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14- Top 15 genus.others.Rmd
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14- Top 15 genus.others.Rmd
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---
title: "Top four phyla"
author: "Marwa Tawfik"
date: "07 07 2023"
output: html_document
---
Summary:
At level of ASVs of intestine, top 15 were identified in intestine and significant ones are identified
Also at level of ASVs of all sample type (intestine, water and feed) or intestine only, top 15 were identified in water. NO signigicant difference either at all sample or intestine
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
##Load the required packages
```{r}
library(tidyverse)
library(cowplot)
library(patchwork)
library(ggh4x)
library(PerformanceAnalytics)
library(RColorBrewer)
library(microbiome)
library(officer)
library(ggsignif)
library(Rcpp)
library(dada2)
library(phyloseq)
library(permute)
library(lattice)
library(vegan)
library(ggplot2)
library(tidyverse)
library(ggstatsplot)
library(dplyr)
library(tidyr)
library(microbiome)
library(microbiomeutilities)
library(knitr)
library(RColorBrewer)
library(DT)
library(gt)
library(cowplot)
library(PerformanceAnalytics)
library(venn)
library(philr)
library(MicrobeR)
library(ape)
library(Biostrings)
library(gridExtra)
library(ggsignif)
library(lmerTest)
library(ggResidpanel)
library(emmeans)
library(picante)
library(ggtext)
library(webshot2)
library(speedyseq)
set.seed(1910)
library(ggpubr)
```
##Set the working driectory and read the saved phyloseq object
```{r}
ps.prev <- readRDS("phyobjects/STEP 15/ps.prev.f.rds")
ps.prev.intes <- readRDS("phyobjects/STEP 15/ps.prev.intes.f.refseq.rds")
ps.prev.feed <- subset_samples(ps.prev, sample == "feed")
ps.prev.feed <- prune_taxa(taxa_sums(ps.prev.feed) > 0, ps.prev.feed)
ps.prev.water <- subset_samples(ps.prev, sample == "water")
ps.prev.water <- prune_taxa(taxa_sums(ps.prev.water) > 0, ps.prev.water)
# saveRDS(ps.prev.feed, "phyobjects/ps.prev.feed.f.rds")
# saveRDS(ps.prev.water, "phyobjects/ps.prev.water.f.rds")
```
```{r}
# # filter samples in case needed
# ps.prev <- ps.prev %>%
# # remove the control samples
# subset_samples(!Sample_type %in% c("control", "Positive_control"))
# change OTU names for easy display
indx <- formatC(1:ntaxa(ps.prev), width = nchar(ntaxa(ps.prev)), format = "d", flag = "0")
taxa_names(ps.prev) <- paste0("OTU", indx)
# extract otu table, taxonomy and metadata
otu <- as.data.frame(t(otu_table(ps.prev)))
txnm <- tax_table(ps.prev) %>% as("matrix") %>% as_tibble(rownames = NA)
mtd <- data.frame(sample_data(ps.prev), check.names = FALSE)
# intes
indx.intes <- formatC(1:ntaxa(ps.prev.intes), width = nchar(ntaxa(ps.prev.intes)), format = "d", flag = "0")
taxa_names(ps.prev.intes) <- paste0("OTU", indx.intes)
# extract otu.intes table, taxonomy and metadata
otu.intes <- as.data.frame(t(otu_table(ps.prev.intes)))
txnm.intes <- tax_table(ps.prev.intes) %>% as("matrix") %>% as_tibble(rownames = NA)
mtd.intes <- data.frame(sample_data(ps.prev.intes), check.names = FALSE)
# feed
indx.feed <- formatC(1:ntaxa(ps.prev.feed), width = nchar(ntaxa(ps.prev.feed)), format = "d", flag = "0")
taxa_names(ps.prev.feed) <- paste0("OTU", indx.feed)
# extract otu.feed table, taxonomy and metadata
otu.feed <- as.data.frame(t(otu_table(ps.prev.feed)))
txnm.feed <- tax_table(ps.prev.feed) %>% as("matrix") %>% as_tibble(rownames = NA)
mtd.feed <- data.frame(sample_data(ps.prev.feed), check.names = FALSE)
# water
indx.water <- formatC(1:ntaxa(ps.prev.water), width = nchar(ntaxa(ps.prev.water)), format = "d", flag = "0")
taxa_names(ps.prev.water) <- paste0("OTU", indx.water)
# extract otu.water table, taxonomy and metadata
otu.water <- as.data.frame(t(otu_table(ps.prev.water)))
txnm.water <- tax_table(ps.prev.water) %>% as("matrix") %>% as_tibble(rownames = NA)
mtd.water <- data.frame(sample_data(ps.prev.water), check.names = FALSE)
```
# Taxonomic assignment overview
First of all, let's look at the coverage of taxonomic assignments at different rank levels.
