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McCulloch_SNF_Sequence2022.Rmd
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---
title: "Chapter 4 Analysis Clean"
output: html_notebook
---
##Loading packages
```{r}
#Load package####
library(tidyverse)
library(plyr)
library(dplyr)
library(ggplot2)
library(ggpubr)
#library(vegetarian)
library(data.table)
library(VennDiagram)
library(phyloseq)
library(vegan)
library(microbiome)
library(devtools)
library("gamlss")
library(PMA)
library(RColorBrewer)
library(viridis)
#install.packages("devtools") #if you don't have devtools
devtools::install_github("jbisanz/qiime2R")
library(qiime2R)
install_github('fawda123/ggord')
library(ggord)
install_github("vmikk/metagMisc")
library("metagMisc")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
#BiocManager::install("microbiome")
#BiocManager::install("DESeq2")
library(microbiome)
#library(DESeq2)
#Set seed for reproducible data
set.seed(13)
```
##Loading meta data and calculating SNF variables
```{r}
setwd('/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4')
meta = read.csv("LS_Rhiz_data_meta.csv")
#meta = meta[,1:32]
meta$type = "Gap"
meta$type[meta$Site > 5] = "Intact_Forest"
meta$type = as.factor(meta$type)
meta$Site = as.factor(meta$Site)
meta$nod.ex.mass[is.na(meta$nod.ex.mass)] = 0
meta$S.num = as.factor(meta$S.num)
meta$S.num <- revalue(meta$S.num, c("1" = "P.mac", "2" = "Z.lon","3" = "S.mic"))
meta$P.num = as.character(meta$P.num)
data.info = meta%>%
group_by(S.num, light, nitro) %>%
summarise(
n=length(nod.ex.mass)
)
#Changing total N in isotope samples from micrograms to miligrams
meta$Total.N.e <- meta$total.N.15n*.001
meta$Total.N.c <- meta$total.N.c*.001
#Calculating %N for each sample from the total N and sample weight of enriched samples ####
meta$XN.e<-((meta$Total.N.e)/meta$mg.tin.e)*100
#Calculating %N for each sample from the total N and sample weight of control samples ####
meta$XN.c<-((meta$total.N.c)/meta$mg.tin.c)*100
#Syringe Volume replaced (proportion), Enrichment of normal atmosphere, enrichment of added gas ####
svr<-.2
X15Nair<-.003663
X15Ngas<-.99
svr.c <- 0
#Calculating the expected enrichment of syringe headspace during incubation.
#this will be very close to the volume replaced times the %15Ngas (how close depends on how close the gas enrichment is to 100%)
syr.enrich<-(X15Ngas*svr)+(X15Nair*(1-svr))
syr.enrich.c <-(X15Ngas*svr.c)+(X15Nair*(1-svr.c))
#Now the Percent of N atoms fixed in each sample calculated is by
#taking the difference between the enriched X15N and control X15N and (?dividing by expected enrichment)
#meta$XNfixd<-with(meta, (X15n.atX.15n-X15n.atX.c)/syr.enrich)
meta$XNfixdX<-with(meta, (X15n.atX.15n-X15n.atX.c))
#Some samples only ended up having values for controls because enriched samples were below detection limit,
#to avoid negatives I made them zeros
meta$XNfixdX[meta$XNfixdX < 0] = 0
#Calculating the total amount of N (g) in the incubated sample amount. n15.mass is in grams
meta$tot.N<-with(meta, (n15.mass)*XN.e)
#calculating the amount of N fixed in each sample (gN fixed per sample per incubation period) check units?
meta$Nfixd.sample<-with(meta, tot.N*XNfixdX)
#Calculating N fixation to units of (gN fixed per gram of nodule per hour) "SNF efficiency"####
#gN fixed per sample divided by the mass of nodules that were incubated (n15.mass)
meta$Nfixd<-with(meta, (Nfixd.sample/n15.mass)*2)
meta$Nfixd[is.na(meta$Nfixd)] = 0
#Converting N fixation to units of (gN fixed per gram of nodule per year)
time.conv<-17520 #the number of "half hours" in a year
meta$Nfixd.yr<-with(meta, (Nfixd.sample/n15.mass)*time.conv)
#calculating total N fixed per plant ####
#Taking "SNF efficiency" and multiplying by the total nodule mass
meta$totnodmass = with(meta, (nod.ex.mass+n15.mass+c.mass))
meta$tot.Nfixd <- with(meta, (Nfixd*totnodmass))
meta$tot.Nfixd[is.na(meta$tot.Nfixd)] = 0
#calculating total N fixed per g of plant####
#Taking total N fixed per plant and dividing by the plants total mass
meta$totmass = with(meta, (l.mass+r.mass+s.mass+totnodmass))
meta$tot.Nfixd.gplant <- with(meta, ((Nfixd*totnodmass)/totmass))
meta$tot.Nfixd.gplant[is.na(meta$tot.Nfixd.gplant)] = 0
meta$nodal = with(meta, (totnodmass/totmass))
#Once all the variables are added to the meta dataframe
#we need to change the column name to be the rowname so that phyloseq can match it to the OTU_table
meta1 = meta %>%
