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R_kiwi_mic_2020_analysis_code_submit.R
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R_kiwi_mic_2020_analysis_code_submit.R
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############################################################################
# Author: Priscilla A San Juan
# Topic: Microbiome analysis of kiwi gut microbiome - edited script
# Manuscript: "Kiwi development through a microbial lens: How time,
# local environment, and disease history influence the
# Brown Kiwi (Apteryx mantelli) gut microbiome"
############################################################################
# Table of contents
# -------------------------------------------------------------------------
# 1 - Load packages and color vectors
# 2 - Load files
# 3 - Decontam
# 4 - Subset
# 5 - Alpha diversity
# 6 - Microbial Abundance
# 7 - Ordination
# 8 - PermANOVA
# 9 - Beta diversity
# 10 - clamtest
# 11 - Temporal betadiversity
############################################################################
# 1 LOAD PACKAGES
# -------------------------------------------------------------------------
req_pkg <- c("readr","microbiome","dplyr","phyloseq","vegan","ggplot2","utils",
"microbiomeutilities", "DESeq2","ape","forcats","DescTools","scales",
"tidyr","ResourceSelection", "gridExtra","ggbeeswarm","mvabund",
"RColorBrewer","randomcoloR","MASS", "viridis","ggpubr","decontam",
"ggbeeswarm","fantaxtic","plotly", "microbial")
# Load all required packages and show version
for(i in req_pkg){
print(i)
print(packageVersion(i))
library(i, quietly=TRUE, verbose=FALSE, warn.conflicts=FALSE, character.only=TRUE)
}
library(SRS)
library(performance)
library(ade4) # aux
library(agricolae) # aux
library(lmerTest) # aux
# Color palettes ----------------------------------------------------------
n<-22
z<-34
qual_col_pals=brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector=unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
pie(rep(1,n), col=sample(col_vector, n))
palette_qual <- distinctColorPalette(n)
pie(rep(1, n), col=palette_qual)
palette_qual_all <- distinctColorPalette(z)
pie(rep(1, z), col=palette_qual_all)
colors_scheme_life_stage<-c("Hatch room"="#a1dab4","Brooder room"="#41b6c4","Runs"="#225ea8")
colors_scheme_what<-c("soil"="#fdb462","kiwi poo"="#b3de69")
color_scheme_cohort<-c("young"="#e29580", "old"="#9f5a47")
color_scheme_cocc <- c("positive"="#d95f02","negative"="#1b9e77")
theme_set(theme_light())
###########################################################################
# 2 LOAD FILES
# -------------------------------------------------------------------------
bacteria_otu_matrix<-as.matrix((read_csv("data/bac_otu.csv", col_names=T)[,-1]))
bacteria_tax_matrix<-as.matrix((read_csv("data/bac_tax.csv", col_names=T)[,-1]))
# Add in rownames
rownames(bacteria_otu_matrix)<-paste0("OTU", 1:nrow(bacteria_otu_matrix))
rownames(bacteria_tax_matrix)<-paste0("OTU", 1:nrow(bacteria_tax_matrix))
# Reading the sample data file
sampledata_kiwi <- data.frame(read_csv("data/sample_datasheet_kiwi_2020.csv"))
# Naming the the first rows using the first column
row.names(sampledata_kiwi) <- sampledata_kiwi$samplename
# Add column to sample data - dates from hatch
sampledata_kiwi$daysfromhatch <- sampledata_kiwi$julian..collection.-sampledata_kiwi$julian..hatch.