```{r}
txnm_assg <- txnm %>%
gather("Rank", "Name", rank_names(ps.prev)) %>%
group_by(Rank) %>%
# Empty taxonomic ranks may be na or strings containing "uncultured" or "Ambiguous_taxa"
summarize(OTUs_classified = sum(!is.na(Name) & !grepl("uncultured|Ambiguous|metagenome", Name))) %>%
mutate(
Frac_classified = OTUs_classified / ntaxa(ps.prev),
Frac_classified = ifelse(
Frac_classified == 1,
"100",
round(Frac_classified * 100, 1)),
Frac_classified = paste(Frac_classified, "%"),
Rank = factor(Rank, rank_names(ps.prev))) %>%
arrange(Rank)
kable(txnm_assg, align = 'lcccc', caption = "Taxonomic Assignment")
# Rank OTUs_classified Frac_classified
# 1 Kingdom 3605 100 %
# 2 Phylum 3605 100 %
# 3 Class 3605 100 %
# 4 Order 3605 100 %
# 5 Family 3605 100 %
# 6 Genus 2845 78.9 %
# 7 Species 388 10.8 %
```
```{r}
## extra
# intes
txnm.intes_assg <- txnm.intes %>%
gather("Rank", "Name", rank_names(ps.prev.intes)) %>%
group_by(Rank) %>%
# Empty taxonomic ranks may be na or strings containing "uncultured" or "Ambiguous_taxa"
summarize(OTUs_classified = sum(!is.na(Name) & !grepl("uncultured|Ambiguous|metagenome", Name))) %>%
mutate(
Frac_classified = OTUs_classified / ntaxa(ps.prev.intes),
Frac_classified = ifelse(
Frac_classified == 1,
"100",
round(Frac_classified * 100, 1)),
Frac_classified = paste(Frac_classified, "%"),
Rank = factor(Rank, rank_names(ps.prev.intes))) %>%
arrange(Rank)
kable(txnm.intes_assg, align = 'lcccc', caption = "Taxonomic Assignment")
# Rank OTUs_classified Frac_classified
# 1 Kingdom 1544 100 %
# 2 Phylum 1544 100 %
# 3 Class 1544 100 %
# 4 Order 1544 100 %
# 5 Family 1544 100 %
# 6 Genus 1221 79.1 %
# 7 Species 183 11.9 %
```
```{r}
# water
txnm.water_assg <- txnm.water %>%
gather("Rank", "Name", rank_names(ps.prev.water)) %>%
group_by(Rank) %>%
# Empty taxonomic ranks may be na or strings containing "uncultured" or "Ambiguous_taxa"
summarize(OTUs_classified = sum(!is.na(Name) & !grepl("uncultured|Ambiguous|metagenome", Name))) %>%
mutate(
Frac_classified = OTUs_classified / ntaxa(ps.prev.water),
Frac_classified = ifelse(
Frac_classified == 1,
"100",
round(Frac_classified * 100, 1)),
Frac_classified = paste(Frac_classified, "%"),
Rank = factor(Rank, rank_names(ps.prev.water))) %>%
arrange(Rank)
kable(txnm_assg, align = 'lcccc', caption = "Taxonomic Assignment")
# Rank OTUs_classified Frac_classified
# 1 Kingdom 1310 100 %
# 2 Phylum 1310 100 %
# 3 Class 1310 100 %
# 4 Order 1310 100 %
# 5 Family 1310 100 %
# 6 Genus 969 74 %
# 7 Species 71 5.4 %
```
```{r}
# feed
txnm.feed_assg <- txnm.feed %>%
gather("Rank", "Name", rank_names(ps.prev.feed)) %>%
group_by(Rank) %>%
# Empty taxonomic ranks may be na or strings containing "uncultured" or "Ambiguous_taxa"
summarize(OTUs_classified = sum(!is.na(Name) & !grepl("uncultured|Ambiguous|metagenome", Name))) %>%
mutate(
Frac_classified = OTUs_classified / ntaxa(ps.prev.feed),
Frac_classified = ifelse(
Frac_classified == 1,
"100",
round(Frac_classified * 100, 1)),
Frac_classified = paste(Frac_classified, "%"),
Rank = factor(Rank, rank_names(ps.prev.feed))) %>%
arrange(Rank)
kable(txnm_assg, align = 'lcccc', caption = "Taxonomic Assignment")
# Rank OTUs_classified Frac_classified
# 1 Kingdom 1065 100 %
# 2 Phylum 1065 100 %
# 3 Class 1065 100 %
# 4 Order 1065 100 %
# 5 Family 1065 100 %
# 6 Genus 930 87.3 %
# 7 Species 197 18.5 %
```
## Biological samples
### Top15 genera
```{r}
taxa_tab <- Summarize.Taxa(otu, txnm)$Genus %>% Make.Percent()
taxa_tab <- taxa_tab[order(rowMeans(taxa_tab), decreasing = T), ]
Others <- colSums(taxa_tab[16:nrow(taxa_tab), ])
taxa_tab <- rbind(taxa_tab[1:15, ], Others)
```
```{r}
# intes
taxa_tab.intes <- Summarize.Taxa(otu.intes, txnm.intes)$Genus %>% Make.Percent()
taxa_tab.intes <- taxa_tab.intes[order(rowMeans(taxa_tab.intes), decreasing = T), ]
OTHERS <- colSums(taxa_tab.intes[16:nrow(taxa_tab.intes), ])
taxa_tab.intes <- rbind(taxa_tab.intes[1:15, ], OTHERS)
```
```{r}
# feed
taxa_tab.feed <- Summarize.Taxa(otu.feed, txnm.feed)$Genus %>% Make.Percent()
taxa_tab.feed <- taxa_tab.feed[order(rowMeans(taxa_tab.feed), decreasing = T), ]
others <- colSums(taxa_tab.feed[16:nrow(taxa_tab.feed), ])
taxa_tab.feed <- rbind(taxa_tab.feed[1:15, ], others)
```
```{r}
