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Figure5_S5_V13_Beta-diversity.Rmd
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Figure5_S5_V13_Beta-diversity.Rmd
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---
title: "Figure 5 and S5: Beta-diversity for V1-V3"
author: "Marta Nierychlo and Morten Simonsen Dueholm"
date: "2021-08-25"
---
#Load packages
```{r load_packages, message=FALSE, warning=FALSE, echo=FALSE, include=FALSE}
library(ampvis2)
library(tidyverse)
library(vegan)
library(ggrepel)
library(patchwork)
```
#Load V13 data
```{r MD data}
#load metadata
seq_metadata <- read.csv("data/2020-07-03_Sequencing_metadata.txt", sep="\t")
wwtp_m <- read.csv("data/DataS1_210413.txt", sep="\t") %>%
mutate(ClimateZone2 = substr(ClimateZone,1,1))
#Subset metadata based on V1-V3 samples
seq_V13 <- seq_metadata[ , which(names(seq_metadata) %in% c("V13_seq_id","WWTP_id"))]
V13metadata <- merge.data.frame(seq_V13, wwtp_m, by ="WWTP_id", all.x = TRUE)
V13metadata$WWTP_ID <- V13metadata$WWTP_id
V13metadata <- V13metadata[,-1] %>%
mutate(V13_seq_id=gsub("-",".",V13_seq_id))
#Load V13ASV table and taxonomy manually
V13ASVtab <- read.csv("data/V13_ASVtab.txt", sep="\t") %>%
rename("ASV" = X.OTU.ID)
V13ASVtax <- read.csv("data/V13ASV_vs_MiDAS_4.8.sintax", sep="\t", header = FALSE) %>%
select(1,4) %>%
mutate(V4=gsub(":","__",V4)) %>%
separate(V4, c("Kingdom","Phylum","Class","Order","Family","Genus","Species"), sep=",") %>%
rename("ASV" = V1)
usearch_otutable <- merge.data.frame(V13ASVtab, V13ASVtax, by ="ASV", all.x = TRUE)
#Convert ASV to genus-table
usearch_gtable <- mutate(usearch_otutable, Species=NA) %>%
mutate(Kingdom=ifelse(is.na(Genus),NA,Kingdom)) %>%
mutate(Phylum=ifelse(is.na(Genus),NA,Phylum)) %>%
mutate(Class=ifelse(is.na(Genus),NA,Class)) %>%
mutate(Order=ifelse(is.na(Genus),NA,Order)) %>%
mutate(Family=ifelse(is.na(Genus),NA,Family)) %>%
mutate(OTU=substr(Genus,4,100)) %>%
group_by(OTU,Kingdom, Phylum, Class, Order, Family, Genus, Species) %>%
summarise(across(starts_with("V13"), sum)) %>%
relocate(c(Kingdom, Phylum, Class, Order, Family, Genus, Species), .after = last_col()) %>%
mutate(OTU=ifelse(is.na(OTU),"Unclassified",OTU))
d13 <- amp_load(otutable = usearch_gtable,
metadata = V13metadata)
d13n <- amp_subset_samples(d13, minreads = 10000, normalise = TRUE)
#Subset AS only in 4 basic process_type(s)
d13nAS <- amp_subset_samples(d13n, Plant_type == "Activated sludge")
d13nAS4PT <- amp_subset_samples(d13nAS, Process_type %in% c("C", "C,N", "C,N,DN", "C,N,DN,P"))
#remove ASVs with no Genus classification
ASVs_nogenus <- as.character(d13nAS4PT$tax[d13nAS4PT$tax$Genus == "" | is.na(d13nAS4PT$tax$Genus),"OTU"])
d13nAS4PT_genus <- amp_subset_taxa(d13nAS4PT, tax_vector = ASVs_nogenus, remove = TRUE)
```
##Create ordinations
```{r}
# Remove samples with missing metadata
d13nAS4PT_genus <- amp_subset_samples(d13nAS4PT_genus, !(is.na(Temp_range) | Temp_range==""))
d13nAS4PT_genus <- amp_subset_samples(d13nAS4PT_genus, !(is.na(ClimateZone2) | ClimateZone2==""))
d13nAS4PT_genus <- amp_subset_samples(d13nAS4PT_genus, !(is.na(Continent) | Continent==""))
d13nAS4PT_genus <- amp_subset_samples(d13nAS4PT_genus, !(is.na(ind_text) | ind_text==""))
d13nAS4PT_genus <- amp_subset_samples(d13nAS4PT_genus, !(is.na(Process_type) | Process_type==""))