column_to_rownames(var = "JV.num")
meta$JV.num= sapply(strsplit(meta$JV.num, split='.', fixed=TRUE), function(x) (x[1]))
meta2 = meta %>%
column_to_rownames(var = "JV.num")
META = sample_data(meta1)
META2 = sample_data(meta2)
```
##Making 16S phyloseq objects
```{r}
#Load in phyloseq objects before any clustering or filtering
ps16s = qza_to_phyloseq(features="/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/16s.qza", taxonomy="/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/16s_taxonomy.qza",metadata = "/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/16s_meta_data.txt", tree = "/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/16tree.qza")
ps16s = merge_phyloseq(ps16s,META)
#Filtering to just bacteria and no mitrochondria or chlorplast
#vignette("phyloseq-basics")
filKing = "Bacteria"
topFamilies <- tax_table(ps16s)[, "Order"]
ps16s_ = subset_taxa(ps16s, Kingdom == "Bacteria")
ps16s_ = subset_taxa(ps16s_, Order != "Mitochondria")
ps16s_ = subset_taxa(ps16s_, Family != "Mitochondria")
ps16s_ = subset_taxa(ps16s_, Order != "Chloroplast")
ps16s_f <- prune_samples(sample_sums(ps16s_)>=1000, ps16s_)
#rarifying the data to equal depths
ps16s_fr= rarefy_even_depth(ps16s_f, rngseed=13, sample.size = min(sample_sums(ps16s_f)))
#use this instead of reruning the code once I do it
saveRDS(ps16s_fr,"ps16s_fr.rds")
#Then always call it this way:
readRDS("ps16s_fr.rds")
```
##Making nifH phyloseq objects
```{r}
psnif<-qza_to_phyloseq(features="/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/niff_env.qza",taxonomy="/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/niff_env_taxonomy.qza",metadata = "/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/niff_meta_table.txt", tree = "/Users/lindsaymcculloch/Google Drive/Brown/Research/LaSelva_Rhizobia/Chapter4/qiime_data/ntree.qza")
psnif = merge_phyloseq(psnif,META2)
psnif_ = subset_taxa(psnif, Class == "Betaproteobacteria" | Class == "Alphaproteobacteria" | Order != "NA")
psnif_f <- prune_samples(sample_sums(psnif_)>=1000, psnif_)
#rarifying the data to equal depths
psnif_fr= rarefy_even_depth(psnif_f, rngseed=13, sample.size = min(sample_sums(psnif_f)))
#use this instead of reruning the code once I do it
saveRDS(psnif_fr,"psnif_fr.rds")
#Then always call it this way:
readRDS("psnif_fr.rds")
```
#Calculating frequency of occurance (FOO) and Relative Read Abundance (RRA) for 16S
```{r}
#Subsetting the rarified 16S phyloseq object into each legume species
ps16s.P = subset_samples(ps16s_fr, S.num == "P.mac")
ps16s.S = subset_samples(ps16s_fr, S.num == "S.mic")
ps16s.Z = subset_samples(ps16s_fr, S.num == "Z.lon")
presence16S <- apply(otu_table(ps16s.P), 1, function(x) d(x, q=0))
FOO16S_P = presence16S/30
FOO16S_P = as.data.frame(FOO16S_P)
presence16S <- apply(otu_table(ps16s.S), 1, function(x) d(x, q=0))
FOO16S_S = presence16S/27
FOO16S_S = as.data.frame(FOO16S_S)
FOO16S_S$ESV = row.names(FOO16S_S)
presence16Z <- apply(otu_table(ps16s.Z), 1, function(x) d(x, q=0))
FOO16S_Z = presence16Z/33
FOO16S_Z = as.data.frame(FOO16S_Z)
FOO16S_Z$ESV = row.names(FOO16S_Z)
#Here I'm trying to calculate to portion of nodules that an ESV occurred
RRA_16SP<- transform_sample_counts(ps16s.P, function(x) x/sum(x))
RRA_16SS <- transform_sample_counts(ps16s.S, function(x) x/sum(x))
RRA_16SZ<- transform_sample_counts(ps16s.Z, function(x) x/sum(x))
dd_rra_otuP <- as.data.frame(t(otu_table(RRA_16SP)))
mean(dd_rra_otuP$ESV_015967)*100
mean(dd_rra_otuP$ESV_016099)*100
dd_rra_otuS <- as.data.frame(t(otu_table(RRA_16SS)))
mean(dd_rra_otuS$ESV_015966)*100
mean(dd_rra_otuS$ESV_000035)*100
mean(dd_rra_otuS$ESV_000051)*100
dd_rra_otuZ <- as.data.frame(t(otu_table(RRA_16SZ)))
mean(dd_rra_otuZ$ESV_015966)*100
mean(dd_rra_otuZ$ESV_009175)*100
mean(dd_rra_otuZ$ESV_001048)*100
mean(dd_rra_otuZ$ESV_001873)*100
mean(dd_rra_otuZ$ESV_000035)*100
mean(dd_rra_otuZ$ESV_015981)*100
mean(dd_rra_otuZ$ESV_015970)*100
mean(dd_rra_otuZ$ESV_004879)*100
```
#Calculating frequency of occurance (FOO) and Relative Read Abundance (RRA) for nifH
```{r}
#Subsetting the rarified 16S phyloseq object into each legume species
psnif.P = subset_samples(psnif_fr, S.num == "P.mac")
psnif.S = subset_samples(psnif_fr, S.num == "S.mic")
psnif.Z = subset_samples(psnif_fr, S.num == "Z.lon")
presencenif <- apply(otu_table(psnif.P), 1, function(x) d(x, q=0))
FOOnif_P = presencenif/28
FOOnif_P = as.data.frame(FOOnif_P)
FOOnif_P