# water
taxa_tab.water <- Summarize.Taxa(otu.water, txnm.water)$Genus %>% Make.Percent()
taxa_tab.water <- taxa_tab.water[order(rowMeans(taxa_tab.water), decreasing = T), ]
OTHERs <- colSums(taxa_tab.water[16:nrow(taxa_tab.water), ])
taxa_tab.water <- rbind(taxa_tab.water[1:15, ], OTHERs)
```
Tidy taxa table for making stacked box plots.
```{r}
taxa_tab <- as.data.frame(taxa_tab) %>%
rownames_to_column("Taxa") %>%
separate(
Taxa,
sep = ";",
c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus")) %>%
mutate(
Phylum = ifelse(
is.na(Phylum)|Phylum == "NA"|grepl("uncultured|Ambiguous|metagenome", Phylum),
Kingdom,
Phylum),
Class = ifelse(
is.na(Class)|Class == "NA"|grepl("uncultured|Ambiguous|metagenome", Class),
Phylum,
Class),
Order = ifelse(
is.na(Order)|Order == "NA"|grepl("uncultured|Ambiguous|metagenome", Order),
Class,
Order),
Family = ifelse(
is.na(Family)|Family == "NA"|grepl("uncultured|Ambiguous|metagenome", Family),
Order,
Family),
Genus = ifelse(
is.na(Genus)|Genus == "NA"|grepl("uncultured|Ambiguous|metagenome", Genus),
Family,
Genus)) %>%
select(-Kingdom, -(Class:Family))
taxa_tab1 <- taxa_tab %>%
mutate(
Phylum = gsub("p__", "", Phylum),
Phylum = factor(Phylum, levels = rev(unique(Phylum))),
Genus = gsub("g__", "", Genus),
Genus = factor(Genus, levels = rev(unique(Genus)))) %>%
arrange(Phylum, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
pivot_longer(-c(Phylum, Genus), names_to = "SampleID", values_to = "Abundance") %>%
inner_join(rownames_to_column(mtd, "SampleID"), by = "SampleID")
# openxlsx::write.xlsx(taxa_tab1, file = "tables/taxatable for gut.xlsx")
```
```{r}
# intes
taxa_tab.intes <- as.data.frame(taxa_tab.intes) %>%
rownames_to_column("Taxa.intes") %>%
separate(
Taxa.intes,
sep = ";",
c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus")) %>%
mutate(
Phylum = ifelse(
is.na(Phylum)|Phylum == "NA"|grepl("uncultured|Ambiguous|metagenome", Phylum),
Kingdom,
Phylum),
Class = ifelse(
is.na(Class)|Class == "NA"|grepl("uncultured|Ambiguous|metagenome", Class),
Phylum,
Class),
Order = ifelse(
is.na(Order)|Order == "NA"|grepl("uncultured|Ambiguous|metagenome", Order),
Class,
Order),
Family = ifelse(
is.na(Family)|Family == "NA"|grepl("uncultured|Ambiguous|metagenome", Family),
Order,
Family),
Genus = ifelse(
is.na(Genus)|Genus == "NA"|grepl("uncultured|Ambiguous|metagenome", Genus),
Family,
Genus)) %>%
select(-Kingdom, -(Class:Family))
taxa_tab.intes1 <- taxa_tab.intes %>%
mutate(
Phylum = gsub("p__", "", Phylum),
Phylum = factor(Phylum, levels = rev(unique(Phylum))),
Genus = gsub("g__", "", Genus),
Genus = factor(Genus, levels = rev(unique(Genus)))) %>%
arrange(Phylum, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
pivot_longer(-c(Phylum, Genus), names_to = "SampleID", values_to = "Abundance") %>%
inner_join(rownames_to_column(mtd.intes, "SampleID"), by = "SampleID")
# openxlsx::write.xlsx(taxa_tab.intes1, file = "tables/taxatable at level of gut for gut.xlsx")
```
```{r}
# feed
taxa_tab.feed <- as.data.frame(taxa_tab.feed) %>%
rownames_to_column("Taxa.feed") %>%
separate(
Taxa.feed,
sep = ";",
c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus")) %>%
mutate(
Phylum = ifelse(
is.na(Phylum)|Phylum == "NA"|grepl("uncultured|Ambiguous|metagenome", Phylum),
Kingdom,
Phylum),
Class = ifelse(
is.na(Class)|Class == "NA"|grepl("uncultured|Ambiguous|metagenome", Class),
Phylum,
Class),
Order = ifelse(
is.na(Order)|Order == "NA"|grepl("uncultured|Ambiguous|metagenome", Order),
Class,
Order),
Family = ifelse(
is.na(Family)|Family == "NA"|grepl("uncultured|Ambiguous|metagenome", Family),
Order,
Family),
Genus = ifelse(
is.na(Genus)|Genus == "NA"|grepl("uncultured|Ambiguous|metagenome", Genus),
Family,
Genus)) %>%
select(-Kingdom, -(Class:Family))
taxa_tab.feed1 <- taxa_tab.feed %>%
mutate(
Phylum = gsub("p__", "", Phylum),
Phylum = factor(Phylum, levels = rev(unique(Phylum))),
Genus = gsub("g__", "", Genus),
Genus = factor(Genus, levels = rev(unique(Genus)))) %>%
arrange(Phylum, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
pivot_longer(-c(Phylum, Genus), names_to = "SampleID", values_to = "Abundance") %>%
inner_join(rownames_to_column(mtd.feed, "SampleID"), by = "SampleID")
# openxlsx::write.xlsx(taxa_tab.feed1, file = "tables/taxatable at level of feed for feed.xlsx")
```
