradation" = "#EED5D2", "TCA cycle" = "#FF0000", "Carbohydrate Degradation" = "#2E8B57", "Nucleic Acid Processing"= "#FA8072", "Fatty Acid and Lipid Degradation" = "#836FFF", "Amine and Polyamine Degradation" = "#FFFF00", "Alcohol Degradation" ="#68838B", "Cyclitol Degradation" = "grey", "Other Biosynthesis"= "black")),
                       annotation_legend_param = list(title_gp = gpar(fontsize = 10, fontface = 2), 
                                                      labels_gp = gpar(fontsize = 10), border='black')) #fontsize =8

```



```{r}
# adjust main legend here with heatmap_legend_param
p = Heatmap(plot_matrix_wide3, name = "-sign(beta)*log(qval)", col = col_fun, na_col = "white", 
            clustering_distance_rows = "pearson",
            row_dend_side = "left", row_dend_width = unit(1, "cm"), 
            show_column_dend = FALSE,
            row_names_side = "right", 
            rect_gp = gpar(col = "white", lwd = 2),
            border_gp = gpar(col = "black", lty = 1),
            row_names_gp = gpar(fontsize = 8, fontface = "italic"),# fontsize 8
            column_names_gp = gpar(fontsize = 8), column_names_rot = -60, #rownames size  # fontsize 8
            left_annotation = row_ha, # this includes the annotation of the graph!
            width = ncol(plot_matrix_wide3)*unit(8, "mm"), # adjust cell size in heatmap #adjust width unit(8)
            height = nrow(plot_matrix_wide3)*unit(4, "mm"), # adjust cell size in heatmap
            row_title = "Pathways (N=25)",
            row_title_gp = gpar(fontsize = 8, fontface = 2),
            row_title_side = "left",
            heatmap_legend_param = list(title_gp = gpar(fontsize = 8,fontface = 2), labels_gp = gpar(fontsize = 8), at = c(-7, -3.5, 0, 3.5, 7),legend_direction = "horizontal", border='black'), #, # fontsize 8
            #column_order = colnames(plot_matrix_wide2)#,
            cell_fun = function(j, i, x, y, w, h, fill) {
              if(matrix_anno_wide3[i, j] < 0.001) {
                grid.text('***', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.01){
                grid.text('**', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.05) {
                grid.text('*', x, y-h*0.01, gp = gpar(fontsize = 6))
              }}
)
p = draw(p, heatmap_legend_side="left", annotation_legend_side="left") 
p 



```
#save Fig. 6 or Fig. S9
```{r}
tiff("Figure6.tiff", units='mm', width=340, height=140, res=350, compression="lzw")
p
dev.off()
```



# Virulence factor genes

# Fig. S10
```{r cars}
VF_classes <- read.csv("Datasets/virulence_factors_classes_relative.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE)
rownames(VF_classes) <- VF_classes$VFcategory
VF_classes2 <- VF_classes[,-1]
```

```{r}

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample <- rownames(metadata)

```


```{r}
VF_classes3 <- t(VF_classes2)
VF_classes3<-as.data.frame(VF_classes3)

VF_classes3$Sample_name<-rownames(VF_classes3)


VF_classes3 <- inner_join(VF_classes3,metadata, by = c("Sample_name"))

rownames(VF_classes3)<-VF_classes3$Sample_name


VF_classes3 <- melt(VF_classes3,id.vars=colnames(metadata))



VF_classes3$value <- as.numeric(VF_classes3$value)
```
```{r}
colors_fix_bacteria <- c("Antimicrobial activity/Competitive advantage" = "#a50026", "Exoenzyme" = "#d73027","Immune modulation" = "#f46d43", "Stress survival" = "#fdae61","Adherence" = "#fee090", "Regulation" = "#ffffbf", "Biofilm" = "#f7f7f7", "Exotoxin" = "#e0f3f8", "Effector delivery system" = "#abd9e9", "Invasion" = "#74add1", "Nutritional/Metabolic factor" = "#4575b4", "Motility" = "#313695" ,"Post-translational modification"= "#CDC5BF","Others" =  "#000000")
```



```{r}
VFclasses_h<-VF_classes3[VF_classes3$Pool=="Healthy",]

```
```{r}

variable_levels <- c("Antimicrobial activity/Competitive advantage" , "Exoenzyme","Immune modulation", "Stress survival","Adherence", "Regulation", "Biofilm", "Exotoxin", "Effector delivery system", "Invasion", "Nutritional/Metabolic factor", "Motility" ,"Post-translational modification", "Others")
```

```{r}
healthy_rel<- VFclasses_h %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above #TAKE CARE!!!! 
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Pool) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'))
```

```{r}
ggsave("VFgenes_classes_healthysorted.png", units="in", width=22, height=11, dpi=400, bg="white")
```


```{r}
VFclasses_les<-VF_classes3[VF_classes3$Body_site_2=="Lesional",]
```

```{r}

variable_levels <- c("Antimicrobial activity/Competitive advantage" , "Exoenzyme","Immune modulation", "Stress survival","Adherence", "Regulation", "Biofilm", "Exotoxin", "Effector delivery system", "Invasion", "Nutritional/Metabolic factor", "Motility" ,"Post-translational modification", "Others")
```
```{r}
lesional_rel<- VFclasses_les %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'))
```

```{r}
ggsave("VFgenes_classes_lesionalsorted.png", units="in", width=11, height=11, dpi=400, bg="white")
```


```{r}
VFclasses_nonles<-VF_classes3[VF_classes3$Body_site_2=="Non_Lesional",]
```

```{r}

variable_levels <- c("Antimicrobial activity/Competitive advantage" , "Exoenzyme","Immune modulation", "Stress survival","Adherence", "Regulation", "Biofilm", "Exotoxin", "Effector delivery system", "Invasion", "Nutritional/Metabolic factor", "Motility" ,"Post-translational modification", "Others")
```

```{r}
nonlesional_rel <- VFclasses_nonles %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'))
```

```{r}
ggsave("VFgenes_classes_nonlesionalsorted.png", units="in", width=11, height=11, dpi=400, bg="white")
```