# Add column to sample data - cohort category
sampledata_kiwi <- sampledata_kiwi %>%
mutate(cohort = case_when(daysfromhatch < 670 ~ "young",
daysfromhatch > 670 ~ "old"))
# Add column to sample data - cohort category
sampledata_kiwi <- sampledata_kiwi %>%
mutate(agebin = case_when(age..days. <= 50 ~ "young",
age..days. > 50 ~ "old"))
# Count how many samples in each category
table(sampledata_kiwi$cohort)
# Remove blank
sampledata_kiwi <- droplevels(sampledata_kiwi[!sampledata_kiwi$what. == 'blank',])
# Add column to sample data - control versus sample
sampledata_kiwi$sample_or_control<-forcats::fct_collapse(
sampledata_kiwi$what.,
Control=c("negative control"),
Sample=c("kiwi poo","possum poo","soil","positive control"),
group_other = T)
sampledata_kiwi$history.of.positive.results <- sampledata_kiwi$history.of.positive.results %>% replace_na('None')
# add column to sample_data
sampledata_kiwi$disease_history <- forcats::fct_collapse(
sampledata_kiwi$history.of.positive.results,
History=c("diarrhea", "coccidiosis","coccidiosis, worms","operation, coccidiosis, worms",
"dehydrated","coccidiosis, worms, eyes", "emphysema, coccidiosis",
"coccidiosis, hernia", "emphysema, coccidiosis, worms", "dehydrated, failed weight gain",
"coccidiosis, fungal"),
None=c("None"),
group_other = T)
# Read file into phyloseq
OTU_kiwi_bac<-otu_table(bacteria_otu_matrix, taxa_are_rows=T)
TAX_kiwi_bac<-tax_table(bacteria_tax_matrix)
SAM_kiwi_bac<-sample_data(sampledata_kiwi)
# Combining OTU, TAX, SAM data
bacteria_kiwi_2020<-phyloseq(OTU_kiwi_bac, TAX_kiwi_bac, SAM_kiwi_bac) # 9816 otus, 1137 samples
###########################################################################
# 3 DECONTAM: FILTER CONTAMS USING NEG CONTROL
# -------------------------------------------------------------------------
# Inspect library sizes
df_bac <- as.data.frame(sample_data(bacteria_kiwi_2020))
df_bac$LibrarySize <- sample_sums(bacteria_kiwi_2020)
df_bac <- df_bac[order(df_bac$LibrarySize),]
df_bac$Index <- seq(nrow(df_bac))
tem_bac <- ggplot(data=df_bac, aes(x=Index, y=LibrarySize, color=sample_or_control))+geom_point(); tem_bac
# Use prevalence method to filter out suspected contaminants
sample_data(bacteria_kiwi_2020)$is.neg <- sample_data(bacteria_kiwi_2020)$sample_or_control == c("Control")
contamdf.prev.bac <- isContaminant(bacteria_kiwi_2020, method="prevalence", neg="is.neg", threshold = 0.4)
table(contamdf.prev.bac$contaminant)
head(which(contamdf.prev.bac$contaminant))
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa.bac <- transform_sample_counts(bacteria_kiwi_2020, function(abund) 1*(abund>0))
ps.pa.neg.bac <- prune_samples(sample_data(ps.pa.bac)$sample_or_control == "Control", ps.pa.bac)
ps.pa.pos.bac <- prune_samples(sample_data(ps.pa.bac)$sample_or_control == "Sample", ps.pa.bac)
# Make data.frame of prevalence in positive and negative samples
df.pa.bac <- data.frame(
pa.pos=taxa_sums(ps.pa.pos.bac),pa.neg=taxa_sums(ps.pa.neg.bac),contaminant=contamdf.prev.bac$contaminant)
ggplotly(ggplot(data=df.pa.bac, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)"))
# Remove contams from phyloseq obj
bac.noncontam <- prune_taxa(!contamdf.prev.bac$contaminant, bacteria_kiwi_2020)
bac.noncontam # Use this phyloseq object for the following steps, 8738 otus, 1137 samples
# normalize first then subset
bac.noncontam.norm <- normalize(bac.noncontam, method="TMM") # 8738 otus, 1137 samples
###########################################################################
# Trimming data
# -------------------------------------------------------------------------
# Remove chloroplast (because plant DNA)
class <- as.vector(data.frame(tax_table(bac.noncontam))$class)
class <- (!(class%in%c("Chloroplast")))
class[is.na(class)]=FALSE
Bac.data.prune_kiwi=prune_taxa(class, bac.noncontam)
# Remove chloroplast (because plant DNA) TMM
class <- as.vector(data.frame(tax_table(bac.noncontam.norm))$class)
class <- (!(class%in%c("Chloroplast")))
class[is.na(class)]=FALSE