```{r}
# water
taxa_tab.water <- as.data.frame(taxa_tab.water) %>%
rownames_to_column("Taxa.water") %>%
separate(
Taxa.water,
sep = ";",
c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus")) %>%
mutate(
Phylum = ifelse(
is.na(Phylum)|Phylum == "NA"|grepl("uncultured|Ambiguous|metagenome", Phylum),
Kingdom,
Phylum),
Class = ifelse(
is.na(Class)|Class == "NA"|grepl("uncultured|Ambiguous|metagenome", Class),
Phylum,
Class),
Order = ifelse(
is.na(Order)|Order == "NA"|grepl("uncultured|Ambiguous|metagenome", Order),
Class,
Order),
Family = ifelse(
is.na(Family)|Family == "NA"|grepl("uncultured|Ambiguous|metagenome", Family),
Order,
Family),
Genus = ifelse(
is.na(Genus)|Genus == "NA"|grepl("uncultured|Ambiguous|metagenome", Genus),
Family,
Genus)) %>%
select(-Kingdom, -(Class:Family))
taxa_tab.water1 <- taxa_tab.water %>%
mutate(
Phylum = gsub("p__", "", Phylum),
Phylum = factor(Phylum, levels = rev(unique(Phylum))),
Genus = gsub("g__", "", Genus),
Genus = factor(Genus, levels = rev(unique(Genus)))) %>%
arrange(Phylum, Genus) %>%
mutate(Genus = factor(Genus, levels = unique(Genus))) %>%
pivot_longer(-c(Phylum, Genus), names_to = "SampleID", values_to = "Abundance") %>%
inner_join(rownames_to_column(mtd.water, "SampleID"), by = "SampleID")
#openxlsx::write.xlsx(taxa_tab.water1, file = "tables/taxatable at level of water for water.xlsx")
```
```{r}
unique(taxa_tab1$Genus)
# [1] Others Arsenicibacter Kocuria
# [4] Sphingomonas Photobacterium Pseudorhodobacter
# [7] Escherichia-Shigella Acinetobacter Yersinia
# [10] Aeromonas Pseudomonas Leuconostoc
# [13] Staphylococcus Enterococcus Lactobacillus
# [16] Ruminococcaceae
# 16 Levels: Others Arsenicibacter Kocuria Sphingomonas ... Ruminococcaceae
```
```{r}
unique(taxa_tab.intes1$Genus)
# [1] OTHERS Cutibacterium Corynebacterium
# [4] Kocuria Enterobacter Stenotrophomonas
# [7] Sphingomonas Escherichia-Shigella Acinetobacter
# [10] Yersinia Aeromonas Pseudomonas
# [13] Staphylococcus Lactobacillus Enterococcus
# [16] Ruminococcaceae
# 16 Levels: OTHERS Cutibacterium Corynebacterium Kocuria ... Ruminococcaceae
```
```{r}
unique(taxa_tab.feed1$Genus)
# [1] others Psychrilyobacter
# [3] Fusobacterium Bifidobacteriaceae
# [5] Janthinobacterium Shewanella
# [7] Photobacterium Tepidimicrobium
# [9] Clostridium sensu stricto 7 Clostridiaceae
# [11] Streptococcus Weissella
# [13] Pediococcus Leuconostoc
# [15] Lactococcus Lactobacillus
# 16 Levels: others Psychrilyobacter Fusobacterium ... Lactobacillus
```
```{r}
unique(taxa_tab.water1$Genus)
# [1] OTHERs Fimbriimonadaceae Microtrichaceae
# [4] Luteolibacter NS11-12 marine group Flavobacterium
# [7] Pedobacter Arsenicibacter Sphaerotilus
# [10] Novosphingobium Sphingomonadaceae Rhodoferax
# [13] AAP99 Rhodobacter Polaromonas
# [16] Pseudorhodobacter
# 16 Levels: OTHERs Fimbriimonadaceae Microtrichaceae ... Pseudorhodobacter
```
### Boxplot to abundance of individual dominant taxa in the gut samples grouped by Region_Regime
```{r, fig.width=10}
# top 15 in intes at all level of all sample type
# define color scheme
# col <- c("lightblue", "lightgreen")
mtd$Region_Regime <- factor(mtd$Region_Regime, levels = c("pyloric.MMV",
"pyloric.VMV",
"middle.MMV",
"middle.VMV",
"distal.MMV",
"distal.VMV", "feed.M", "feed.V", "water.MMV", "water.VMV"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Pseudomonas", "Acinetobacter", "Photobacterium", "Kocuria","Escherichia-Shigella", "Yersinia", "Staphylococcus", "Lactobacillus", "Leuconostoc", "Enterococcus", "Arsenicibacter", "Aeromonas", "Sphingomonas","Pseudorhodobacter", "Others"))
# gut samples
taxa_boxplot_gut <- filter(taxa_tab1, sample == "gut") %>%
ggplot(aes(x = factor(Region, c("pyloric",
"middle",
"distal", "feed", "water")), y = Abundance, color = Regime)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Region", y = "Relative abundance (%)") +
# scale_colour_manual(values = c("lightblue", "lightgreen")) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() #+
#theme(legend.position = "bottom")
# Export the plot
#ggsave("figures/top15_intes.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
```{r, fig.width=10}
# intes at level of intestine
# define color scheme
# col <- c("lightblue", "lightgreen")
mtd.intes$Region_Regime <- factor(mtd.intes$Region_Regime, levels = c("pyloric.MMV",