```{r}
# Add labels to each plot
plot_H_labeled <- healthy_rel + labs(tag = "A") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_L_labeled <- lesional_rel + labs(tag = "B") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_NL_labeled <- nonlesional_rel + labs(tag = "C") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_empty <-ggplot()
```


```{r}
library(cowplot)
library(grid)

# Combine the legend of the three plots
combined_legend <- cowplot::get_legend(plot_H_labeled)  # Assuming plot_H_labeled has the legend

# Remove the legends from individual plots
plot_H_labeled <- plot_H_labeled + theme(legend.position = "none")
plot_L_labeled <- plot_L_labeled + theme(legend.position = "none")
plot_NL_labeled <- plot_NL_labeled + theme(legend.position = "none")
```


```{r}



# Create the grid with the aligned plots and the combined legend
grid <- plot_grid(plot_H_labeled,
                  plot_grid(plot_L_labeled, plot_NL_labeled, ncol = 2),
                  #combined_legend, #,align = "hv",
                  ncol = 1,
                  rel_heights = c(1, 1, 0.2))
```
Fig. S10
```{r}
# Save the figure with adjusted dimensions and legend settings
ggsave("figure_VF_healthy_lesional_nonlesional_relativeabundance.png",
       plot = grid,
       units = "in",
       width = 10,  # Adjust the width as needed
       height = 13,  # Adjust the height as needed
       dpi = 300,
       bg = "white",
       scale = 1.5)  # Increase the scale factor to improve legend readability

```

#Maaslin2
```{r}
VF_classes <- read.csv("Datasets/virulence_factors_relative_overview.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE, row.names=1)
#rownames(VF_classes) <- VF_classes$VFcategory
VF_classes2 <- VF_classes
```

```{r}

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

rownames(metadata) <- metadata$Sample_name

```


```{r}
# sum values in columns
colSums(VF_classes2)

# transpose metagenomics table
VF_classes2t = as.data.frame(t(VF_classes2))

# check all rows sum up to 1
rowSums(VF_classes2t)
```

#Non-Lesional vs Top-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```

```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ]

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]


```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_NonLesionalvstopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```


#Non-Lesional vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))


```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_NonLesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```


#Lesional vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]


```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_Lesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```

#Lesional vs Top-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 


# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]


```



```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_LesionalvsTopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```


#Lesional vs Non-Lesional
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 


# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```

```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_LesionalvsNon_Lesional", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Non_Lesional"))
```
#Top-scalp vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_VF_topscalpcsbackscalp", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Top_scalp"))
```

# Fig. 7: Complex Heatmap Virulence factor genes
#STEP1: 

```{r}
VF_list <- read.csv("Datasets/virulence_classes.csv", header=TRUE,  sep=",", stringsAsFactors=FALSE, row.names=1)

VF_list$feature <- row.names(VF_list)

VF_list$feature2 <- paste(VF_list$feature, VF_list$VF_Name, sep = " - ")

data_plot_arranged = data.table::fread("Datasets/VF_significantvalues_Maaslin2.csv", dec=",")

myplot = data_plot_arranged

```

#STEP2:
```{r}
VF_data <- inner_join(myplot, VF_list, by = c("feature"))
VF_data <- VF_data[,-c(12:14)]
VF_data <- VF_data[,-c(13:17)]
```

```{r pressure, echo=FALSE}
# create matrix for heatmap
plot_matrix = as.data.frame(cbind(VF_data$feature2, VF_data$value, VF_data$heatmap_value, VF_data$VF_Name, VF_data$VFcategory))
names(plot_matrix) = c("feature", "value", "Heatmap_value", "VF_Name", "VF_category")
```

```{r}
# transform from long format to wide format
plot_matrix_wide <- spread(plot_matrix, value, Heatmap_value)
plot_matrix_wide <- plot_matrix_wide %>% remove_rownames %>% column_to_rownames(var="feature")
plot_matrix_wide4 <- subset(plot_matrix_wide, select=-c(VF_Name, VF_category))
plot_matrix_wide2 = apply(as.matrix(plot_matrix_wide4), 2, as.numeric)
rownames(plot_matrix_wide2) = rownames(plot_matrix_wide)
```

```{r}
plot_matrix2 = as.data.frame(cbind(VF_data$feature2,VF_data$value, VF_data$qval))
names(plot_matrix2) = c("feature","value", "qval")
```

```{r}
# transform from long format to wide format
matrix_anno_wide <- spread(plot_matrix2, value, qval)
matrix_anno_wide <- matrix_anno_wide %>% remove_rownames %>% column_to_rownames(var="feature")
matrix_anno_wide2 = apply(as.matrix(matrix_anno_wide), 2, as.numeric)
rownames(matrix_anno_wide2) = rownames(matrix_anno_wide)


```



```{r}
#comparison of only three pairwise comparisons
VF_category= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, VF_Name))
Superclass_1_list <- VF_category
```

```{r}
#comparison of only three pairwise comparisons
VF_name= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, VF_Name))
Superclass_1_list <- VF_name
```

```{r}
# bind for annotation: Superclass_1 and Superclass_2"
anno = as.data.frame(Superclass_1_list)
anno$VF_name = rownames(anno)
```



```{r}
# replace NA by 0 in df for clustering
plot_matrix_wide3 = plot_matrix_wide2
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows <- complete.cases(plot_matrix_wide2)

# Subset the original dataframe to keep only complete rows
plot_matrix_wide3 <- plot_matrix_wide2[complete_rows,]

```



```{r}

plot_matrix_wide3[is.na(plot_matrix_wide3)] <- 0
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows2 <- complete.cases(matrix_anno_wide2)

# Subset the original dataframe to keep only complete rows
matrix_anno_wide3 <- matrix_anno_wide2[complete_rows,]
```
```{r}
matrix_anno_wide3 = matrix_anno_wide2
matrix_anno_wide3[is.na(matrix_anno_wide3)] <- 1
```

```{r}

# Get the row names of both dataframes
df1_row_names <- rownames(anno)

df2_row_names <- rownames(plot_matrix_wide3)
# Find the row names that are present in both dataframes
common_row_names <- intersect(df1_row_names, df2_row_names)

# Subset both dataframes based on the common row names
plot_matrix_wide3 <- plot_matrix_wide3[common_row_names, ]
anno <- anno[common_row_names, ]
anno2 <- anno 

anno2 <- subset(anno, select = c("VF_category"))

anno <- anno2

#anno2 <- as.data.frame(anno2[ , c("VF_category")])
#row.names(anno2) <- row.names(anno)
#col.names(anno2) <- col.names(anno$VF_category)

anno$VF_category <- factor(anno$VF_category, levels = c("Antimicrobial activity/Competitive advantage" , "Exoenzyme","Immune modulation", "Stress survival","Adherence", "Regulation", "Biofilm", "Exotoxin", "Effector delivery system", "Invasion", "Nutritional/Metabolic factor", "Motility" ,"Post-translational modification", "Others"))


