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14- Top15_lower ASV rank.Rmd
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14- Top15_lower ASV rank.Rmd
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##Load the required packages
```{r}
library(tidyverse)
library(cowplot)
library(patchwork)
library(ggh4x)
##library(ggtext)
library(PerformanceAnalytics)
library(RColorBrewer)
#library(MicrobeR)
library(microbiome)
##library(speedyseq)
library(flextable)
##library(webshot2)
library(officer)
library(DT)
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)
set.seed(1910)
library(ggpubr)
```
##Set the working driectory and read the saved phyloseq object
```{r}
ps.prev <- readRDS("phyobjects/STEP 14/ps.prev.f.rds")
ps.prev.intes <- readRDS("phyobjects/STEP 15/ps.prev.intes.f.rds")
ps.prev.feed <- readRDS("phyobjects/STEP 15'/ps.prev.feed.f.rds")
ps.prev.water <- readRDS("phyobjects/STEP 15'/ps.prev.water.f.rds")
# # filter samples
# 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)
txnm_assg %>%
datatable(options = list(columnDefs = list(list(className = 'dt-left', targets = c(0:3)))))
Rank OTUs_classified Frac_classified
1 Kingdom 4988 100 %
2 Phylum 4988 100 %
3 Class 4988 100 %
4 Order 4988 100 %
5 Family 4988 100 %
6 Genus 3914 78.5 %
7 Species 482 9.7 %
# 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)
txnm.intes_assg %>%
datatable(options = list(columnDefs = list(list(className = 'dt-left', targets = c(0:3)))))
Rank OTUs_classified Frac_classified
1 Kingdom 2150 100 %
2 Phylum 2150 100 %
3 Class 2150 100 %
4 Order 2150 100 %
5 Family 2150 100 %
6 Genus 1780 82.8 %
7 Species 273 12.7 %
# 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)
txnm.water_assg %>%
datatable(options = list(columnDefs = list(list(className = 'dt-left', targets = c(0:3)))))
Rank OTUs_classified Frac_classified
1 Kingdom 2474 100 %
2 Phylum 2474 100 %
3 Class 2474 100 %
4 Order 2474 100 %
5 Family 2474 100 %
6 Genus 1818 73.5 %
7 Species 156 6.3 %
# 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)
txnm.feed_assg %>%
datatable(options = list(columnDefs = list(list(className = 'dt-left', targets = c(0:3)))))
Rank OTUs_classified Frac_classified
1 Kingdom 1119 100 %
2 Phylum 1119 100 %
3 Class 1119 100 %
4 Order 1119 100 %
5 Family 1119 100 %
6 Genus 986 88.1 %
7 Species 203 18.1 %
```
## Biological samples
### Top15 genera
```{r}
taxa_tab <- Summarize.Taxa(otu, txnm)$Genus %>% Make.Percent()
taxa_tab <- taxa_tab[order(rowMeans(taxa_tab), decreasing = T), ]
Others <- colSums(taxa_tab[16:nrow(taxa_tab), ])
taxa_tab <- rbind(taxa_tab[1:15, ], Others)
# intes
taxa_tab.intes <- Summarize.Taxa(otu.intes, txnm.intes)$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)
# feed
taxa_tab.feed <- Summarize.Taxa(otu.feed, txnm.feed)$Genus %>% Make.Percent()
taxa_tab.feed <- taxa_tab.feed[order(rowMeans(taxa_tab.feed), decreasing = T), ]
others <- colSums(taxa_tab.feed[16:nrow(taxa_tab.feed), ])
taxa_tab.feed <- rbind(taxa_tab.feed[1:15, ], others)
# water
taxa_tab.water <- Summarize.Taxa(otu.water, txnm.water)$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 intestine.xlsx")
# 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 intestine for intestine.xlsx")
# 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")
# 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")
```
### Boxplot to abundance of individual dominant taxa in the intestine samples grouped by Regime
```{r, fig.width=10}
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd$Regime <- factor(mtd$Regime, levels = c("M", "V", "MM", "VM", "MMV", "VMV"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Bifidobacteriaceae", "Pseudomonas", "Photobacterium", "Pediococcus", "Lactobacillus", "Lactococcus","Leuconostoc", "Sphaerotilus", "Enterococcus", "Flavobacterium", "Yersinia", "Aeromonas", "Polaromonas","Pseudorhodobacter", "Others"))