"pyloric.VMV",
"middle.MMV",
"middle.VMV",
"distal.MMV",
"distal.VMV", "feed.M", "feed.V", "water.MMV", "water.VMV"))
taxa_tab.intes1$Genus2 <- factor(taxa_tab.intes1$Genus, levels = c("Ruminococcaceae", "Pseudomonas", "Acinetobacter", "Enterobacter", "Kocuria","Escherichia-Shigella", "Yersinia", "Staphylococcus", "Lactobacillus", "Cutibacterium", "Enterococcus", "Corynebacterium", "Aeromonas", "Sphingomonas","Stenotrophomonas", "OTHERS"))
# gut samples
taxa_boxplot_gut <- filter(taxa_tab.intes1, sample == "gut") %>%
ggplot(aes(x = factor(Region, c("pyloric",
"middle",
"distal", "feed", "water")), y = Abundance, color = Regime)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Region", y = "Relative abundance (%)") +
scale_color_manual(values = c("#C71585", "#008080"), labels = c("MMV", "VMV")) +
guides(color = guide_legend(title = "Regime")) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() #+
#theme(legend.position = "bottom")
# Export the plot
#ggsave("figures/figS6_top15_intes.intes.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
## Boxplot to abundance of individual dominant taxa in the feed samples grouped by Region_Regime
```{r, fig.width=10}
# feed
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd$Regime <- factor(mtd$Regime, levels = c("M", "V", "MMV","VMV"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Pseudomonas", "Acinetobacter", "Photobacterium", "Kocuria","Escherichia-Shigella", "Yersinia", "Staphylococcus", "Lactobacillus", "Leuconostoc", "Enterococcus", "Arsenicibacter", "Aeromonas", "Sphingomonas","Pseudorhodobacter", "Others"))
# gut samples
taxa_boxplot_feed <- filter(taxa_tab1, sample == "feed") %>%
ggplot(aes(x = factor(Regime, c("M", "V")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
#ggsave("figures/top15_feed.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
```{r}
# feed at level of feed
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd.feed$Regime <- factor(mtd.feed$Regime, levels = c("M", "V"))
taxa_tab.feed1$Genus2 <- reorder(taxa_tab.feed1$Genus, taxa_tab.feed1$Abundance)
# gut samples
taxa_boxplot_feed <- filter(taxa_tab.feed1, sample == "feed") %>%
ggplot(aes(x = factor(Regime, c("M", "V")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
#ggsave("figures/top15.feed_feed.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
## Boxplot to abundance of individual dominant taxa in the water samples grouped by Region_Regime
```{r, fig.width=10}
# water
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd$Regime <- factor(mtd$Regime, levels = c("M", "V", "MMV", "VMV"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Pseudomonas", "Acinetobacter", "Photobacterium", "Kocuria","Escherichia-Shigella", "Yersinia", "Staphylococcus", "Lactobacillus", "Leuconostoc", "Enterococcus", "Arsenicibacter", "Aeromonas", "Sphingomonas","Pseudorhodobacter", "Others"))
# gut samples
taxa_boxplot_gut <- filter(taxa_tab1, sample == "water") %>%
ggplot(aes(x = factor(Regime, c("MMV", "VMV")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
#ggsave("figures/top15_wtr.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
```{r}
# water at level of water
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd.water$Regime <- factor(mtd.water$Regime, levels = c("MMV", "VMV"))
taxa_tab.water1$Genus2 <- reorder(taxa_tab.water1$Genus, taxa_tab.water1$Abundance)
# watertine samples
taxa_boxplot_water.water <- filter(taxa_tab.water1, sample == "water") %>%
ggplot(aes(x = factor(Regime, c("MMV", "VMV")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
#ggsave("figures/top15.water_wtr.tiff", width = 10, height = 6, units = "in", dpi = 300, compression = "lzw")
```
##Flitering the dataframe into each individual taxonomic
```{r}
# gut
##Filter only the dataframe for only gut
intes.taxa_tab2 <- filter(taxa_tab1, sample == "gut")
##Filter taxa_tab2 dataframe for only Acinetobacter
intes.taxa_ac <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Acinetobacter")
##Filter taxa_tab2 dataframe for only the Aeromonas
intes.taxa_ar <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Arsenicibacter
intes.taxa_as <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Arsenicibacter")