# Order factors
d13nAS4PT_genus$metadata$Temp_range <- factor(d13nAS4PT_genus$metadata$Temp_range, levels=c("very low", "low", "moderate", "high", "very high", "extremely high"))
d13nAS4PT_genus$metadata$ind_text <- factor(d13nAS4PT_genus$metadata$ind_text, levels=c("none", "very_low", "low", "medium", "high", "all"))
d13nAS4PT_genus$metadata$Process_type <- factor(d13nAS4PT_genus$metadata$Process_type, levels=c("C", "C,N", "C,N,DN", "C,N,DN,P"))
######################
#~Process type
######################
# PCoA plot (Bray-Curtis diversity of genera)
p1a <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="none",
distmeasure = "bray",
sample_color_by = "Process_type",
sample_point_size = 3,
filter_species = 0,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p1a$layers[[1]]$aes_params$alpha <- 1
# PCoA plot (Soerensen diversity of genera)
p1b <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="pa",
distmeasure = "bray",
sample_color_by = "Process_type",
sample_point_size = 3,
filter_species = 0.001,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p1b$layers[[1]]$aes_params$alpha <- 1
# RDA plot
p1c_raw <- amp_ordinate(d13nAS4PT_genus,
type = "RDA",
constrain = "Process_type",
sample_color_by = "Process_type",
sample_point_size = 3,
filter_species = 0,
transform = "hellinger",
sample_colorframe = FALSE,
detailed_output = TRUE)
p1c <- p1c_raw$plot +
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p1c$layers[[1]]$aes_params$alpha <- 1
RDA_scores1 <- as.data.frame(scores(p1c$plot_env$model, display = "species")) %>%
arrange(desc(abs(RDA1))) %>%
rename(Process_type_RDA1 = RDA1, Process_type_RDA2 = RDA2)
RDA_scores1$Genus <- rownames(RDA_scores1)
p1c <- p1c + geom_point(mapping = aes(Process_type_RDA1, Process_type_RDA2), data=RDA_scores1[1:10,], colour="black",size=2) +
geom_label_repel(mapping=aes(x=Process_type_RDA1, y=Process_type_RDA2,label = Genus),data=RDA_scores1[1:10,],
colour="black", max.overlaps = 100)
######################
#~Industrial load
######################
# PCoA plot (Bray-Curtis diversity of genera)
p2a <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="none",
distmeasure = "bray",
sample_color_by = "ind_text",
sample_point_size = 3,
filter_species = 0,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction = -1)
p2a$layers[[1]]$aes_params$alpha <- 1
# PCoA plot (Soerensen diversity of genera)
p2b <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="pa",
distmeasure = "bray",
sample_color_by = "ind_text",
sample_point_size = 3,
filter_species = 0.001,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction = -1)
p2b$layers[[1]]$aes_params$alpha <- 1
# RDA plot
p2c_raw <- amp_ordinate(d13nAS4PT_genus,
type = "RDA",
constrain = "ind_text",
sample_color_by = "ind_text",
sample_point_size = 3,
filter_species = 0,
transform = "hellinger",
sample_colorframe = FALSE,
detailed_output = TRUE)
p2c <- p2c_raw$plot +
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction = -1)
p2c$layers[[1]]$aes_params$alpha <- 1
RDA_scores2 <- as.data.frame(scores(p2c$plot_env$model, display = "species")) %>%
arrange(desc(abs(RDA1))) %>%
rename(Industrial_load_RDA1 = RDA1, Industrial_load_RDA2 = RDA2)
RDA_scores2$Genus <- rownames(RDA_scores2)
p2c <- p2c + geom_point(mapping = aes(Industrial_load_RDA1, Industrial_load_RDA2), data=RDA_scores2[1:10,], colour="black",size=2) +