presencenif <- apply(otu_table(psnif.S), 1, function(x) d(x, q=0))
FOOnif_S = presencenif/25
FOOnif_S = as.data.frame(FOOnif_S)
FOOnif_S$ESV = row.names(FOOnif_S)
presencenif <- apply(otu_table(psnif.Z), 1, function(x) d(x, q=0))
FOOnif_Z = presencenif/30
FOOnif_Z = as.data.frame(FOOnif_Z)
FOOnif_Z$ESV = row.names(FOOnif_Z)
#Here I'm trying to calculate to portion of nodules that an ESV occurred
RRA_nifP<- transform_sample_counts(psnif.P, function(x) x/sum(x))
RRA_nifS<- transform_sample_counts(psnif.S, function(x) x/sum(x))
RRA_nifZ<- transform_sample_counts(psnif.Z, function(x) x/sum(x))
dd_rra_otuP <- as.data.frame(t(otu_table(RRA_nifP)))
mean(dd_rra_otuP$ESV_000001)*100
dd_rra_otuS <- as.data.frame(t(otu_table(RRA_nifS)))
mean(dd_rra_otuS$ESV_000014)*100
mean(dd_rra_otuS$ESV_000021)*100
dd_rra_otuZ <- as.data.frame(t(otu_table(RRA_nifZ)))
mean(dd_rra_otuZ$ESV_000011)*100
mean(dd_rra_otuZ$ESV_000008)*100
```
#Multivariate comparisons of microbial community composition for 16s
```{r}
otu16s <- abundances(ps16s_fr)
meta16s <- meta(ps16s_fr)
meta16s$light = as.factor(meta16s$light)
meta16s$S.num = as.factor(meta16s$S.num)
meta16s$nitro = as.factor(meta16s$nitro)
permanova16s <- adonis(t(otu16s) ~ S.num,
data = meta16s, method = "bray")
permanova16s
dist16s <- vegdist(t(otu16s), method = "bray")
test = adonis_pairwise(meta16s, dist16s, "S.num")
test$Adonis.tab
otu16sP <- abundances(ps16s_P)
meta16sP <- meta(ps16s_P)
permanova16sP <- adonis(t(otu16sP) ~ type*nitro,
data = meta16sP, method = "bray")
permanova16sP
otu16sZ <- abundances(ps16s_Z)
meta16sZ <- meta(ps16s_Z)
permanova16sZ <- adonis(t(otu16sZ) ~ type*nitro,
data = meta16sZ, method = "bray")
permanova16sZ
otu16sS <- abundances(ps16s_S)
meta16sS <- meta(ps16s_S)
permanova16sS <- adonis(t(otu16sS) ~ type*nitro,
data = meta16sS, method = "bray")
permanova16sS
```
```{r}
#NIFH
ps_tmpN <- fantaxtic::get_top_taxa(physeq_obj = psnif_fr, n = 30, relative = TRUE,
discard_other = FALSE, other_label = "Other")
ps_tmpN <- fantaxtic::name_taxa(ps_tmpN, label = "Unknown", species = T, other_label = "Other")
ps_tmpN <- microbiome::transform(ps_tmpN, "compositional")
#Create labels for missing taxonomic ranks
fantaxtic::fantaxtic_bar(ps_tmpN, color_by = "Genus", label_by = "Genus", other_label = "Other", facet_by = "S.num")
nb.cols <- 5
mycolors <- colorRampPalette(brewer.pal(8, "Dark2"))(nb.cols)
mycolors <- c("#1B9E77", "grey")
sample_data(ps_tmpN)$S.num <- factor(sample_data(ps_tmpN)$S.num, levels = c("P.mac", "S.mic", "Z.lon"))
supp.labs <- c("P. macroloba", "S. microstachyum", "Z. longifolia")
names(supp.labs) = c("P.mac", "S.mic", "Z.lon")
plot_cN = plot_bar(ps_tmpN, fill = "Genus")+ facet_wrap(~S.num, scales="free_x", nrow=1, labeller = labeller(S.num = supp.labs))+
ylab("Relative Abundance (%)")+
theme_pubr()+
theme(strip.text.x = element_text(face = "italic", size = 18))+
theme(text=element_text(size=18),axis.text.x=element_text(size=12, colour="black", angle = 90))+
scale_fill_manual(values = mycolors)+theme(legend.position = "bottom")
plot_cN
```
#Indicator species analysis
```{r}
library(indicspecies)
md16s = meta(ps16s_fr)
md16s$rank = "low"
md16s$rank[md16s$Nfixd > 0.0275] = "high"
m16s = md16s[,c(1,33,75)]
colnames(m16s) = c("plant","sp", "rank")
rank = m16s$rank
a_ <- cbind(rownames(a), data.frame(a, row.names=NULL))
colnames(a_)[1] = "plant"
pc16s = inner_join(m16s,a_, by ="plant")
inv_16s = multipatt(a, rank, func = "r.g", control = how(nperm=999))
summary(inv_16s)
sub_pc16 = data.frame(Rank = pc16s$rank,ESV_015967 = pc16s$ESV_015967, ESV_016004 = pc16s$ESV_016004, ESV_015974 = pc16s$ESV_015974, ESV_000035 = pc16s$ESV_000035 , ESV_015981 = pc16s$ESV_015981 , ESV_015970 = pc16s$ESV_015970,
ESV_000122 = pc16s$ESV_000122, ESV_001048 = pc16s$ESV_001048, ESV_016055 = pc16s$ESV_016055,
ESV_005019 = pc16s$ESV_005019)
sub_pc16_m = melt(sub_pc16, id = "Rank")
is_16s = ggplot(sub_pc16_m, aes(x = variable, y = value, fill = Rank)) +
geom_boxplot(colour = "black") +
geom_vline(xintercept = c(1.5, 2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5), colour = "grey85", size = 1.2) +
theme(legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10, face = "bold"), legend.position = "right",
axis.text.x = element_text(face = "bold",colour = "black", size = 12, angle = 45, hjust = 1),
axis.text.y = element_text(face = "bold", size = 12, colour = "black"),
axis.title.y = element_text(face = "bold", size = 14, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