##Filter taxa_tab2 dataframe for only the Enterococcus
intes.taxa_e <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only the Escherichia-Shigella
intes.taxa_es <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Escherichia-Shigella")
##Filter taxa_tab2 dataframe for only the Kocuria
intes.taxa_k <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Kocuria")
##Filter taxa_tab2 dataframe for only the Lactobacillus
intes.taxa_l <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
intes.taxa_le <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Others
intes.taxa_o <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Others")
##Filter taxa_tab2 dataframe for only Photobacterium
intes.taxa_ph <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pseudomonas
intes.taxa_ps <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
intes.taxa_pd <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only the Ruminococcaceae
intes.taxa_r <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Sphingomonas
intes.taxa_sph <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Sphingomonas")
##Filter taxa_tab2 dataframe for only the Staphylococcus
intes.taxa_st <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Staphylococcus")
##Filter taxa_tab2 dataframe for only Yersinia
intes.taxa_y <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Yersinia")
```
```{r}
# intes at gut level
##Filter only the dataframe for only gut
intes.taxa_tab.intes2 <- filter(taxa_tab.intes1, sample == "gut")
##Filter taxa_tab.intes2 dataframe for only Acinetobacter
intes.taxa.intes_ac <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Acinetobacter")
##Filter taxa_tab.intes2 dataframe for only the Aeromonas
intes.taxa.intes_ar <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Aeromonas")
##Filter taxa_tab.intes2 dataframe for only the Corynebacterium
intes.taxa.intes_c <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Corynebacterium")
##Filter taxa_tab.intes2 dataframe for only the Cutibacterium
intes.taxa.intes_cu <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Cutibacterium")
##Filter taxa_tab.intes2 dataframe for only the Enterobacter
intes.taxa.intes_e <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Enterobacter")
##Filter taxa_tab.intes2 dataframe for only the Enterococcus
intes.taxa.intes_ec <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Enterococcus")
##Filter taxa_tab.intes2 dataframe for only the Escherichia-Shigella
intes.taxa.intes_es <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Escherichia-Shigella")
##Filter taxa_tab.intes2 dataframe for only the Kocuria
intes.taxa.intes_k <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Kocuria")
##Filter taxa_tab.intes2 dataframe for only the Lactobacillus
intes.taxa.intes_l <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Lactobacillus")
##Filter taxa_tab.intes2 dataframe for only OTHERS
intes.taxa.intes_o <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "OTHERS")
##Filter taxa_tab.intes2 dataframe for only Pseudomonas
intes.taxa.intes_ps <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Pseudomonas")
##Filter taxa_tab.intes2 dataframe for only the Ruminococcaceae
intes.taxa.intes_r <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Ruminococcaceae")
##Filter taxa_tab.intes2 dataframe for only Sphingomonas
intes.taxa.intes_s <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Sphingomonas")
##Filter taxa_tab.intes2 dataframe for only the Staphylococcus
intes.taxa.intes_st <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Staphylococcus")
##Filter taxa_tab.intes2 dataframe for only the Stenotrophomonas
intes.taxa.intes_sph <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Stenotrophomonas")
##Filter taxa_tab.intes2 dataframe for only Yersinia
intes.taxa.intes_y <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Yersinia")
```
```{r}
# water
##Filter only the dataframe for only water
water.taxa_tab2 <- filter(taxa_tab1, sample == "water")
##Filter taxa_tab2 dataframe for only Acinetobacter
water.taxa_ac <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Acinetobacter")
##Filter taxa_tab2 dataframe for only the Aeromonas