Bac.data.prune_kiwi.TMM=prune_taxa(class, bac.noncontam.norm) # 8738 otus, 1137 samples
# Remove chloroplast (because plant DNA)
phyla <- as.vector(data.frame(tax_table(Bac.data.prune_kiwi))$phylum)
phyla <- (!(phyla%in%c("Cyanobacteria/Chloroplast")))
phyla[is.na(phyla)]=FALSE
Bac.data.prune_kiwi=prune_taxa(phyla, Bac.data.prune_kiwi) # 8616 otus, 1137 samples
# Remove chloroplast (because plant DNA) TMM
phyla <- as.vector(data.frame(tax_table(Bac.data.prune_kiwi.TMM))$phylum)
phyla <- (!(phyla%in%c("Cyanobacteria/Chloroplast")))
phyla[is.na(phyla)]=FALSE
Bac.data.prune_kiwi.TMM=prune_taxa(phyla, Bac.data.prune_kiwi.TMM) # 8616 otus, 1137 samples
# Remove controls
control <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi))$sample_or_control)
control <- (!(control%in%c("Control")))
control[is.na(control)]=FALSE
Bac.data.prune_kiwi=prune_samples(control, Bac.data.prune_kiwi) # 8616 otus, 1114 samples
# Remove controls TMM
control <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM))$sample_or_control)
control <- (!(control%in%c("Control")))
control[is.na(control)]=FALSE
Bac.data.prune_kiwi.TMM=prune_samples(control, Bac.data.prune_kiwi.TMM) # 8616 otus, 1114 samples
Bac.data.prune_kiwi.TMM.keepwild=Bac.data.prune_kiwi.TMM # 8616 otus, 1114 samples
# Remove wild samples
captive <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi))$wild.cap)
captive <- (!(captive%in%c("wild")))
captive[is.na(captive)] = FALSE
Bac.data.prune_kiwi = prune_samples(captive, Bac.data.prune_kiwi) # 8616 otus, 962 samples
# Remove wild samples TMM
captive <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM))$wild.cap)
captive <- (!(captive%in%c("wild")))
captive[is.na(captive)] = FALSE
Bac.data.prune_kiwi.TMM = prune_samples(captive, Bac.data.prune_kiwi.TMM) # 8616 otus, 962 samples
# Remove samples with less than 100 reads
Bac.data.prune_kiwi=prune_samples(sample_sums(Bac.data.prune_kiwi)>=100, Bac.data.prune_kiwi) # 8616 otus, 946 samples
Bac.data.prune_kiwi.TMM=prune_samples(sample_sums(Bac.data.prune_kiwi.TMM)>=100, Bac.data.prune_kiwi.TMM) # 8616 otus, 962 samples
# Remove OTUs with zero counts
Bac.data.prune_kiwi=prune_taxa(taxa_sums(Bac.data.prune_kiwi)>0, Bac.data.prune_kiwi) # 7547 otus, 946 samples
Bac.data.prune_kiwi.TMM=prune_taxa(taxa_sums(Bac.data.prune_kiwi.TMM)>0, Bac.data.prune_kiwi.TMM) # 8616 otus, 962 samples
###########################################################################
# 4 SUBSET DATA
# -------------------------------------------------------------------------
kiwi <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi))$what.)
kiwi <- kiwi%in%c("kiwi poo","soil")
kiwi[is.na(kiwi)] = FALSE
Bac.data.prune_kiwi = prune_samples(kiwi, Bac.data.prune_kiwi) # 7547 otus, 832 samples
# TMM
kiwi <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM))$what.)
kiwi <- kiwi%in%c("kiwi poo","soil")
kiwi[is.na(kiwi)] = FALSE
Bac.data.prune_kiwi.TMM = prune_samples(kiwi, Bac.data.prune_kiwi.TMM) # 8616 otus, 847 samples
# wild and cap TMM
kiwi_wild_cap <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM.keepwild))$wild.cap)
kiwi_wild_cap <- kiwi_wild_cap%in%c("cap","wild")
kiwi_wild_cap[is.na(kiwi_wild_cap)] = FALSE
Bac.data.prune_kiwi.TMM.keepwild = prune_samples(kiwi_wild_cap, Bac.data.prune_kiwi.TMM.keepwild) # 8616 otus, 1004 samples
# Subset by poo
poo <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi))$what.)
poo <- poo%in%c("kiwi poo")
poo[is.na(poo)]=FALSE
poodata.bac <- prune_samples(poo, Bac.data.prune_kiwi) # 7547 otus, 752 samples
# Subset by poo TMM
poo <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM))$what.)
poo <- poo%in%c("kiwi poo")
poo[is.na(poo)]=FALSE
poodata.bac.TMM <- prune_samples(poo, Bac.data.prune_kiwi.TMM) # 8616 otus, 767 samples
# Subset by soil
soil <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi))$what.)
soil <- soil%in%c("soil")
soil[is.na(soil)]=FALSE
soildata.bac <- prune_samples(soil, Bac.data.prune_kiwi) # 7547 otus, 80 samples
# Subset by soil
soil <- as.vector(data.frame(sample_data(Bac.data.prune_kiwi.TMM))$what.)