legend.key=element_blank()) +
labs(x= "", y = "Relative Abundance (%)", fill = "Rank") +
scale_fill_manual(values = c("steelblue2", "steelblue4"))
mdN = meta(psnif_fr)
mdN$rank = "low"
mdN$rank[mdN$Nfixd > 0.10863] = "high"
mN = mdN[,c(1,34,75)]
colnames(mN) = c("plant","sp", "rank")
rankN = mN$rank
b_ <- cbind(rownames(b), data.frame(b, row.names=NULL))
colnames(b_)[1] = "plant"
pcnif = inner_join(mN,b_, by ="plant")
inv_nifH = multipatt(b, rankN, func = "r.g", control = how(nperm=999))
summary(inv_nifH)
sub_pcnif = data.frame(Rank = pcnif$rank,ESV_000011 = pcnif$ESV_000011, ESV_000008 = pcnif$ESV_000008)
sub_pcnif_m = melt(sub_pcnif, id = "Rank")
is_nif = ggplot(sub_pcnif_m, aes(x = variable, y = value, fill = Rank)) +
geom_boxplot(colour = "black") +
geom_vline(xintercept = (1.5), colour = "grey85", size = 1.2) +
theme(legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10, face = "bold"), legend.position = "right",
axis.text.x = element_text(face = "bold",colour = "black", size = 12),
axis.text.y = element_text(face = "bold", size = 12, colour = "black"),
axis.title.y = element_text(face = "bold", size = 14, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
legend.key=element_blank()) +
labs(x= "", y = "Relative Abundance (%)", fill = "Rank") +
scale_fill_manual(values = c("steelblue2", "steelblue4"))
```
##Indicator species analysis BY SPECIES 16S
```{r}
library(indicspecies)
ps16s.P = subset_samples(ps16s_fr, S.num == "P.mac")
ps16s.S = subset_samples(ps16s_fr, S.num == "S.mic")
ps16s.Z = subset_samples(ps16s_fr, S.num == "Z.lon")
md16s_P = meta(ps16s.P)
md16s_P$rank = "low"
md16s_P$rank[md16s_P$Nfixd > 0.1314221] = "high"
m16s_P = md16s_P[,c(1,33,75)]
colnames(m16s_P) = c("plant","sp", "rank")
rank = m16s_P$rank
inv_16sP = multipatt(aP, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sP)
md16s_S = meta(ps16s.S)
md16s_S$rank = "low"
md16s_S$rank[md16s_S$Nfixd > 0.05721239] = "high"
m16s_S = md16s_S[,c(1,33,75)]
colnames(m16s_S) = c("plant","sp", "rank")
rank = m16s_S$rank
inv_16sS = multipatt(aS, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sS)
md16s_Z = meta(ps16s.Z)
md16s_Z$rank = "low"
md16s_Z$rank[md16s_Z$Nfixd > 0.1131634] = "high"
m16s_Z = md16s_Z[,c(1,33,75)]
colnames(m16s_Z) = c("plant","sp", "rank")
rank = m16s_Z$rank
inv_16sZ = multipatt(aZ, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sZ)
```
##Indicator species analysis BY SPECIES nifH
```{r}
library(indicspecies)
psnif.P = subset_samples(psnif_fr, S.num == "P.mac")
psnif.S = subset_samples(psnif_fr, S.num == "S.mic")
psnif.Z = subset_samples(psnif_fr, S.num == "Z.lon")
mdnif_P = meta(psnif.P)
mdnif_P$rank = "low"
mdnif_P$rank[mdnif_P$Nfixd > 0.1314221] = "high"
mnif_P = mdnif_P[,c(1,33,75)]
colnames(mnif_P) = c("plant","sp", "rank")
rank = mnif_P$rank
inv_nifP = multipatt(bP, rank, func = "r.g", control = how(nperm=999))
summary(inv_nifP)
mdnif_S = meta(psnif.S)
mdnif_S$rank = "low"
mdnif_S$rank[mdnif_S$Nfixd > 0.05721239] = "high"
mnif_S = mdnif_S[,c(1,33,75)]
colnames(mnif_S) = c("plant","sp", "rank")
rank = mnif_S$rank
inv_nifS = multipatt(bS, rank, func = "r.g", control = how(nperm=999))
summary(inv_nifS)
mdnif_Z = meta(psnif.Z)
mdnif_Z$rank = "low"
mdnif_Z$rank[mdnif_Z$Nfixd > 0.1131634] = "high"
mnif_Z = mdnif_Z[,c(1,33,75)]
colnames(mnif_Z) = c("plant","sp", "rank")
rank = mnif_Z$rank
inv_nifZ = multipatt(bZ, rank, func = "r.g", control = how(nperm=999))
summary(inv_nifZ)
```
##Alpha Diversity for 16S
```{r}
rich.16s = ps16s_fr %>%
plot_richness(
x = "S.num",
measures = c("Observed", "Shannon", "InvSimpson"))+
geom_violin(aes(fill=S.num), show.legend = FALSE)+
geom_boxplot(width = 0.1) +
theme_classic () +
xlab(NULL) +
theme(axis.text.y.left = element_text(size = 20),
axis.text.x = element_text(size = 20, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 20))+
theme(strip.text = element_text(face = "bold", size = 25))+
ggtitle("Alpha Diversity 16S") +
theme(plot.title=element_text(size = 25, face = "bold", hjust = 0.5))
rich.16s.type = ps16s_f %>%
plot_richness(
x = "type",
measures = c("Shannon"))+
geom_violin(aes(fill=type), show.legend = FALSE)+
geom_boxplot(width = 0.1) +
theme_classic () +
xlab(NULL) +
theme(axis.text.y.left = element_text(size = 20),
axis.text.x = element_text(size = 20, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 20))+
theme(strip.text = element_text(face = "bold", size = 25))+