# intestine samples
taxa_boxplot_intestine <- filter(taxa_tab1, sample == "intestine") %>%
ggplot(aes(x = factor(Regime, c("M", "V", "MM", "VM", "MMV", "VMV")), y = Abundance, color = Phase)) +
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_intes.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
```{r}
# intes
unique(taxa_tab.intes1$Genus)
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd.intes$Regime <- factor(mtd.intes$Regime, levels = c("M", "V", "MM", "VM", "MMV", "VMV"))
taxa_tab.intes1$Genus2 <- factor(taxa_tab.intes1$Genus, levels = c("Ruminococcaceae", "Bifidobacteriaceae", "Pseudomonas", "Photobacterium", "Pediococcus", "Lactobacillus", "Lactococcus","Leuconostoc", "Acinetobacter", "Enterococcus", "Escherichia-Shigella", "Staphylococcus", "Cutibacterium", "Yersinia","Aeromonas", "OTHERS"))
# intestine samples
taxa_boxplot_intestine.intes <- filter(taxa_tab.intes1, sample == "intestine") %>%
ggplot(aes(x = factor(Regime, c("M", "V", "MM", "VM", "MMV", "VMV")), y = Abundance, color = Phase)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
ggsave("figures/top15.intes_intes.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
```{r}
# intes colored manually!
unique(taxa_tab.intes1$Genus)
# define color scheme
col <- c("#F8766D", "#00BFC4", "#F8766D", "#00BFC4", "#F8766D", "#00BFC4")
names(col) <- c("M", "V", "MM", "VM", "MMV", "VMV")
mtd.intes$Regime <- factor(mtd.intes$Regime, levels = c("M", "V", "MM", "VM", "MMV", "VMV"))
taxa_tab.intes1$Genus2 <- factor(taxa_tab.intes1$Genus, levels = c("Ruminococcaceae", "Bifidobacteriaceae", "Pseudomonas", "Photobacterium", "Pediococcus", "Lactobacillus", "Lactococcus","Leuconostoc", "Acinetobacter", "Enterococcus", "Escherichia-Shigella", "Staphylococcus", "Cutibacterium", "Yersinia","Aeromonas", "OTHERS"))
# intestine samples
taxa_boxplot_intestine.intes <- filter(taxa_tab.intes1, sample == "intestine") %>%
ggplot(aes(x = factor(Regime, c("M", "V", "MM", "VM", "MMV", "VMV")), y = Abundance, color = Regime)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
scale_color_manual(values = col) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
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.intes_intes1.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
## Boxplot to abundance of individual dominant taxa in the feed samples grouped by Regime
```{r, fig.width=10}
# feed
unique(taxa_tab1$Genus)
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd$Regime <- factor(mtd$Regime, levels = c("M", "V"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Bifidobacteriaceae", "Pseudomonas", "Photobacterium", "Pediococcus", "Lactobacillus", "Lactococcus","Leuconostoc", "Sphaerotilus", "Enterococcus", "Flavobacterium", "Yersinia", "Aeromonas", "Polaromonas","Pseudorhodobacter", "Others"))
# intestine samples
taxa_boxplot_feed <- filter(taxa_tab1, sample == "feed") %>%
ggplot(aes(x = factor(Regime, c("M", "V")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
ggsave("figures/top15_feed.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
```{r}
# feed at level of feed
unique(taxa_tab.feed1$Genus)
# 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$Abundance)
# feed samples
taxa_boxplot_feed <- filter(taxa_tab.feed1, sample == "feed") %>%
ggplot(aes(x = factor(Regime, c("M", "V")), y = Abundance)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "none")
# Export the plot
ggsave("figures/top15.feed_feed.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
## Boxplot to abundance of individual dominant taxa in the water samples grouped by Regime
```{r, fig.width=10}
# water
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd$Regime <- factor(mtd$Regime, levels = c("M", "V", "MM", "VM", "MMV", "VMV"))