soil <- soil%in%c("soil")
soil[is.na(soil)]=FALSE
soildata.bac.TMM <- prune_samples(soil, Bac.data.prune_kiwi.TMM) # 8616 otus, 80 samples
# Subset by younger birds
youngerdata.bac <- poodata.bac %>%
subset_samples(daysfromhatch<670) %>%
subset_samples(name=!NA)
youngerdata.bac=prune_taxa(taxa_sums(youngerdata.bac)>0, youngerdata.bac) # 3815 otus, 381 samples
# Subset by younger birds TMM
youngerdata.bac.TMM <- poodata.bac.TMM %>%
subset_samples(daysfromhatch<670) %>%
subset_samples(name=!NA)
youngerdata.bac.TMM=prune_taxa(taxa_sums(youngerdata.bac.TMM)>0, youngerdata.bac.TMM) # 8616 otus, 389 samples
# Subset out Frosty (the oldest kiwi - outlier)
no_Frostdata.bac <- poodata.bac %>%
subset_samples(daysfromhatch<7000) %>%
subset_samples(name=!NA)
no_Frostdata.bac=prune_taxa(taxa_sums(no_Frostdata.bac)>0, no_Frostdata.bac) # 5510 otus, 748 samples
# Subset out Frosty (the oldest kiwi - outlier)
no_Frostdata.bac.TMM <- poodata.bac.TMM %>%
subset_samples(daysfromhatch<7000) %>%
subset_samples(name=!NA)
no_Frostdata.bac.TMM=prune_taxa(taxa_sums(no_Frostdata.bac.TMM)>0, no_Frostdata.bac.TMM) # 8616 otus, 763 samples
# remove NA
to_remove <- c(NA)
no_Frostdata.bac <- prune_samples(!(sample_data(no_Frostdata.bac)$name %in% to_remove), no_Frostdata.bac) # 5510 otus, 746 samples
# remove NA TMM
no_Frostdata.bac.TMM <- prune_samples(!(sample_data(no_Frostdata.bac.TMM)$name %in% to_remove), no_Frostdata.bac.TMM) # 8616 otus, 761 samples
# Subset by older birds minus Frosty
olderdata.bac <- no_Frostdata.bac %>%
subset_samples(daysfromhatch>670) %>%
subset_samples(name=!NA)
olderdata.bac=prune_taxa(taxa_sums(olderdata.bac)>0, olderdata.bac) # 4139 otus, 367 samples
# Subset by older birds minus Frosty
olderdata.bac.TMM <- no_Frostdata.bac.TMM %>%
subset_samples(daysfromhatch>670) %>%
subset_samples(name=!NA)
olderdata.bac.TMM=prune_taxa(taxa_sums(olderdata.bac.TMM)>0, olderdata.bac.TMM) # 8616 otus, 374 samples
# Subset no_Frostdata.bac with coccidiosis data
coccid <- as.vector(data.frame(sample_data(poodata.bac))$coccidiosis.status)
coccid <- coccid%in%c("negative","positive")
coccid[is.na(coccid)]=FALSE
poodata.bac.coccid <- prune_samples(coccid, poodata.bac) # 7547 otus, 325 samples (all life stages)
# Subset no_Frostdata.bac with coccidiosis data TMM
coccid <- as.vector(data.frame(sample_data(poodata.bac.TMM))$coccidiosis.status)
coccid <- coccid%in%c("negative","positive")
coccid[is.na(coccid)]=FALSE
poodata.bac.coccid.TMM <- prune_samples(coccid, poodata.bac.TMM) # 8616 otus, 336 samples (all life stages)
# Subset poodata.fun.coccid by life stage (only Runs)
coccid <- as.vector(data.frame(sample_data(poodata.bac.coccid))$location)
coccid <- coccid%in%c("Runs")
coccid[is.na(coccid)]=FALSE
poodata.bac.coccid.runsonly <- prune_samples(coccid, poodata.bac.coccid) # 7547 otus, 62 samples
# Subset poodata.fun.coccid by life stage (only Runs) TMM
coccid <- as.vector(data.frame(sample_data(poodata.bac.coccid.TMM))$location)
coccid <- coccid%in%c("Runs")
coccid[is.na(coccid)]=FALSE
poodata.bac.coccid.runsonly.TMM <- prune_samples(coccid, poodata.bac.coccid.TMM) # 8616 otus, 65 samples
# Subset poodata.fun.coccid by life stage (only Runs)
current <- as.vector(data.frame(sample_data(poodata.bac))$location)
current <- current%in%c("Runs")
current[is.na(current)]=FALSE
poodata.bac.runsonly.current <- prune_samples(current, poodata.bac) # 7547 otus, 488 samples
# Subset poodata.fun.coccid by life stage (only Runs) TMM
current <- as.vector(data.frame(sample_data(poodata.bac.TMM))$location)