ggtitle("Alpha Diversity 16S") +
theme(plot.title=element_text(size = 25, face = "bold", hjust = 0.5))
rich.16s
rich.16s.type
#Making the data behind this graphic into a data frame to run analysis
obs = rich.16s$data
#Filtering so the dataframe just has richness data
stat.ob = obs %>%
filter(variable == "Observed")
#Checking normality
shapiro.test(stat.ob$value) #Not normal
#Significant differences found in richness
wilcox.test(stat.ob$value ~ stat.ob$type) #P= 0.0001355
kruskal.test(stat.ob$value ~ stat.ob$S.num) #P = 0.01154
pairwise.wilcox.test(stat.ob$value, stat.ob$S.num, p.adjust.method="BH")
#P.mac&S.mic(0.00064),Z.lon&S.mic(0.1385),P.mac&Z.lon(0.2389)
#write.csv(stat.ob, file = "export.richness.16s.csv")
#Filtering so the dataframe just has Shannon Index data
stat.sh = obs %>%
filter(variable == "Shannon")
#Checking normality
shapiro.test(stat.sh$value) #Not normal
#Significant differences found in shannon index
wilcox.test(stat.sh$value ~ stat.sh$type) #p-value = 0.001987
wilcox.test(stat.sh$value ~ stat.sh$nitro) #p-value = 0.5301
kruskal.test(stat.sh$value ~ stat.sh$light) #p-value = 0.008228 signficant difference between 0 and 1 and 0 and 2
pairwise.wilcox.test(stat.sh$value, stat.sh$light, p.adjust.method="BH")
kruskal.test(stat.sh$value ~ stat.sh$S.num) #p-value = 0.005357
pairwise.wilcox.test(stat.sh$value, stat.sh$S.num, p.adjust.method="BH")
#P.mac&S.mic(0.0028),Z.lon&S.mic(0.1836),P.mac&Z.lon(0.0929)
write.csv(stat.sh, file = "export.shannon.16s.csv")
#Filtering so the data frame just has Inverse Simpson index
stat.isim = obs %>%
filter(variable == "InvSimpson")
#Checking normality
shapiro.test(stat.isim$value) #Not normal
#Significant differences found in inv. simpson
wilcox.test(stat.isim$value ~ stat.isim$type) #p-value = 0.004379
kruskal.test(stat.isim$value ~ stat.isim$S.num) #p-value = 0.006092
pairwise.wilcox.test(stat.isim$value, stat.isim$S.num, p.adjust.method="BH")
#P.mac&S.mic (0.0036),Z.lon&S.mic(0.2259),P.mac&Z.lon (0.0717)
#write.csv(stat.isim, file = "export.simpson.16s.csv")
```
##Alpha Diversity for nifH
```{r}
rich.nif = psnif_f %>%
plot_richness(
x = "S.num",
measures = c("Observed", "Shannon", "InvSimpson"))+
geom_violin(aes(fill=S.num), show.legend = FALSE)+
geom_boxplot(width = 0.1) +
theme_classic () +
xlab(NULL) +
theme(axis.text.y.left = element_text(size = 20),
axis.text.x = element_text(size = 20, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 20))+
theme(strip.text = element_text(face = "bold", size = 25))+
ggtitle("Alpha Diversity nifH") +
theme(plot.title=element_text(size = 25, face = "bold", hjust = 0.5))
rich.nif.type = psnif_f %>%
plot_richness(
x = "type",
measures = c("Observed", "Shannon", "InvSimpson"))+
geom_violin(aes(fill=type), show.legend = FALSE)+
geom_boxplot(width = 0.1) +
theme_classic () +
xlab(NULL) +
theme(axis.text.y.left = element_text(size = 20),
axis.text.x = element_text(size = 20, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 20))+
theme(strip.text = element_text(face = "bold", size = 25))+
ggtitle("Alpha Diversity nifH") +
theme(plot.title=element_text(size = 25, face = "bold", hjust = 0.5))
rich.nif
rich.nif.type
#Making the data behind this graphic into a data frame to run analysis
obs = rich.nif$data
#Filtering so the dataframe just has richness data
stat.ob = obs %>%
filter(variable == "Observed")
#Checking normality
shapiro.test(stat.ob$value) #Not normal
#No significant difference found in richness
kruskal.test(stat.ob$value ~ stat.ob$type) #P= 0.2175
kruskal.test(stat.ob$value ~ stat.ob$S.num) #P = 0.1407
#write.csv(stat.ob, file = "export.richness.nifH.csv")
#Filtering so the dataframe just has Shannon Index data
stat.sh = obs %>%
filter(variable == "Shannon")
#Checking normality
shapiro.test(stat.sh$value) #Not normal
#No significant difference found in shannon index
kruskal.test(stat.sh$value ~ stat.sh$type) #p-value = 0.2063
kruskal.test(stat.sh$value ~ stat.sh$nitro) #p-value = 0.3615
kruskal.test(stat.sh$value ~ stat.sh$light) #p-value =0.2476
kruskal.test(stat.sh$value ~ stat.sh$S.num) #p-value =0.04075
pairwise.wilcox.test(stat.sh$value, stat.sh$S.num, p.adjust.method="BH")
#P.mac&S.mic(0.101),Z.lon&S.mic(0.028),P.mac&Z.lon(0.450)
#write.csv(stat.sh, file = "export.shannon.nifH.csv")
#Filtering so the data frame just has Inverse Simpson index
stat.isim = obs %>%
filter(variable == "InvSimpson")
#Checking normality