taxa_tab1$Genus2 <- factor(taxa_tab1$Genus, levels = c("Ruminococcaceae", "Bifidobacteriaceae", "Pseudomonas", "Photobacterium", "Pediococcus", "Lactobacillus", "Lactococcus","Leuconostoc", "Sphaerotilus", "Enterococcus", "Flavobacterium", "Yersinia", "Aeromonas", "Polaromonas","Pseudorhodobacter", "Others"))
# intestine samples
taxa_boxplot_intestine <- filter(taxa_tab1, sample == "water") %>%
ggplot(aes(x = factor(Regime, c("M", "V", "MM", "VM", "MMV", "VMV")), y = Abundance, color = Phase)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
ggsave("figures/top15_wtr.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
```{r}
# water at level of water
unique(taxa_tab.water1$Genus)
# define color scheme
# col <- c("grey", brewer.pal(n = 12, name = "Paired"))
mtd.water$Regime <- factor(mtd.water$Regime, levels = c("M", "V", "MM", "VM", "MMV", "VMV"))
taxa_tab.water1$Genus2 <- reorder(taxa_tab.water1$Genus, taxa_tab.water1$Abundance)
# watertine samples
taxa_boxplot_watertine.water <- filter(taxa_tab.water1, sample == "water") %>%
ggplot(aes(x = factor(Regime, c("M", "V", "MM", "VM", "MMV", "VMV")), y = Abundance, color = Phase)) +
geom_boxplot(alpha = 0.5, width = 0.5) +
labs(x = "Regime", y = "Relative abundance (%)") +
##scale_y_continuous(breaks = 0:10*10, expand = c(0,0)) +
#scale_fill_manual(values = col) +
facet_wrap(
~ Genus2,nrow = 4,
scale = "free"
) +
theme_bw() +
theme(##axis.text.x = element_blank(),
##strip.background = element_blank(),
legend.position = "bottom")
# Export the plot
ggsave("figures/top15.water_wtr.tiff", width = 10, height = 6,
units = "in", dpi = 300, compression = "lzw")
```
##Flitering the dataframe into each individual taxonomic
```{r}
# intestine
##Filter only the dataframe for only intestine
intes.taxa_tab2 <- filter(taxa_tab1, sample == "intestine")
##Filter taxa_tab2 dataframe for only Ruminococcaceae
intes.taxa_r <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Bifidobacteriaceae
intes.taxa_b <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab2 dataframe for only the Pseudomonas
intes.taxa_p <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Photobacterium
intes.taxa_ph <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pediococcus
intes.taxa_ped <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Pediococcus")
##Filter taxa_tab2 dataframe for only the Lactobacillus
intes.taxa_l <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Lactococcus
intes.taxa_la <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Lactococcus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
intes.taxa_le <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Sphaerotilus
intes.taxa_s <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Sphaerotilus")
##Filter taxa_tab2 dataframe for only Enterococcus
intes.taxa_e <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only Flavobacterium
intes.taxa_f <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Flavobacterium")
##Filter taxa_tab2 dataframe for only the Yersinia
intes.taxa_y <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Yersinia")
##Filter taxa_tab2 dataframe for only the Aeromonas
intes.taxa_a <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Polaromonas
intes.taxa_po <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Polaromonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
intes.taxa_ps <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only Others
intes.taxa_o <- intes.taxa_tab2 %>%
filter(intes.taxa_tab2$Genus == "Others")
```
```{r}
# intes at intestine level
##Filter only the dataframe for only intestine
intes.taxa_tab.intes2 <- filter(taxa_tab.intes1, sample == "intestine")
##Filter taxa_tab.intes2 dataframe for only Ruminococcaceae
intes.taxa.intes_r <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Ruminococcaceae")
##Filter taxa_tab.intes2 dataframe for only the Bifidobacteriaceae
intes.taxa.intes_b <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab.intes2 dataframe for only the Pseudomonas