```

```{r}
col_fun = colorRamp2(c(-10, 0, 10), c("blue", "#EEEEEE", "red"), space = "RGB")


# adjust legend in annotation here > fontface = 2 is bold
row_ha = rowAnnotation(df = anno, annotation_name_gp= gpar(fontsize = 8, fontface = 2), annotation_name_rot = -60, 
                       gp = gpar(col ="white", lwd = 2), border = TRUE,
                       col = list(VF_category = c("Antimicrobial activity/Competitive advantage" = "#a50026", "Exoenzyme" = "#d73027","Immune modulation" = "#f46d43", "Stress survival" = "#fdae61","Adherence" = "#fee090", "Regulation" = "#ffffbf", "Biofilm" = "#f7f7f7", "Exotoxin" = "#e0f3f8", "Effector delivery system" = "#abd9e9", "Invasion" = "#74add1", "Nutritional/Metabolic factor" = "#4575b4", "Motility" = "#313695" ,"Post-translational modification"= "#CDC5BF","Others" =  "#000000")),
                       annotation_legend_param = list(title_gp = gpar(fontsize = 8, fontface = 2), 
                                                      labels_gp = gpar(fontsize = 8), border='black'))


```

```{r}
# adjust main legend here with heatmap_legend_param
p = Heatmap(plot_matrix_wide3, name = "-sign(beta)*log(qval)", col = col_fun, na_col = "white", 
            clustering_distance_rows = "pearson",
            row_dend_side = "left", row_dend_width = unit(1, "cm"), 
            show_column_dend = FALSE,
            row_names_side = "right", 
            rect_gp = gpar(col = "white", lwd = 2),
            border_gp = gpar(col = "black", lty = 1),
            row_names_gp = gpar(fontsize = 8, fontface = "italic"),# fontsize 8
            column_names_gp = gpar(fontsize = 8), column_names_rot = -60, #rownames size  # fontsize 8
            left_annotation = row_ha, # this includes the annotation of the graph!
            width = ncol(plot_matrix_wide3)*unit(8, "mm"), # adjust cell size in heatmap #adjust width unit(8)
            height = nrow(plot_matrix_wide3)*unit(4, "mm"), # adjust cell size in heatmap
            row_title = "Virulence factor genes (N=41)",
            row_title_gp = gpar(fontsize = 8, fontface = 2),
            row_title_side = "left",
            heatmap_legend_param = list(title_gp = gpar(fontsize = 8,fontface = 2), labels_gp = gpar(fontsize = 8), at = c(-10, -5, 0, 5, 10),legend_direction = "horizontal", border='black'), #, # fontsize 8
            #column_order = colnames(plot_matrix_wide2)#,
            cell_fun = function(j, i, x, y, w, h, fill) {
              if(matrix_anno_wide3[i, j] < 0.001) {
                grid.text('***', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.01){
                grid.text('**', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.05) {
                grid.text('*', x, y-h*0.01, gp = gpar(fontsize = 6))
              }}
)
p = draw(p, heatmap_legend_side="left", annotation_legend_side="left") 
p 

```

#save Fig. 7 
```{r}
tiff("Figure7.tiff", units='mm', width=180, height=200, res=350, compression= "lzw")
p
dev.off()
```


#AMR elements
```{r cars}
VF_classes <- read.csv("Datasets/AMR_elements_relative_overview.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE, row.names =1)


colSums(VF_classes)

VF_classes2 <- VF_classes
```


```{r}

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample <- rownames(metadata)

```


#Fig. S12
```{r}
selecttop20par <- rowSums(VF_classes2)

VF_classes2 <- rownames_to_column(VF_classes2)
VF_classes2 <- as.data.frame(VF_classes2 %>% top_n(10,selecttop20par))
other <- 1-colSums(VF_classes2[,-1])
rownames(VF_classes2) <- VF_classes2$rowname
VF_classes2 <- VF_classes2[,-1]
VF_classes2 <- rbind(VF_classes2,other)
rownames(VF_classes2)[nrow(VF_classes2)] <- "Other"
VF_classes2 <- as.data.frame(t(VF_classes2))
```


```{r}
#optional
names(VF_classes2)[1] <- "MFS antibiotic efflux pump (Phosponic acid)"
names(VF_classes2)[2] <- "ABC antibiotic efflux pump (MDR)"
names(VF_classes2)[3] <- "rpoB (Rifamycin)"
names(VF_classes2)[4] <- "RND antibiotic efflux pump (MDR)"
names(VF_classes2)[5] <- "ileS (Mupirocin)"
names(VF_classes2)[6] <- "vga-type ABC-F protein (Lincosamide/Streptogramin)"
names(VF_classes2)[7] <- "Tet(O) (Tetracycline)"
names(VF_classes2)[8] <- "ABC-F subfamily (Macrolide)"
names(VF_classes2)[9] <- "vanR (Glycopeptide)"
names(VF_classes2)[10] <- "Van ligase (Glycopeptide)"
```


```{r}
VF_classes2$Sample_name<-rownames(VF_classes2)


VF_classes2 <- inner_join(VF_classes2,metadata, by = c("Sample_name"))
rownames(VF_classes2)<-VF_classes2$Sample


VF_classes3 <- melt(VF_classes2,id.vars=colnames(metadata))

#Warning in melt(Bacterialspecies2, id.vars = colnames(metadata)) :
#The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(Bacterialspecies2). In the next version, this warning will become an error.

VF_classes3$value <- as.numeric(VF_classes3$value)