geom_label_repel(mapping=aes(x=Industrial_load_RDA1, y=Industrial_load_RDA2,label = Genus),data=RDA_scores2[1:10,],
colour="black", max.overlaps = 100)
######################
#~Temp_range
######################
# PCoA plot (Bray-Curtis diversity of genera)
p3a <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="none",
distmeasure = "bray",
sample_color_by = "Temp_range",
sample_point_size = 3,
filter_species = 0,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction=-1)
p3a$layers[[1]]$aes_params$alpha <- 1
# PCoA plot (Soerensen diversity of genera)
p3b <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="pa",
distmeasure = "bray",
sample_color_by = "Temp_range",
sample_point_size = 3,
filter_species = 0.001,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction=-1)
p3b$layers[[1]]$aes_params$alpha <- 1
# RDA plot
p3c_raw <- amp_ordinate(d13nAS4PT_genus,
type = "RDA",
constrain = "Temp_range",
sample_color_by = "Temp_range",
sample_point_size = 3,
filter_species = 0,
transform = "hellinger",
sample_colorframe = FALSE,
detailed_output = TRUE)
p3c <- p3c_raw$plot +
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu", direction=-1)
p3c$layers[[1]]$aes_params$alpha <- 1
RDA_scores3 <- as.data.frame(scores(p3c$plot_env$model, display = "species")) %>%
arrange(desc(abs(RDA1))) %>%
rename(Temperature_RDA1 = RDA1, Temperature_RDA2 = RDA2)
RDA_scores3$Genus <- rownames(RDA_scores3)
p3c <- p3c + geom_point(mapping = aes(Temperature_RDA1, Temperature_RDA2), data=RDA_scores3[1:10,], colour="black",size=2) + geom_label_repel(mapping=aes(x=Temperature_RDA1, y=Temperature_RDA2,label = Genus),data=RDA_scores3[1:10,],
colour="black", max.overlaps = 100)
######################
#~Continent
######################
# PCoA plot (Bray-Curtis diversity of genera)
p4a <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="none",
distmeasure = "bray",
sample_color_by = "Continent",
sample_point_size = 3,
filter_species = 0,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p4a$layers[[1]]$aes_params$alpha <- 1
# PCoA plot (Soerensen diversity of genera)
p4b <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="pa",
distmeasure = "bray",
sample_color_by = "Continent",
sample_point_size = 3,
filter_species = 0.001,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p4b$layers[[1]]$aes_params$alpha <- 1
# RDA plot
p4c_raw <- amp_ordinate(d13nAS4PT_genus,
type = "RDA",
constrain = "Continent",
sample_color_by = "Continent",
sample_point_size = 3,
filter_species = 0,
transform = "hellinger",
sample_colorframe = FALSE,
detailed_output = TRUE)
p4c <- p4c_raw$plot +
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p4c$layers[[1]]$aes_params$alpha <- 1
RDA_scores4 <- as.data.frame(scores(p4c$plot_env$model, display = "species")) %>%
arrange(desc(abs(RDA1))) %>%
rename(Continent_RDA1 = RDA1, Continent_RDA2 = RDA2)
RDA_scores4$Genus <- rownames(RDA_scores4)
p4c <- p4c + geom_point(mapping = aes(Continent_RDA1, Continent_RDA2), data=RDA_scores4[1:10,], colour="black",size=2) + geom_label_repel(mapping=aes(x=Continent_RDA1, y=Continent_RDA2,label = Genus),data=RDA_scores4[1:10,],
colour="black", max.overlaps = 100)
######################
#~Climate Zone
######################
# PCoA plot (Bray-Curtis diversity of genera)
p5a <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="none",