shapiro.test(stat.isim$value) #Not normal
#No significant difference found in inv. simpson
kruskal.test(stat.isim$value ~ stat.isim$type) #p-value = 0.1394
kruskal.test(stat.isim$value ~ stat.isim$S.num) #p-value = 0.01405
pairwise.wilcox.test(stat.isim$value, stat.isim$S.num, p.adjust.method="BH")
#P.mac&S.mic (0.058),Z.lon&S.mic(0.013),P.mac&Z.lon (0.489)
#write.csv(stat.isim, file = "export.simpson.nifH.csv")
```
#Figure 1
```{r}
if(!"devtools" %in% installed.packages()){
install.packages("devtools", dependencies = T, )
}
devtools::install_github("gmteunisse/Fantaxtic")
library(fantaxtic)
#16S
ps_tmp <- fantaxtic::get_top_taxa(physeq_obj = ps16s_fr, n = 10, relative = TRUE,
discard_other = FALSE, other_label = "Other")
ps_tmp <- fantaxtic::name_taxa(ps_tmp, label = "Unknown", species = T, other_label = "Other")
ps_tmp <- microbiome::transform(ps_tmp, "compositional")
mycolors <- c("#ffffcc","#57be7a","#deebf7","#225ea8", "#41b6c4")
sample_data(ps_tmp)$S.num <- factor(sample_data(ps_tmp)$S.num, levels = c("P.mac", "Z.lon", "S.mic"))
supp.labs <- c("P. macroloba", "Z. longifolia", "S. microstachyum")
names(supp.labs) = c("P.mac", "Z.lon", "S.mic")
plot_a = plot_bar(ps_tmp, fill = "Order")+ facet_wrap(~S.num, scales="free_x", nrow=1, labeller = labeller(S.num = supp.labs))+
ylab("Relative Abundance (%)")+
theme_pubr()+
theme(strip.text.x = element_text(face = "italic", size = 15))+
theme(text=element_text(size=15),axis.text.x=element_text(size=12, colour="black", angle = 90))+
scale_fill_manual(values = mycolors)+theme(legend.position = "bottom")
#NIFH panel
ps_tmpN <- fantaxtic::get_top_taxa(physeq_obj = psnif_fr, n = 30, relative = TRUE,
discard_other = FALSE, other_label = "Other")
ps_tmpN <- fantaxtic::name_taxa(ps_tmpN, label = "Unknown", species = T, other_label = "Other")
ps_tmpN <- microbiome::transform(ps_tmpN, "compositional")
#Create labels for missing taxonomic ranks
mycolors <- c("#41b6c4", "grey")
sample_data(ps_tmp)$S.num <- factor(sample_data(ps_tmp)$S.num, levels = c("P.mac", "Z.lon", "S.mic"))
supp.labs <- c("P. macroloba", "Z. longifolia", "S. microstachyum")
names(supp.labs) = c("P.mac", "Z.lon", "S.mic")
plot_c = plot_bar(ps_tmpN, fill = "Genus")+ facet_wrap(~S.num, scales="free_x", nrow=1, labeller = labeller(S.num = supp.labs))+
ylab("Relative Abundance (%)")+
theme_pubr()+
theme(strip.text.x = element_text(face = "italic", size = 15))+
theme(text=element_text(size=15),axis.text.x=element_text(size=12, colour="black", angle = 90))+
scale_fill_manual(values = mycolors)+theme(legend.position = "bottom")
plot_c
#removing all the rhizobia for 16S
ps16s.P = subset_samples(ps16s_fr, S.num == "P.mac")
ps_tmp2 <- fantaxtic::get_top_taxa(physeq_obj = ps16s, n = 30, relative = TRUE,
discard_other = FALSE, other_label = "Other")
ps_tmp22<- transform_sample_counts(ps_tmp2, function(x) x/sum(x))
ps_tmp222 = subset_taxa(ps_tmp22, Order != "Rhizobiales")
#Calculating the non-rhizobia relative abundance for P.mac
df_pstmp22 <- as.data.frame(t(otu_table(ps_tmp222)))
df_pstmp22$total = rowSums(df_pstmp22[,1:24])
mean(df_pstmp22$total)
sd(df_pstmp22$total)
max(df_pstmp22$total)
#ps16s_frN = subset_taxa(ps_tmp2, Order != "Rhizobiales")
rich.16s1 = ps_tmp2 %>%
plot_richness(
x = "S.num",
measures = "Shannon")+
geom_boxplot(fill = "grey75")+
theme_classic () +
xlab(NULL) +
ylab("Shannon Diversity Index")+
theme(axis.text.y.left = element_text(size = 15),
axis.text.x = element_text(size = 15, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 15))+
theme(strip.text = element_blank())+
scale_x_discrete(labels=c("P.mac" = "P. macroloba",
"Z.lon" = "Z. longifolia", "S.mic" = "S. microstachyum"))+
theme(plot.title = element_text(hjust = 0.5, vjust = 2, size = 25, face = "bold"))
rich.16s1
#Making the data behind this graphic into a data frame to run analysis
shan16S_R =rich.16s1$data
#Filtering so the dataframe just has Shannon Index data
stat.sh16S_R = shan16S_R[c("S.num", "samples", "value")]
kruskal.test(stat.sh16S_R$value~stat.sh16S_R$S.num)
library(rstatix)
wilcox_test(value ~ S.num, data=stat.sh16S_R, p.adjust.method="bonferroni")
hist(stat.sh16S_R$value)
#removing all the rhizobia for nifH
nif_tmp2 <- fantaxtic::get_top_taxa(physeq_obj = psnif_fr, n = 30, relative = TRUE,
discard_other = FALSE, other_label = "Other")
rich.nif = nif_tmp2 %>%
plot_richness(
x = "S.num",
measures = "Shannon")+
geom_boxplot(fill = "grey75") +
theme_classic () +