```


```{r}
VFclasses_h<-VF_classes3[VF_classes3$Body_site_2=="Healthy",]
```



```{r}
colors_fix_bacteria <- c("MFS antibiotic efflux pump (Phosponic acid)" = "#7f3b08", "ABC antibiotic efflux pump (MDR)" = "#b35806","rpoB (Rifamycin)"  = "#e08214", "RND antibiotic efflux pump (MDR)"  = "#fdb863","ileS (Mupirocin)" = "#fee0b6", "vga-type ABC-F protein (Lincosamide/Streptogramin)"   =  "#f7f7f7", "Tet(O) (Tetracycline)"  = "#d8daeb", "ABC-F subfamily (Macrolide)" = "#b2abd2",  "vanR (Glycopeptide)" = "#8073ac", "Van ligase (Glycopeptide)" = "#542788","Other"  = "#2d004b")
```

```{r}
#variable levels for new color code! 
variable_levels <-  c("MFS antibiotic efflux pump (Phosponic acid)", "ABC antibiotic efflux pump (MDR)","rpoB (Rifamycin)", "RND antibiotic efflux pump (MDR)","ileS (Mupirocin)", "vga-type ABC-F protein (Lincosamide/Streptogramin)", "Tet(O) (Tetracycline)", "ABC-F subfamily (Macrolide)",  "vanR (Glycopeptide)" , "Van ligase (Glycopeptide)","Other")
```


```{r}
healthy_rel <- VFclasses_h %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18))
```
```{r}
ggsave("AMRelements_classes_relativehealthy.png", units="in", width=22, height=11, dpi=400, bg="white")
```


```{r}
VFclasses_nonles<-VF_classes3[VF_classes3$Body_site_2=="Non_Lesional",]
```


```{r}
nonlesional_rel<-VFclasses_nonles %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'))
```

```{r}
ggsave("AMRelements_classes_relativenonlesional.png", units="in", width=11, height=11, dpi=400, bg="white")
```


```{r}
VFclasses_les<-VF_classes3[VF_classes3$Body_site_2=="Lesional",]
```

```{r}
lesional_rel <- VFclasses_les %>% 
  dplyr::mutate(
    across(c(Sample, variable),
           ~ forcats::as_factor(.)
           )) %>% 
  dplyr::mutate(
    across(variable, ~ forcats::fct_relevel(., variable_levels))
    ) %>% 
  dplyr::mutate(
    across(Sample, ~ forcats::fct_relevel(., variable_levels)) # or provide a vector of levels and use fct_relevel as above
    ) %>% 
  ggplot(aes(Sample, y =value, fill = variable)) + 
  geom_col() + facet_grid(.~Body_site_2) + theme(axis.text.x = element_text(angle =0), axis.ticks.x = element_line(colour = "black"), axis.ticks.y =element_line(colour = "black"), panel.background = element_rect(fill = "white"), legend.position = "bottom", panel.grid.major=element_blank(),  panel.grid.minor=element_blank(), axis.line.x  = element_line(colour = "black"), axis.line.y  = element_line(colour = "black"),)+  theme(axis.text.x = element_text(size=12, angle=90),axis.text.y = element_text(size=14), strip.text.x = element_text(size =16, face="bold"), axis.title.y = element_text(size=18),axis.title.x = element_text(size=18)) +xlab("Participant")+scale_fill_manual(name='',values=colors_fix_bacteria) + ylab("Relative abundance") + theme(legend.text=element_text(size=18,face='italic'))
```

```{r}
ggsave("AMRelements_classes_relativelesional.png", units="in", width=11, height=11, dpi=400, bg="white")
```



```{r}
# Add labels to each plot
plot_H_labeled <- healthy_rel + labs(tag = "A") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_L_labeled <- lesional_rel + labs(tag = "B") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_NL_labeled <- nonlesional_rel + labs(tag = "C") +
  theme(plot.tag = element_text(size = 20, face = "bold"))
plot_empty <-ggplot()
```


```{r}
library(cowplot)
library(grid)

# Combine the legend of the three plots
combined_legend <- cowplot::get_legend(plot_H_labeled)  # Assuming plot_H_labeled has the legend

# Remove the legends from individual plots
plot_H_labeled <- plot_H_labeled + theme(legend.position = "none")
plot_L_labeled <- plot_L_labeled + theme(legend.position = "none")
plot_NL_labeled <- plot_NL_labeled + theme(legend.position = "none")
```


```{r}



# Create the grid with the aligned plots and the combined legend
grid <- plot_grid(plot_H_labeled,
                  plot_grid(plot_L_labeled, plot_NL_labeled, ncol = 2),
                  #combined_legend, #,align = "hv",
                  ncol = 1,
                  rel_heights = c(1, 1, 0.2))
```

```{r}
# Save the figure with adjusted dimensions and legend settings
ggsave("figure_healthy_lesional_nonlesional_relativeabundance_AMR_labeled.png",
       plot = grid,
       units = "in",
       width = 10,  # Adjust the width as needed
       height = 13,  # Adjust the height as needed
       dpi = 300,
       bg = "white",
       scale = 1.5)  # Increase the scale factor to improve legend readability

```

# Fig. S13: AMR-elements
```{r}
VF_classes <- read.csv("Datasets/AMR_elements_relative_overview.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE, row.names =1)

VF_classes2 <- VF_classes
```

```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

rownames(metadata) <- metadata$Sample_name
```



```{r}
# sum values in columns
colSums(VF_classes2)

# transpose metagenomics table
VF_classes2t = as.data.frame(t(VF_classes2))

# check all rows sum up to 1
rowSums(VF_classes2t)
```

#Non-Lesional vs Top-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 


# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_NonLesionalvstopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```


#Non-Lesional vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Non_Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 



# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```




```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_NonLesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Non_Lesional"))
```


#Lesional vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 


# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_Lesionalvsbackscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```


#Lesional vs Top-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Top_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 


# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```



```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_LesionalvsTopscalp", 
    fixed_effects = c("Body_site"),
    reference = c("Body_site,Lesional"))
```


#Lesional vs Non-Lesional
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Lesional' | metadata_filt$Body_site=='Non_Lesional'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ]

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]

```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_LesionalvsNon_Lesional", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Non_Lesional"))
```