current <- current%in%c("Runs")
current[is.na(current)]=FALSE
poodata.bac.runsonly.current.TMM <- prune_samples(current, poodata.bac.TMM) # 8616 otus, 495 samples
# Samples (post-decontam, post-clean, includes poo and soil)
Bac.data.prune_kiwi
Bac.data.prune_kiwi.TMM
Bac.data.prune_kiwi.TMM.keepwild
# Samples (subset of Bac.data.prune_kiwi, just poo)
poodata.bac
poodata.bac.TMM
# Samples (subset of Bac.data.prune_kiwi, just soil)
soildata.bac
soildata.bac.TMM
# Samples (subet of poodata.bac, just poo minus the resident kiwi)
no_Frostdata.bac
no_Frostdata.bac.TMM
# Samples (subet of poodata.bac, just younger cohort)
youngerdata.bac
youngerdata.bac.TMM
# Samples (subet of poodata.bac, just older cohort)
olderdata.bac
olderdata.bac.TMM
# collapse replicates that are TMM normalized
bac_poo_soil_collapse <- collapse_replicates(Bac.data.prune_kiwi.TMM,method = "sample", replicate_fields = c("name", "daysfromhatch")) # 661 samples
bac_poo_collapse <- collapse_replicates(no_Frostdata.bac.TMM,method = "sample", replicate_fields = c("name", "daysfromhatch")) # 624 samples
bac_young_collapse <- collapse_replicates(youngerdata.bac.TMM,method = "sample", replicate_fields = c("name", "daysfromhatch")) # 337 samples
bac_old_collapse <- collapse_replicates(olderdata.bac.TMM,method = "sample", replicate_fields = c("name", "daysfromhatch")) # 289 samples
# The most abundant OTUs within all samples - bacteria
common.taxa.bac <- sort(taxa_sums(no_Frostdata.bac),T)
common.taxa.bac <- tax_table(no_Frostdata.bac)[names(common.taxa.bac[1:50])]; common.taxa.bac
###########################################################################
# 5 Alpha diversity
# -------------------------------------------------------------------------
# what (i.e. sample type) -------------------------------------------------
alpha.bac.shan.what <- plot_richness(
Bac.data.prune_kiwi, x="what.",measures=c("Shannon"), color="what.") +
geom_boxplot() +
scale_colour_manual(values = colors_scheme_what) +
geom_quasirandom(size = 3.0)
alpha.bac.shan.what$layers <- alpha.bac.shan.what$layers[-1]; alpha.bac.shan.what
# ANOVA and Tukey test | anova better for categorical so use lm
aov.what.bac <- aov(value~what., data=alpha.bac.shan.what$data)
summary(aov.what.bac)
HSD.what.bac <- HSD.test(aov.what.bac, "what.", group=T);HSD.what.bac
# soil location -----------------------------------------------------------
alpha.soil <- plot_richness(
soildata.bac, x="location", measures=c("Shannon"), color="location") +
geom_boxplot() +
geom_quasirandom(size = 3.0) +
scale_colour_manual(values=colors_scheme_life_stage)
alpha.soil$layers <- alpha.soil$layers[-1];alpha.soil
# ANOVA and Tukey test | anova better for categorical so use lm
aov.soil.bac <- aov(value~location, data=alpha.soil$data)
summary(aov.soil.bac)
HSD.soil.bac <- HSD.test(aov.soil.bac, "location", group=T);HSD.soil.bac
# cohort ------------------------------------------------------------------
alpha.bac.shan.cohort <- plot_richness(
no_Frostdata.bac, x="cohort", measures=c("Shannon"), color="cohort") +
geom_boxplot() +
scale_colour_manual(values = color_scheme_cohort) +
geom_quasirandom(size = 3.0)
alpha.bac.shan.cohort$data$cohort <- factor(alpha.bac.shan.cohort$data$cohort,levels=c("young", "old"))
alpha.bac.shan.cohort$layers <- alpha.bac.shan.cohort$layers[-1];alpha.bac.shan.cohort
# ANOVA and Tukey test | anova better for categorical so use lm
aov.cohort.bac <- aov(value~cohort, data=alpha.bac.shan.cohort$data)