xlab(NULL) +
ylab("Shannon Diversity Index")+
theme(axis.text.y.left = element_text(size = 15),
axis.text.x = element_text(size = 15, hjust = 0.5, vjust = .5, angle = 45),
axis.title.y = element_text(size = 15))+
theme(strip.text = element_blank())+
scale_x_discrete(labels=c("P.mac" = "P. macroloba","S.mic" = "S. microstachyum",
"Z.lon" = "Z. longifolia"))+
theme(plot.title = element_text(hjust = 0.5, vjust = 2, size = 25, face = "bold"))
rich.nif
#Making the data behind this graphic into a data frame to run analysis
shannif_R =rich.nif$data
#Filtering so the dataframe just has Shannon Index data
shannif_R= shannif_R[c("S.num", "samples", "value")]
kruskal.test(shannif_R$value~shannif_R$S.num)
fig1 = ggarrange(plot_a, rich.16s1,plot_c,rich.nif,
labels = c("A", "B", "C", "D"),
ncol = 2, nrow =2,
widths = c(1.5,.5))
fig1
fig1 = ggsave("figure1_52522.jpeg", width = 15, height = 10, units = "in")
```
#Figure 2
```{r}
#devtools::install_github("david-barnett/[email protected]")
library("microViz")
metadata16s = meta(ps16s_fr)
GP.ord <- ordinate(ps16s_fr, "NMDS", "bray")
p1 = plot_ordination(ps16s_fr, GP.ord, type="sample", color="S.num", title = "")+
geom_point(size=3)+
scale_color_manual(labels = c("P. macroloba", "Z. longifolia", "S. microstachyum"),
values = c("#016c59", "#67a9cf","#bdc9e1"))+
theme(legend.title = element_text(size = 15, face = "bold"),
legend.text = element_text(size = 15, face = "italic"), legend.position = "none",
axis.text.x = element_text(face = "bold",colour = "black", size = 12),
axis.text.y = element_text(face = "bold", size = 15, colour = "black"),
axis.title = element_text(face = "bold", size = 15, colour = "black"),
axis.title.y = element_text(face = "bold", size = 15, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
legend.key=element_blank())+
guides(color=guide_legend(title="Host species"))
p1
GP.ordNF <- ordinate(psnif_fr, "NMDS", "bray")
scores(GP.ordNF)
p2 = plot_ordination(psnif_fr, GP.ordNF, type="sample", color="S.num", title = "nifH")+
geom_point(size=3)+
theme(legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10, face = "italics"), legend.position = "none",
axis.text.x = element_text(face = "bold",colour = "black", size = 12),
axis.text.y = element_text(face = "bold", size = 12, colour = "black"),
axis.title = element_text(face = "bold", size = 15, colour = "black"),
axis.title.y = element_text(face = "bold", size = 15, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"))
#without outlier S025721
psnif_fr2 = subset_samples(psnif_fr, P.num != "527")
GP.ordNF2 <- ordinate(psnif_fr2, "NMDS", "bray")
scores(GP.ordNF)
p3 = plot_ordination(psnif_fr2, GP.ordNF2, type="sample", color="S.num", title = "")+
geom_point(size=3)+
scale_color_manual(labels = c("P. macroloba", "Z. longifolia", "S. microstachyum"),
values = c("#016c59", "#67a9cf","#bdc9e1"))+
theme(legend.title = element_text(size = 15, face = "bold"),
legend.text = element_text(size = 15, face = "italic"), legend.position = "right",
axis.text.x = element_text(face = "bold",colour = "black", size = 12),
axis.text.y = element_text(face = "bold", size = 15, colour = "black"),
axis.title = element_text(face = "bold", size = 15, colour = "black"),
axis.title.y = element_text(face = "bold", size = 15, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
legend.key=element_blank())+
guides(color=guide_legend(title="Host species"))
p1
p2
p3
figORD = ggarrange(p1, p3,
labels = c("A", "B"),
ncol = 2, nrow =1,
widths = c(.75,1))
figORD
figORD = ggsave("figureORD_60822.jpeg", width = 14, height = 8, units = "in")
#perMANOVA analysis 16S:
otu16s <- abundances(ps16s_fr)
meta16s <- meta(ps16s_fr)
meta16s$light = as.factor(meta16s$light)
meta16s$S.num = as.factor(meta16s$S.num)
meta16s$nitro = as.factor(meta16s$nitro)
permanova16s <- adonis(t(otu16s) ~ S.num*type*nitro,
data = meta16s, method = "bray")
permanova16s
dist16s <- vegdist(t(otu16s), method = "bray")
test = adonis_pairwise(meta16s, dist16s, "S.num")
test
print(permanova16s)
#running nifH without S025721 outlier
otunif <- abundances(psnif_fr)
otunif2 = subset(otunif, select = -c(S025721))
metanif <- meta(psnif_fr)
metanif$light = as.factor(metanif$light)
metanif$S.num = as.factor(metanif$S.num)
metanif$nitro = as.factor(metanif$nitro)
metanif2 = metanif[metanif$BLANK.3.1 != "S025721",]
permanovanif <- adonis2(t(otunif) ~ S.num,
data = metanif, method = "bray")
permanovanif