#Top-scalp vs Back-scalp
```{r}
### NO FILTER TAXA BY PREVALENCE & ABUNDANCE (DATA PRE-PROCESSING)
VF_classes2_input_filt = VF_classes2t
```


```{r}
##### SELECTING SAMPLE GROUPS
# select sample ids in metadata
set.seed(123)
metadata_filt = metadata
metadata_filt$sampleID = row.names(metadata)
metadata_filt = metadata_filt[(metadata_filt$Body_site=='Top_scalp' | metadata_filt$Body_site=='Back_scalp'), ]
table(metadata_filt$Body_site)
table(metadata_filt$subjectID)
keep = as.vector(metadata_filt$Sample_name)


# select sample ids in metagenomics table
VF_classes2_input_filt2 = VF_classes2_input_filt[rownames(VF_classes2_input_filt) %in% keep, ] 

# Get the order of the samples in metadata_filt
order <- match(metadata_filt$Sample_name, rownames(VF_classes2_input_filt2))

# Use the order to reorder VF_classes2_input_filt2
VF_classes2_input_filt2_ordered <- VF_classes2_input_filt2[order, ]


```


```{r}
fit_data = Maaslin2(
    input_data = VF_classes2_input_filt2, 
    input_metadata = metadata_filt, 
    min_abundance = 0.0001,
    min_prevalence = 0.1,
    max_significance = 0.05,
    normalization = "NONE",
    output = "output_AMR_topscalpcsbackscalp", 
    fixed_effects = c("Body_site"),
    random_effects = c("subjectID"),
    reference = c("Body_site,Top_scalp"))
```


# Fig S13: Complex Heatmap AMR genes
##STEP1
```{r}
VF_list <- read.csv("Datasets/AMR_classes.csv", header=TRUE,  sep=",", stringsAsFactors=FALSE, row.names = 1)


VF_list$feature <- row.names(VF_list)
VF_list$feature <- gsub(";", " ", VF_list$feature)

data_plot_arranged = data.table::fread("significantvalues_AMR.csv", dec=",")
#data_plot_arranged$feature <- gsub("..ABC..", " (ABC) ", data_plot_arranged$feature)

myplot = data_plot_arranged


```

```{r}
VF_data <- inner_join(myplot, VF_list, by = c("feature"))

names(VF_data)[11] <- "Drug_Class"

names(VF_data)[12] <- "Resistance_Mechanism"
#VF_data <- VF_data[,-c(12:14)]
#VF_data <- VF_data[,-c(13:17)]
```

```{r pressure, echo=FALSE}
# create matrix for heatmap
plot_matrix = as.data.frame(cbind(VF_data$feature, VF_data$value, VF_data$heatmap_value, VF_data$Drug_Class, VF_data$Resistance_Mechanism))
names(plot_matrix) = c("feature", "value", "Heatmap_value", "Drug_Class", "Resistance_Mechanism")


```

```{r}
# transform from long format to wide format
plot_matrix_wide <- spread(plot_matrix, value, Heatmap_value)
plot_matrix_wide <- plot_matrix_wide %>% remove_rownames %>% column_to_rownames(var="feature")
plot_matrix_wide4 <- subset(plot_matrix_wide, select=-c(Drug_Class, Resistance_Mechanism))
plot_matrix_wide2 = apply(as.matrix(plot_matrix_wide4), 2, as.numeric)
rownames(plot_matrix_wide2) = rownames(plot_matrix_wide)
```

```{r}
plot_matrix2 = as.data.frame(cbind(VF_data$feature,VF_data$value, VF_data$qval))
names(plot_matrix2) = c("feature","value", "qval")
```

```{r}
# transform from long format to wide format
matrix_anno_wide <- spread(plot_matrix2, value, qval)
matrix_anno_wide <- matrix_anno_wide %>% remove_rownames %>% column_to_rownames(var="feature")
matrix_anno_wide2 = apply(as.matrix(matrix_anno_wide), 2, as.numeric)
rownames(matrix_anno_wide2) = rownames(matrix_anno_wide)


```

```{r}
# comparison of all pairwise comparisons
# extract info from Superclass_1

Drug_class= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, `Non_Lesional vs Back_scalp`, `Non_Lesional vs Top_scalp`, Resistance_Mechanism, `Non_Lesional vs Top_scalp`))
Superclass_1_list <- Drug_class

```


```{r}
# extract info from Superclass_1
#Superclass_1 = as.data.frame(cbind(myplot$feature, myplot$Superclass_1))
Resistance_Mechanism= subset(plot_matrix_wide, select=-c(
`Lesional vs Back_scalp`,`Lesional vs Non_Lesional`, `Lesional vs Top_scalp`, `Non_Lesional vs Back_scalp`, `Non_Lesional vs Top_scalp`, Drug_Class, `Non_Lesional vs Top_scalp`))
Superclass_2_list <- Resistance_Mechanism

```

```{r}
# bind for annotation: Superclass_1 and Superclass_2"
anno = as.data.frame(Superclass_2_list)
#anno$AMR_name = rownames(anno)
```



```{r}
# replace NA by 0 in df for clustering
plot_matrix_wide3 = plot_matrix_wide2
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows <- complete.cases(plot_matrix_wide2)

# Subset the original dataframe to keep only complete rows
plot_matrix_wide3 <- plot_matrix_wide2[complete_rows,]

```



```{r}

plot_matrix_wide3[is.na(plot_matrix_wide3)] <- 0
```

#Alternatively drop those columns that have an NA to only visualize those pathways that are differentially abundant in all three groups
```{r}

# Use complete.cases() to get a logical vector of complete rows
complete_rows2 <- complete.cases(matrix_anno_wide2)

# Subset the original dataframe to keep only complete rows
matrix_anno_wide3 <- matrix_anno_wide2[complete_rows,]
```
```{r}
matrix_anno_wide3 = matrix_anno_wide2
matrix_anno_wide3[is.na(matrix_anno_wide3)] <- 1
```

```{r}


anno$Resistance_Mechanism <- factor(anno$Resistance_Mechanism, levels = c("antibiotic inactivation", "antibiotic target alteration","antibiotic efflux", "reduced permeability to antibiotic","antibiotic target protection", "antibiotic target alteration;antibiotic target replacement", "antibiotic target replacement"))