intes.taxa.intes_p <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Pseudomonas")
##Filter taxa_tab.intes2 dataframe for only the Photobacterium
intes.taxa.intes_ph <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Photobacterium")
##Filter taxa_tab.intes2 dataframe for only the Pediococcus
intes.taxa.intes_ped <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Pediococcus")
##Filter taxa_tab.intes2 dataframe for only the Lactobacillus
intes.taxa.intes_l <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Lactobacillus")
##Filter taxa_tab.intes2 dataframe for only the Lactococcus
intes.taxa.intes_la <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Lactococcus")
##Filter taxa_tab.intes2 dataframe for only the Leuconostoc
intes.taxa.intes_le <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Leuconostoc")
##Filter taxa_tab.intes2 dataframe for only the Acinetobacter
intes.taxa.intes_ac <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Acinetobacter")
##Filter taxa_tab.intes2 dataframe for only Enterococcus
intes.taxa.intes_e <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Enterococcus")
##Filter taxa_tab.intes2 dataframe for only Escherichia-Shigella
intes.taxa.intes_es <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Escherichia-Shigella")
##Filter taxa_tab.intes2 dataframe for only the Staphylococcus
intes.taxa.intes_st <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Staphylococcus")
##Filter taxa_tab.intes2 dataframe for only Cutibacterium
intes.taxa.intes_cut <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Cutibacterium")
##Filter taxa_tab.intes2 dataframe for only the Yersinia
intes.taxa.intes_y <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Yersinia")
##Filter taxa_tab.intes2 dataframe for only the Aeromonas
intes.taxa.intes_a <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "Aeromonas")
##Filter taxa_tab.intes2 dataframe for only OTHERS
intes.taxa.intes_o <- intes.taxa_tab.intes2 %>%
filter(intes.taxa_tab.intes2$Genus == "OTHERS")
```
```{r}
# water
##Filter only the dataframe for only water
water.taxa_tab2 <- filter(taxa_tab1, sample == "water")
##Filter taxa_tab2 dataframe for only Ruminococcaceae
water.taxa_r <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Bifidobacteriaceae
water.taxa_b <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab2 dataframe for only the Pseudomonas
water.taxa_p <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Photobacterium
water.taxa_ph <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pediococcus
water.taxa_ped <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Pediococcus")
##Filter taxa_tab2 dataframe for only the Lactobacillus
water.taxa_l <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Lactococcus
water.taxa_la <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Lactococcus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
water.taxa_le <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Sphaerotilus
water.taxa_s <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Sphaerotilus")
##Filter taxa_tab2 dataframe for only Enterococcus
water.taxa_e <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only Flavobacterium
water.taxa_f <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Flavobacterium")
##Filter taxa_tab2 dataframe for only the Yersinia
water.taxa_y <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Yersinia")
##Filter taxa_tab2 dataframe for only the Aeromonas
water.taxa_a <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Polaromonas
water.taxa_po <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Polaromonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
water.taxa_ps <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only Others
water.taxa_o <- water.taxa_tab2 %>%
filter(water.taxa_tab2$Genus == "Others")
```
```{r}