water.taxa_ar <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Arsenicibacter
water.taxa_as <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Arsenicibacter")
##Filter taxa_tab2 dataframe for only the Enterococcus
water.taxa_e <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only the Escherichia-Shigella
water.taxa_es <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Escherichia-Shigella")
##Filter taxa_tab2 dataframe for only the Kocuria
water.taxa_k <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Kocuria")
##Filter taxa_tab2 dataframe for only the Lactobacillus
water.taxa_l <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
water.taxa_le <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Others
water.taxa_o <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Others")
##Filter taxa_tab2 dataframe for only Photobacterium
water.taxa_ph <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pseudomonas
water.taxa_ps <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
water.taxa_pd <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only the Ruminococcaceae
water.taxa_r <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Sphingomonas
water.taxa_sph <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Sphingomonas")
##Filter taxa_tab2 dataframe for only the Staphylococcus
water.taxa_st <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Staphylococcus")
##Filter taxa_tab2 dataframe for only Yersinia
water.taxa_y <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Yersinia")
```
```{r}
# water at level of water
##Filter only the dataframe for only water
water.taxa_tab.water2 <- filter(taxa_tab.water1, sample == "water")
##Filter taxa_tab.water2 dataframe for only AAP99
water.taxa.water_aa <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "AAP99")
##Filter taxa_tab.water2 dataframe for only the Arsenicibacter
water.taxa.water_ar <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Arsenicibacter")
##Filter taxa_tab.water2 dataframe for only Fimbriimonadaceae
water.taxa.water_f <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Fimbriimonadaceae")
##Filter taxa_tab.water2 dataframe for only the Flavobacterium
water.taxa.water_fl <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Flavobacterium")
##Filter taxa_tab.water2 dataframe for only the Luteolibacter
water.taxa.water_lu <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Luteolibacter")
##Filter taxa_tab.water2 dataframe for only the Microtrichaceae
water.taxa.water_m <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Microtrichaceae")
##Filter taxa_tab.water2 dataframe for only the Novosphingobium
water.taxa.water_n <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Novosphingobium")
##Filter taxa_tab.water2 dataframe for only the NS11-12 marine group
water.taxa.water_ns <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "NS11-12 marine group")
##Filter taxa_tab.water2 dataframe for only the OTHERs
water.taxa.water_o <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "OTHERs")
##Filter taxa_tab.water2 dataframe for only the Pedobacter
water.taxa.water_pe <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Pedobacter")
##Filter taxa_tab.water2 dataframe for only the Polaromonas
water.taxa.water_po <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Polaromonas")
##Filter taxa_tab.water2 dataframe for only Pseudorhodobacter
water.taxa.water_ps <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Pseudorhodobacter")
##Filter taxa_tab.water2 dataframe for only the Rhodobacter
water.taxa.water_rd <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Rhodobacter")
##Filter taxa_tab.water2 dataframe for only the Rhodoferax
water.taxa.water_rf <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Rhodoferax")
##Filter taxa_tab.water2 dataframe for only Sphaerotilus
water.taxa.water_st <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Sphaerotilus")
##Filter taxa_tab.water2 dataframe for only Sphingomonadaceae
water.taxa.water_sd <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Sphingomonadaceae")
```
```{r}
# feed
##Filter only the dataframe for only feed
feed.taxa_tab2 <- filter(taxa_tab1, sample == "feed")
##Filter taxa_tab2 dataframe for only Acinetobacter