```

```{r}
col_fun = colorRamp2(c(-10, 0, 10), c("blue", "#EEEEEE", "red"), space = "RGB")


# adjust legend in annotation here > fontface = 2 is bold
row_ha = rowAnnotation(df = anno, annotation_name_gp= gpar(fontsize = 8, fontface = 2), annotation_name_rot = -60, 
                       gp = gpar(col ="white", lwd = 2), border = TRUE,
                       col = list(Resistance_Mechanism= c("antibiotic inactivation" = "#b35806", "antibiotic target alteration" = "#f1a340","antibiotic efflux" = "#fee0b6", "reduced permeability to antibiotic" = "#f7f7f7","antibiotic target protection" = "#d8daeb", "antibiotic target alteration;antibiotic target replacement" = "#998ec3", "antibiotic target replacement" = "#542788")),
                       annotation_legend_param = list(title_gp = gpar(fontsize = 8, fontface = 2), 
                                                      labels_gp = gpar(fontsize = 8), border='black'))


```

```{r}
# adjust main legend here with heatmap_legend_param
p = Heatmap(plot_matrix_wide3, name = "-sign(beta)*log(qval)", col = col_fun, na_col = "white", 
            clustering_distance_rows = "pearson",
            row_dend_side = "left", row_dend_width = unit(1, "cm"), 
            show_column_dend = FALSE,
            row_names_side = "right", 
            rect_gp = gpar(col = "white", lwd = 2),
            border_gp = gpar(col = "black", lty = 1),
            row_names_gp = gpar(fontsize = 8, fontface = "italic"),# fontsize 8
            column_names_gp = gpar(fontsize = 8), column_names_rot = -60, #rownames size  # fontsize 8
            left_annotation = row_ha, # this includes the annotation of the graph!
            width = ncol(plot_matrix_wide3)*unit(8, "mm"), # adjust cell size in heatmap #adjust width unit(8)
            height = nrow(plot_matrix_wide3)*unit(4, "mm"), # adjust cell size in heatmap
            row_title = "AMR genes (N=36)",
            row_title_gp = gpar(fontsize = 8, fontface = 2),
            row_title_side = "left",
            heatmap_legend_param = list(title_gp = gpar(fontsize = 8,fontface = 2), labels_gp = gpar(fontsize = 8), at = c(-10, -5, 0, 5, 10),legend_direction = "horizontal", border='black'), #, # fontsize 8
            #column_order = colnames(plot_matrix_wide2)#,
            cell_fun = function(j, i, x, y, w, h, fill) {
              if(matrix_anno_wide3[i, j] < 0.001) {
                grid.text('***', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.01){
                grid.text('**', x, y-h*0.01, gp = gpar(fontsize = 6))
              } else if(matrix_anno_wide3[i, j] < 0.05) {
                grid.text('*', x, y-h*0.01, gp = gpar(fontsize = 6))
              }}
)
p = draw(p, heatmap_legend_side="left", annotation_legend_side="left") 
p 

```
#save Fig. S13
```{r}
tiff("heatmap_AMR_N36_withnames_orderedlegend.tiff", units='mm', width=350, height=400, res=300)
p
dev.off()
```

# Fig. S11
```{r}
mpa_table <- read.csv("Datasets/AMR_elements_absolute_overview.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE, row.names=1)
mpa_tablet <- as.data.frame(t(mpa_table))
hostreads_kneaddata <- read.csv("Datasets/Hostreads_kneaddata.csv", header=TRUE, sep=",", dec=",", stringsAsFactors=FALSE)
#metadata <- read.csv("Metadata_metaphlan.csv", header=TRUE, dec=",", sep=";", stringsAsFactors=FALSE)
```

```{r}
#mpa_tablet$Sample <- rownames(mpa_tablet)
# Get the order of the samples in metadata_filt
order <- match(hostreads_kneaddata$Sample, rownames(mpa_tablet))

# Use the order to reorder VF_classes2_input_filt2
mpatablet_ordered <- mpa_tablet[order, ]

```

```{r}
mpa_sums <- as.data.frame(rowSums(mpatablet_ordered))


perc_AMR <- as.data.frame(mpa_sums$`rowSums(mpatablet_ordered)`/hostreads_kneaddata$decontaminated.hg37dec_v0.1.pair1)
rownames(perc_AMR) <- rownames(mpa_sums)

```

```{r}

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample <- rownames(metadata)

rownames(metadata) <- metadata$Sample_name

```
```{r}
keep <- intersect(rownames(metadata), rownames(perc_AMR))
perc_AMR2 <- as.data.frame(perc_AMR[keep,])
```
```{r}
metadata2 <- cbind(metadata, perc_AMR2)
metadata2 <- metadata2 %>% rename_at('perc_AMR[keep, ]', ~'percen_AMR')
```

```{r}

div_metadata_new <- metadata2                          # Duplicate data
div_metadata_new$Body_site <- factor(div_metadata_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))

```


#FDR adjusted p-values
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```
```{r}
a <- ggplot(subset(div_metadata_new, Body_site %in% c("Top_scalp","Back_scalp", "Lesional", "Non_Lesional") ), aes(y = percen_AMR, x = Body_site, fill=Body_site)) +
  geom_boxplot(outlier.shape = NA) +  theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) +
  theme(panel.grid = element_blank()) + 
  ylab("AMR reads / total decontaminated reads") + xlab("Group") + scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))+ stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=3)
```
```{r}
ggsave("AMR_percenreads_FDRadjusted.png", units="in", width=9, height=7, dpi=400, bg="white")
```
```{r}
mean(metadata2$percen_AMR)
```
# percentage of VF genes compared to total reads per sample group

```{r}
#Hostreads_kneaddata.csv is located in the folder C:\Users\bdpessem\OneDrive - UGent\CMET\Psoriasis_2021\Kneaddata\qstat10% Sample name is corrected with an X in front
hostreads_kneaddata <- read.csv("Datasets/Hostreads_kneaddata.csv", header=TRUE, sep=",", dec=",", stringsAsFactors=FALSE)
mpa_table <- read.csv("Datasets/virulence_factors_absolute_overview.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE)
colnames(mpa_table) <- gsub("_matches.tsv", "", colnames(mpa_table))
rownames(mpa_table) <- mpa_table$X
mpa_table2 <- mpa_table[,-1]
mpa_tablet <- as.data.frame(t(mpa_table2))
```

```{r}
#mpa_tablet$Sample <- rownames(mpa_tablet)
# Get the order of the samples in metadata_filt
order <- match(hostreads_kneaddata$Sample, rownames(mpa_tablet))

# Use the order to reorder VF_classes2_input_filt2
mpatablet_ordered <- mpa_tablet[order, ]