# water at level of water
# unique(taxa_tab.water2)$Genus
# [1] OTHERs Microtrichaceae Luteolibacter Arsenicibacter
# [5] Sediminibacterium Saprospiraceae Flavobacterium Rubrivivax
# [9] Rhodoferax Leptothrix AAP99 Novosphingobium
# [13] Rhodobacter Sphaerotilus Polaromonas Pseudorhodobacter
# 16 Levels: OTHERs Microtrichaceae Luteolibacter Arsenicibacter ... Pseudorhodobacter
##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 Flavobacterium
water.taxa.water_f <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Flavobacterium")
##Filter taxa_tab.water2 dataframe for only the Leptothrix
water.taxa.water_le <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Leptothrix")
##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$Genus == "Novosphingobium")
##Filter taxa_tab.water2 dataframe for only the Polaromonas
water.taxa.water_po <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Polaromonas")
##Filter taxa_tab.water2 dataframe for only the Pseudorhodobacter
water.taxa.water_ps <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Pseudorhodobacter")
##Filter taxa_tab.water2 dataframe for only the Rhodobacter
water.taxa.water_rb <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Rhodobacter")
##Filter taxa_tab.water2 dataframe for only the Rhodoferax
water.taxa.water_rf <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Rhodoferax")
##Filter taxa_tab.water2 dataframe for only Rubrivivax
water.taxa.water_rx <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Rubrivivax")
##Filter taxa_tab.water2 dataframe for only the Saprospiraceae
water.taxa.water_sa <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Saprospiraceae")
##Filter taxa_tab.water2 dataframe for only the Sediminibacterium
water.taxa.water_sed <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Sediminibacterium")
##Filter taxa_tab.water2 dataframe for only Sphaerotilus
water.taxa.water_s <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "Sphaerotilus")
##Filter taxa_tab.water2 dataframe for only OTHERs
water.taxa.water_o <- water.taxa_tab.water2 %>%
filter(water.taxa_tab.water2$Genus == "OTHERs")
```
```{r}
# feed
##Filter only the dataframe for only feed
feed.taxa_tab2 <- filter(taxa_tab1, sample == "feed")
##Filter taxa_tab2 dataframe for only Ruminococcaceae
feed.taxa_r <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Ruminococcaceae")
##Filter taxa_tab2 dataframe for only the Bifidobacteriaceae
feed.taxa_b <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab2 dataframe for only the Pseudomonas
feed.taxa_p <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Pseudomonas")
##Filter taxa_tab2 dataframe for only the Photobacterium
feed.taxa_ph <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Photobacterium")
##Filter taxa_tab2 dataframe for only the Pediococcus
feed.taxa_ped <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Pediococcus")
##Filter taxa_tab2 dataframe for only the Lactobacillus
feed.taxa_l <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Lactobacillus")
##Filter taxa_tab2 dataframe for only the Lactococcus
feed.taxa_la <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Lactococcus")
##Filter taxa_tab2 dataframe for only the Leuconostoc
feed.taxa_le <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Leuconostoc")
##Filter taxa_tab2 dataframe for only Sphaerotilus
feed.taxa_s <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Sphaerotilus")
##Filter taxa_tab2 dataframe for only Enterococcus
feed.taxa_e <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Enterococcus")
##Filter taxa_tab2 dataframe for only Flavobacterium
feed.taxa_f <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Flavobacterium")
##Filter taxa_tab2 dataframe for only the Yersinia
feed.taxa_y <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Yersinia")
##Filter taxa_tab2 dataframe for only the Aeromonas