distmeasure = "bray",
sample_color_by = "ClimateZone2",
sample_point_size = 3,
filter_species = 0,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p5a$layers[[1]]$aes_params$alpha <- 1
# PCoA plot (Soerensen diversity of genera)
p5b <- amp_ordinate(d13nAS4PT_genus,
type = "PCoA",
transform="pa",
distmeasure = "bray",
sample_color_by = "ClimateZone2",
sample_point_size = 3,
filter_species = 0.001,
sample_colorframe = FALSE)+
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 20),
axis.title.y=element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p5b$layers[[1]]$aes_params$alpha <- 1
# RDA plot
p5c_raw <- amp_ordinate(d13nAS4PT_genus,
type = "RDA",
constrain = "ClimateZone2",
sample_color_by = "ClimateZone2",
sample_point_size = 3,
filter_species = 0,
transform = "hellinger",
sample_colorframe = FALSE,
detailed_output = TRUE)
p5c <- p5c_raw$plot +
theme_bw() +
theme(legend.position = "top",
text = element_text(size = 20),
axis.text.y = element_text(colour = "black"),
axis.text.x = element_text(colour = "black"))+
scale_color_brewer(palette="RdYlBu")
p5c$layers[[1]]$aes_params$alpha <- 1
RDA_scores5 <- as.data.frame(scores(p5c$plot_env$model, display = "species")) %>%
arrange(desc(abs(RDA1))) %>%
rename(ClimateZone_RDA1 = RDA1, ClimateZone_RDA2 = RDA2)
RDA_scores5$Genus <- rownames(RDA_scores5)
p5c <- p5c + geom_point(mapping = aes(ClimateZone_RDA1, ClimateZone_RDA2), data=RDA_scores5[1:10,], colour="black",size=2) + geom_label_repel(mapping=aes(x=ClimateZone_RDA1, y=ClimateZone_RDA2,label = Genus),data=RDA_scores5[1:10,],
colour="black", max.overlaps = 100)
######################
#~Combined
######################
p1 <- ( p1a | p2a | p3a | p4a | p5a ) / ( p1b | p2b | p3b | p4b | p5b )
ggsave(filename="output/Figure5_V13_Beta-diversity_PCoA.pdf", plot=p1, width=22, height=10, useDingbats=FALSE, limitsize=FALSE)
p2 <- ( p1c | p2c | p2c ) / (p3c | p4c | p5c )
ggsave(filename="output/FigureS5_V13_Beta-diversity_RDA.pdf", plot=p2, width=15, height=11.5, useDingbats=FALSE, limitsize=FALSE)
RDA_scores <- merge(RDA_scores1, RDA_scores2, by = "Genus", all=TRUE)
RDA_scores <- merge(RDA_scores, RDA_scores3, by = "Genus", all=TRUE)
RDA_scores <- merge(RDA_scores, RDA_scores4, by = "Genus", all=TRUE)
RDA_scores <- merge(RDA_scores, RDA_scores5, by = "Genus", all=TRUE)
RDA_scores[is.na(RDA_scores)] = 0
write.csv(RDA_scores,"output/DataS3_V13.csv", row.names = FALSE, quote = FALSE)
######################
#~ADONIS analyses
######################
bc.dist.matrix <- vegdist(t(d13nAS4PT_genus$abund), method = "bray")
s.dist.matrix <- vegdist(t(d13nAS4PT_genus$abund), method = "bray", binary = TRUE)
metadata <- d13nAS4PT_genus$metadata
# Temp_range
adonis(bc.dist.matrix ~ Temp_range, data = metadata)
adonis(s.dist.matrix ~ Temp_range, data = metadata)
# Climate zone
adonis(bc.dist.matrix ~ ClimateZone2, data = metadata)
adonis(s.dist.matrix ~ ClimateZone2, data = metadata)
# Continent
adonis(bc.dist.matrix ~ Continent, data = metadata)
adonis(s.dist.matrix ~ Continent, data = metadata)
# Industrial load
adonis(bc.dist.matrix ~ ind_text, data = metadata)
adonis(s.dist.matrix ~ ind_text, data = metadata)
# Process type
adonis(bc.dist.matrix ~ Process_type, data = metadata)
adonis(s.dist.matrix ~ Process_type, data = metadata)
```