```
```{r}
mpa_sums <- as.data.frame(rowSums(mpatablet_ordered))


perc_AMR <- as.data.frame(mpa_sums$`rowSums(mpatablet_ordered)`/hostreads_kneaddata$decontaminated.hg37dec_v0.1.pair1)
rownames(perc_AMR) <- rownames(mpa_sums)

```


```{r}

metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)

metadata$Sample <- rownames(metadata)

rownames(metadata) <- metadata$Sample_name

```

```{r}
keep <- intersect(rownames(metadata), rownames(perc_AMR))
perc_AMR2 <- as.data.frame(perc_AMR[keep,])
```
```{r}
metadata2 <- cbind(metadata, perc_AMR2)
metadata2 <- metadata2 %>% rename_at('perc_AMR[keep, ]', ~'percen_VF')
```

```{r}

div_metadata_new <- metadata2                          # Duplicate data
div_metadata_new$Body_site <- factor(div_metadata_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))

```


#FDR adjusted p-values
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```
```{r}
b <- ggplot(subset(div_metadata_new, Body_site %in% c("Top_scalp","Back_scalp", "Lesional", "Non_Lesional") ), aes(y = percen_VF, x = Body_site, fill=Body_site)) +
  geom_boxplot(outlier.shape = NA) +  theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) +
  theme(panel.grid = element_blank()) + 
  ylab("VF reads / total decontaminated reads") + xlab("Group") + scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d")) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=3)
```

```{r}
mean(metadata2$percen_VF)
```
```{r}
ggsave("VF_percenreads_FDRadjusted.png", units="in", width=9, height=7, dpi=400, bg="white")
```

```{r}
figure <- ggarrange(b,a,ncol=2, nrow=1, align="hv", labels=c("A","B"), font.label=list(size=14), common.legend=TRUE)
```
#save Fig. S11

```{r}
ggsave("Psoriasiscapitis_VFAMR.png", units="in", width=9, height=5, dpi=400, bg="white")
```

#Revisions May 14 2024; NMDS 

```{r}
Bacterialspecies <- read.csv2("Datasets/metaphlan4_species_relab.csv", sep=",",dec=".", row.names=1, header= TRUE )
mpa_table<- Bacterialspecies
```
```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, row.names=1,sep=",", dec =".", stringsAsFactors=FALSE)
```

```{r}
colSums(mpa_table)

```
```{r}
### Data transformation
mpa_table <- mpa_table / 100

```


```{r cars}

bug_mat = mpa_table|> 
  as.matrix() |>
  t()

```


```{r}
set.seed(123)  # Setting seed for reproducibility
nmds_res <- metaMDS(bug_mat, distance = "jaccard", k = 2, trymax = 100) #adapt to bray for Bray-Curtis

```

```{r}
nmds_scores <- scores(nmds_res, display = "sites")  # Extracting site scores (samples)
dim(nmds_scores) 
```

```{r}


nmds_scores_df <- as.data.frame(nmds_scores)
colnames(nmds_scores_df) <- c("NMDS1", "NMDS2")

nmds_meta <- bind_cols(nmds_scores_df, metadata)


```
```{r}
nmds_meta_new <- nmds_meta
nmds_meta_new$Body_site <- factor(nmds_meta_new$Body_site, levels = c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))

```
```{r}
#library(ggplot2)
#p_nmds_bray
p_nmds_jacc <- ggplot(nmds_meta_new, aes(x = NMDS1, y = NMDS2, color = Body_site)) +
  geom_point(aes(shape = Pool, fill = Body_site), size = 3) +
  scale_colour_manual(values = c("#018571", "#80cdc1", "#a6611a", "#dfc27d")) +
  labs(title = "NMDS - Jaccard", x = "NMDS1", y = "NMDS2")

p_nmds_jacc

```

```{r}
figure <- ggarrange(p_nmds_jacc, p_nmds_bray, ncol = 2, nrow = 1,common.legend = TRUE, legend ="right",align="hv", labels=c("A","B"), font.label=list(size=17))
```

save Fig S.5 
```{r}
ggsave("bray_curtis_jaccard_NMDS.png", units="in", width=10, height=5, dpi=400, bg="white")
```