feed.taxa_ac <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Acinetobacter")
##Filter taxa_tab2 dataframe for only the Aeromonas
feed.taxa_ar <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Arsenicibacter
feed.taxa_as <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Arsenicibacter")
##Filter taxa_tab2 dataframe for only the Enterococcus
feed.taxa_e <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only the Escherichia-Shigella
feed.taxa_es <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Escherichia-Shigella")
##Filter taxa_tab2 dataframe for only the Kocuria
feed.taxa_k <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Kocuria")
##Filter taxa_tab2 dataframe for only the Lactobacillus
feed.taxa_l <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
feed.taxa_le <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Others
feed.taxa_o <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Others")
##Filter taxa_tab2 dataframe for only Photobacterium
feed.taxa_ph <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pseudomonas
feed.taxa_ps <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
feed.taxa_pd <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only the Ruminococcaceae
feed.taxa_r <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Sphingomonas
feed.taxa_sph <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Sphingomonas")
##Filter taxa_tab2 dataframe for only the Staphylococcus
feed.taxa_st <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Staphylococcus")
##Filter taxa_tab2 dataframe for only Yersinia
feed.taxa_y <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Yersinia")
```
```{r}
# feed at level of feed
##Filter only the dataframe for only feed
feed.taxa_tab.feed2 <- filter(taxa_tab.feed1, sample == "feed")
##Filter taxa_tab.feed2 dataframe for only Bifidobacteriaceae
feed.taxa.feed_b <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab.feed2 dataframe for only the Clostridiaceae
feed.taxa.feed_cc <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Clostridiaceae")
##Filter taxa_tab.feed2 dataframe for only the Clostridium sensu stricto 7
feed.taxa.feed_c <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Clostridium sensu stricto 7")
##Filter taxa_tab.feed2 dataframe for only Fusobacterium
feed.taxa.feed_f <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Fusobacterium")
##Filter taxa_tab.feed2 dataframe for only the Janthinobacterium
feed.taxa.feed_j <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Janthinobacterium")
##Filter taxa_tab.feed2 dataframe for only the Lactobacillus
feed.taxa.feed_l <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Lactobacillus")
##Filter taxa_tab.feed2 dataframe for only the Lactococcus
feed.taxa.feed_la <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Lactococcus")
##Filter taxa_tab.feed2 dataframe for only the Leuconostoc
feed.taxa.feed_le <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Leuconostoc")
##Filter taxa_tab.feed2 dataframe for only others
feed.taxa.feed_o <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "others")
##Filter taxa_tab.feed2 dataframe for only the Pediococcus
feed.taxa.feed_p <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Pediococcus")
##Filter taxa_tab.feed2 dataframe for only the Photobacterium
feed.taxa.feed_ph <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Photobacterium")
##Filter taxa_tab.feed2 dataframe for only the Psychrilyobacter
feed.taxa.feed_py <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Psychrilyobacter")
##Filter taxa_tab.feed2 dataframe for only the Shewanella
feed.taxa.feed_sh <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Shewanella")
##Filter taxa_tab.feed2 dataframe for only Streptococcus
feed.taxa.feed_st <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Streptococcus")
##Filter taxa_tab.feed2 dataframe for only the Tepidimicrobium
feed.taxa.feed_t <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Tepidimicrobium")
##Filter taxa_tab.feed2 dataframe for only Weissella
feed.taxa.feed_w <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Weissella")
```
##Test the Region_Regime effect on individual taxonomy - Kruska-walis test
```{r}
# intes