permanovanif2 <- adonis2(t(otunif2) ~ S.num,
data = metanif2, method = "bray")
permanovanif2
distnif <- vegdist(t(otunif), method = "bray")
pair.nif = adonis_pairwise(metanif, distnif, "S.num")
pair.nif
```
#Figure 3
```{r}
library(indicspecies)
ps16s.P = subset_samples(ps16s_fr, S.num == "P.mac")
ps16s.S = subset_samples(ps16s_fr, S.num == "S.mic")
ps16s.Z = subset_samples(ps16s_fr, S.num == "Z.lon")
md16s_P = meta(ps16s.P)
md16s_P$rank = "low"
md16s_P$rank[md16s_P$Nfixd > 0.1314221] = "high"
m16s_P = md16s_P[,c(1,33:75)]
colnames(m16s_P) = c("plant","sp", "rank")
rank = m16s_P$rank
inv_16sP = multipatt(aP, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sP)
md16s_S = meta(ps16s.S)
md16s_S$rank = "low"
md16s_S$rank[md16s_S$Nfixd > 0.05721239] = "high"
m16s_S = md16s_S[,c(1,33,75)]
colnames(m16s_S) = c("plant","sp", "rank")
rank = m16s_S$rank
inv_16sS = multipatt(aS, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sS)
md16s_Z = meta(ps16s.Z)
md16s_Z$rank = "low"
md16s_Z$rank[md16s_Z$Nfixd > 0.1131634] = "high"
m16s_Z = md16s_Z[,c(1,33,75)]
colnames(m16s_Z) = c("plant","sp", "rank")
rank = m16s_Z$rank
inv_16sZ = multipatt(aZ, rank, func = "r.g", control = how(nperm=999))
summary(inv_16sZ)
#panel a for figure for paper
#calculating the relative abundance of P.mac
RRA_16SP<- transform_sample_counts(ps16s.P, function(x) x/sum(x))
#Making this a dataframe to merge
dd_rra_otuP <- as.data.frame(t(otu_table(RRA_16SP)))
tail(sort(colMeans(dd_rra_otuP)),10)
#Creating an other category and leaving the one high (015967) and one low (016099) and top ten rel. abun ESV for Pmac
dd_rra_otuP$other = rowSums(dd_rra_otuP[,1:1238])-dd_rra_otuP$ESV_015967-dd_rra_otuP$ESV_016099-
dd_rra_otuP$ESV_015974-dd_rra_otuP$ESV_008206-dd_rra_otuP$ESV_001019-dd_rra_otuP$ESV_000029-dd_rra_otuP$ESV_000261-
dd_rra_otuP$ESV_015966-dd_rra_otuP$ESV_015968-dd_rra_otuP$ESV_000464-dd_rra_otuP$ESV_000258
dd_rra_otuP = tibble::rownames_to_column(dd_rra_otuP, "ID")
#new dataframe with just the categories of interest for P.mac (other, low and high, and top ten)
RRA_16SP_3 = dd_rra_otuP[c('ID','ESV_016099', 'other', 'ESV_015967', 'ESV_015974', 'ESV_008206', 'ESV_001019', 'ESV_000029', 'ESV_000261', 'ESV_015966', 'ESV_015968', 'ESV_000464', 'ESV_000258')]
library(reshape2)
rra_16SP_3_m = melt(RRA_16SP_3, id.vars="ID", measure.vars = c('ESV_016099', 'ESV_015974', 'ESV_008206', 'ESV_001019', 'ESV_000029', 'ESV_000261', 'ESV_015966', 'ESV_015968', 'ESV_000464', 'ESV_000258', 'other', 'ESV_015967'))
#Merging this dataframe with rank status and N fixation
names(m16s_P)[1] = "ID"
rra16S_Pnf = merge(m16s_P, rra_16SP_3_m, by = "ID")
rra16S_Pnf$Nfixd = round(rra16S_Pnf$Nfixd, 3)
finalP = rra16S_Pnf[c('ID','variable','value', 'rank', 'Nfixd')]
fig3_panela = rra16S_Pnf %>%
mutate(ID_ = fct_reorder(ID, Nfixd)) %>%
ggplot(aes(x=ID_, y = value, fill = variable)) +
geom_bar(stat="identity", color = "black") +
#geom_text(aes(label = Nfixd), position = top)+
scale_fill_manual(labels = c("Thermosporothrix", "Norcardia sp. 1", "Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium",
"Burkholderia-Caballeronia-Paraburkholderia sp. 1",
"Diaphorobacter sp. 1", "Afipia sp. 1", "Bradyrhizobium sp. 1",
"Burkholderia-Caballeronia-Paraburkholderia sp. 2",
"Burkholderia-Caballeronia-Paraburkholderia sp. 3",
"Bradyrhizobium sp. 2", "Other", "Actinospica"),
values = c("#a1dab4", "#253494","#08306b" ,"#08519c", "#225ea8","#2171b5","#4292c6", "#6baed6", "#9ecae1","#c6dbef", "#deebf7", "#57be7a"))+
guides(fill=guide_legend(title="ESV Identity"))+
theme(legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10, face = "italic"), legend.position = "right",
axis.text.x = element_text(face = "bold",colour = "black", size = 12, angle = 90),
axis.text.y = element_text(face = "bold", size = 12, colour = "black"),
axis.title.y = element_text(face = "bold", size = 14, colour = "black"),
panel.background = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"),
legend.key=element_blank()) +
labs(x= "", y = "Relative Abundance (%)")
#panel b for figure for paper
ps16s.P.ESV = as.data.frame(t(otu_table(ps16s.P)))
ps16s.P.ESV = tibble::rownames_to_column(ps16s.P.ESV, "ID")
ps16s.P.esvmerge = merge(m16s_P, ps16s.P.ESV, by = "ID")
plot(ps16s.P.esvmerge$Nfixd~ps16s.P.esvmerge$ESV_015967)
abline(test)
cor.test(ps16s.P.esvmerge$Nfixd,ps16s.P.esvmerge$ESV_015967)
#test of ESV importance
onlyes = finalP[finalP$variable == "ESV_015967",]