#Rarefaction
#rarefaction of the data
```{r}
uparse_otus<-read.csv2("Datasets/metaphlan4_merged_species_readstats.csv", sep=",",row.names=1, dec=".", header=TRUE)
```

```{r}
otutab <- as.matrix(uparse_otus)
#rownames(otutab) <- paste0("OTU", 1:nrow(otutab))
#colnames(otutab) <- paste0("Sample", 1:ncol(otutab))
otutab
colSums(otutab)
min(colSums(otutab))
# Create a taxonomy table with the species names
tax_table <- data.frame(taxonomy = rownames(otutab))
taxmat <- as.matrix(tax_table)
rownames(taxmat) <- rownames(otutab)
colnames(taxmat) <-c("Species")

class(otutab)
class(taxmat)
```

```{r}
OTU = otu_table(otutab, taxa_are_rows = TRUE)
TAX = tax_table(taxmat)
OTU
TAX
physeq = phyloseq(OTU, TAX)
physeq
sample_names(physeq)
```
#RAREFACTION CURVES TO ANALYZE
```{r}
#without replacement and without trimming OTUs
oturarefied <- rarefy_even_depth(physeq, sample.size = min(sample_sums(physeq)), rngseed = 711, replace = FALSE, trimOTUs = FALSE)
```

```{r}
otutabrarefied = as.data.frame(otu_table(oturarefied))
```
```{r}

write.csv2(otutabrarefied,"otutabel_rarefied_metaphlan4qstat10.csv")

colSums(otutabrarefied)
```

```{r}
library("optparse")
library(microbiome)
library(ape)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(ggpubr)
```

```{r}
divdf_metaphlan <- data.frame(microbiome::alpha(otutabrarefied, index = c("richness_observed")),microbiome::alpha(otutabrarefied, index = c("diversity_shannon")), microbiome::alpha(otutabrarefied, index = c("diversity_gini_simpson")),microbiome::alpha(otutabrarefied, index = c("dominance_gini")))
divdf_metaphlan <- as.data.frame(divdf_metaphlan)

divdf_metaphlan$Sample_name <- rownames(divdf_metaphlan)
```


```{r}
metadata <- read.csv("Datasets/metadata_metaphlan.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE)
```
```{r}
metadata$Sample_name_full <- metadata$Sample_name
metadata$Sample_name <- metadata$Sample
```

```{r}
metadata_3 <- inner_join(divdf_metaphlan,metadata, by="Sample_name")
```

```{r}
metadata_3_new <- metadata_3                             # Duplicate data
metadata_3_new$Body_site <- factor(metadata_3_new$Body_site,     # Reorder factor levels
                         c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional"))

rownames(metadata_3_new) <- metadata_3_new$Sample_name
```
```{r}
my_comparisons <- list(
  c("Top_scalp", "Back_scalp"),
   c("Non_Lesional", "Lesional"),
  c("Lesional", "Back_scalp"),
  c("Lesional", "Top_scalp"),
  c("Non_Lesional", "Back_scalp"),
  c("Non_Lesional", "Top_scalp")
)
```
```{r}
a_micr<-ggplot(subset(metadata_3_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = observed, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + #scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Richness observed") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))   +theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)
```

```{r}
b_micr<-ggplot(subset(metadata_3_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = diversity_shannon, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + #scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Shannon") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)

```

```{r}
c_micr<-ggplot(subset(metadata_3_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = diversity_gini_simpson, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + #scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Simpson") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE)+ stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)
```

```{r}
d_micr<-ggplot(subset(metadata_3_new ,  Body_site %in% c("Top_scalp", "Back_scalp", "Lesional", "Non_Lesional")),aes(y = dominance_gini, x = Body_site, fill=Body_site) ) + 
  geom_boxplot(outlier.shape = NA) + theme_bw() +
  geom_point(position = position_jitter(width = 0.25), pch=21) + #scale_y_continuous(trans='log10')+ 
  theme(panel.grid = element_blank()) + 
  ylab("Dominance Gini") + xlab("") +  scale_fill_manual(values=c("#018571", "#80cdc1", "#a6611a","#dfc27d"))  +  theme(axis.title=element_text(size=21),axis.text.y=element_text(size=14),axis.text.x=element_text(size=20))+guides(fill=FALSE) + stat_compare_means(label= "p.signif",comparisons = my_comparisons,method="wilcox.test", p.adjust.method="BH", size=6)
```
```{r}
figure_rare <- ggarrange(a_micr,b_micr, c_micr, d_micr, ncol=2, nrow=2, align="hv", labels=c("A","B", "C", "D"), font.label=list(size=17))
```
```{r}
ggsave("alphadiverisity_rarefied_nonreplacenontrim.png", units="in", width=16, height=10, dpi=400, bg="white")
```

#qPCR ACTB and 18S rRNA gene 

```{r}
data_pso_all_16S <- read.csv("Datasets/qPCR_16S_ACTB_normalized.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE)
```
```{r}
data_pso_all_18S <- read.csv("Datasets/qPCR_18S_ACTB_normalized.csv", header=TRUE, dec=".", sep=",", stringsAsFactors=FALSE)
```

```{r}
# Assuming your dataframe is named data_pso_all_16S
subset_sebaceous <- data_pso_all_16S[data_pso_all_16S$site.type == "sebaceous", ]


```

```{r}

# Your ggplot code
qPCR_result <- ggplot(subset(subset_sebaceous, type %in% c("Lesional", "Non_Lesional")), aes(y = bacterial.norm, x = type, fill = type)) +  
  scale_y_continuous(trans = 'log10') +
  geom_boxplot(outlier.shape = NA) +  
  theme_bw() +  
  geom_point(position = position_jitter(width = 0.25), pch = 21, size=3) +
  theme(panel.grid = element_blank(), axis.text.x = element_text(size = 20), axis.text.y = element_text(size = 14), axis.title = element_text(size = 21)) +   
  ylab(expression(Log[10] ~ "(16S rRNA Copies/ACTB Copies)")) + xlab("") + scale_fill_manual(values = c("#a6611a", "#dfc27d"))  +guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = list(c("Lesional", "Non_Lesional")),method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8,size=6, bracket.size = 0.3, vjust=0)


```
```{r}
qPCR_result
```

```{r}
subset_sebaceous_18s <- data_pso_all_18S[data_pso_all_18S$site.type == "sebaceous", ]
```

```{r}

# Your ggplot code
qPCR_result_18S <- ggplot(subset(subset_sebaceous_18s, type %in% c("Lesional", "Non_Lesional")), aes(y = fungal.norm, x = type, fill = type)) +  
  scale_y_continuous(trans = 'log10') +
  geom_boxplot(outlier.shape = NA) +  
  theme_bw() +  
  geom_point(position = position_jitter(width = 0.25), pch = 21, size=3) +
  theme(panel.grid = element_blank(), axis.text.x = element_text(size = 20), axis.text.y = element_text(size = 14), axis.title = element_text(size = 21)) +   
  ylab(expression(Log[10] ~ "(18S rRNA Copies/ACTB Copies)")) + xlab("") + scale_fill_manual(values = c("#a6611a", "#dfc27d"))  +guides(fill=FALSE)+ 
  stat_compare_means(label= "p.signif",comparisons = list(c("Lesional", "Non_Lesional")),method="wilcox.test", p.adjust.method="BH", tip.length=0.01, textsize=8,size=6, bracket.size = 0.3, vjust=0)


```
```{r}
qPCR_result_18S
```

```{r}
figure_count <- ggarrange(log_decontam, qPCR_result, qPCR_result_18S, ncol=3, nrow=1, align="hv", labels=c("a","b", "c"), widths = c(2,1,1), font.label=list(size=17))
```
```{r}
ggsave("Fig.2_revisions.png", units="in", width=20, height=8, dpi=300, bg="white")
```