feed.taxa_a <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Aeromonas")
##Filter taxa_tab2 dataframe for only the Polaromonas
feed.taxa_po <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Polaromonas")
##Filter taxa_tab2 dataframe for only the Pseudorhodobacter
feed.taxa_ps <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Pseudorhodobacter")
##Filter taxa_tab2 dataframe for only Others
feed.taxa_o <- feed.taxa_tab2 %>%
filter(feed.taxa_tab2$Genus == "Others")
```
```{r}
# feed at level of feed
# unique(taxa_tab.feed1)$Genus
# [1] others Psychrilyobacter
# [3] Fusobacterium Bifidobacteriaceae
# [5] Janthinobacterium Shewanella
# [7] Photobacterium Tepidimicrobium
# [9] Clostridium sensu stricto 7 Clostridiaceae
# [11] Streptococcus Weissella
# [13] Pediococcus Leuconostoc
# [15] Lactococcus Lactobacillus
# 16 Levels: others Psychrilyobacter Fusobacterium ... Lactobacillus
##Filter only the dataframe for only feed
feed.taxa_tab.feed2 <- filter(taxa_tab.feed1, sample == "feed")
##Filter taxa_tab.feed2 dataframe for only Bifidobacteriaceae
feed.taxa.feed_b <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Bifidobacteriaceae")
##Filter taxa_tab.feed2 dataframe for only the Clostridium sensu stricto 7
feed.taxa.feed_c <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Clostridium sensu stricto 7")
##Filter taxa_tab.feed2 dataframe for only the Clostridiaceae
feed.taxa.feed_cc <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Clostridiaceae")
##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$Genus == "Lactobacillus")
##Filter taxa_tab.feed2 dataframe for only the Microtrichaceae
feed.taxa.feed_la <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Lactococcus")
##Filter taxa_tab.feed2 dataframe for only the Leuconostoc
feed.taxa.feed_le <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "Leuconostoc")
##Filter taxa_tab.feed2 dataframe for only the Pediococcus
feed.taxa.feed_p <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$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")
##Filter taxa_tab.feed2 dataframe for only others
feed.taxa.feed_o <- feed.taxa_tab.feed2 %>%
filter(feed.taxa_tab.feed2$Genus == "others")
```
##Test the Regime effect on individual taxonomy - Kruska-walis test
```{r}
# intes
##Statistics for 1. Ruminococcaceae
kruskal.test(Abundance ~ Regime, data = intes.taxa_r)
##Statistics for 2. Bifidobacteriaceae
kruskal.test(Abundance ~ Regime, data = intes.taxa_b)
##Statistics for 3. Pseudomonas
kruskal.test(Abundance ~ Regime, data = intes.taxa_p)
##Statistics for 4. Photobacterium
kruskal.test(Abundance ~ Regime, data = intes.taxa_ph)
##Statistics for 5. Pediococcus
kruskal.test(Abundance ~ Regime, data = intes.taxa_ped)
##Statistics for 6. Lactobacillus
kruskal.test(Abundance ~ Regime, data = intes.taxa_l)
##Statistics for 7. Lactococcus
kruskal.test(Abundance ~ Regime, data = intes.taxa_la)
##Statistics for 8. Leuconostoc
kruskal.test(Abundance ~ Regime, data = intes.taxa_le)
##Statistics for 9. Sphaerotilus
kruskal.test(Abundance ~ Regime, data = intes.taxa_s)
##Statistics for 10. Enterococcus
kruskal.test(Abundance ~ Regime, data = intes.taxa_e)
##Statistics for 11. Flavobacterium
kruskal.test(Abundance ~ Regime, data = intes.taxa_f)
##Statistics for 12. Yersinia
kruskal.test(Abundance ~ Regime, data = intes.taxa_y)
##Statistics for 13. Aeromonas
kruskal.test(Abundance ~ Regime, data = intes.taxa_a)
##Statistics for 14. Polaromonas
kruskal.test(Abundance ~ Regime, data = intes.taxa_po)
##Statistics for 15. Pseudorhodobacter
kruskal.test(Abundance ~ Regime, data = intes.taxa_ps)
##Statistics for 16. Others
kruskal.test(Abundance ~ Regime, data = intes.taxa_o)
# 1. Kruskal-Wallis rank sum test
#
# data: Abundance by Regime
# Kruskal-Wallis chi-squared = 42.159, df = 5, p-value = 5.469e-08
#
#
# 2. Kruskal-Wallis rank sum test
#
# data: Abundance by Regime