summary(aov.cohort.bac)
HSD.cohort.bac <- HSD.test(aov.cohort.bac, "cohort", group=T);HSD.cohort.bac
# kiwi location aka life stage --------------------------------------------
alpha.bac.shan.all <- plot_richness(
no_Frostdata.bac, x="location", measures=c("Shannon"), color="location") +
geom_boxplot() +
scale_colour_manual(values = colors_scheme_life_stage) +
geom_quasirandom(size = 3.0)
alpha.bac.shan.all$data$location <- factor(alpha.bac.shan.all$data$location,levels=c("Hatch room", "Brooder room", "Runs"))
alpha.bac.shan.all$layers <- alpha.bac.shan.all$layers[-1];alpha.bac.shan.all
# ANOVA and Tukey test | anova better for categorical so use lm
aov.kiwi.bac.all <- aov(value~location, data=alpha.bac.shan.all$data)
summary(aov.kiwi.bac.all)
HSD.kiwi.bac.all <- HSD.test(aov.kiwi.bac.all, "location", group=T);HSD.kiwi.bac.all
# kiwi origin -------------------------------------------------------------
alpha.bac.shan.origin <- plot_richness(
no_Frostdata.bac, x="origin", measures=c("Shannon"), color="origin") +
geom_boxplot() +
geom_quasirandom(size = 3.0)
alpha.bac.shan.origin$layers <- alpha.bac.shan.origin$layers[-1];alpha.bac.shan.origin
# ANOVA and Tukey test | anova better for categorical so use lm
aov.kiwi.bac.ori <- aov(value~origin, data=alpha.bac.shan.origin$data)
summary(aov.kiwi.bac.ori)
HSD.kiwi.bac.ori <- HSD.test(aov.kiwi.bac.ori, "origin", group=T);HSD.kiwi.bac.ori
# coccidiosis status ------------------------------------------------------
alpha.bac.shan.coccid <- plot_richness(
poodata.bac.coccid.runsonly, x="coccidiosis.status", measures=c("Shannon"), color="coccidiosis.status") +
geom_boxplot() +
geom_quasirandom(size = 3.0)
alpha.bac.shan.coccid$layers <- alpha.bac.shan.coccid$layers[-1]; alpha.bac.shan.coccid
# ANOVA and Tukey test | anova better for categorical so use lm
aov.kiwi.bac.cocc <- aov(value~coccidiosis.status, data=alpha.bac.shan.coccid$data)
summary(aov.kiwi.bac.cocc)
HSD.kiwi.bac.cocc <- HSD.test(aov.kiwi.bac.cocc, "coccidiosis.status", group=T);HSD.kiwi.bac.cocc
# days from hatch younger cohort ------------------------------------------
alpha.bac.shan.daysfromhatch.younger <- plot_richness(
youngerdata.bac, x="daysfromhatch", measures=c("Shannon"), color="daysfromhatch")
ggscatter(alpha.bac.shan.daysfromhatch.younger$data, x="daysfromhatch", y="value",
add = "reg.line", # Add loess
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 3, # Size of dots
color="#a09f9e",
show.legend.text = NA) +
theme_light() +
theme(legend.position = "none")
ggscatter(alpha.bac.shan.daysfromhatch.younger$data, x="daysfromhatch", y="value",
add = "reg.line", # Add loess
add.params = list(color = "name", fill = "name"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 1, # Size of dots
color = "#a09f9e",
show.legend.text = NA) +
facet_wrap(name~.) +
theme_light() +
theme(legend.position = "none") +
ylim(0,5)
# alpha diversity by days from hatch older --------------------------------
alpha.bac.shan.daysfromhatch.older <- plot_richness(
olderdata.bac, x="daysfromhatch", measures=c("Shannon"), color="daysfromhatch")
ggscatter(alpha.bac.shan.daysfromhatch.older$data, x="daysfromhatch", y="value",
add = "reg.line", # Add loess
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 3, # Size of dots
color = "#a09f9e",
show.legend.text = NA) +
theme(legend.position = "none")
ggscatter(alpha.bac.shan.daysfromhatch.older$data, x="daysfromhatch", y="value", color = "cohort",
add = "reg.line", # Add loess
palette = c("#9f5a47","#e29580"),
add.params = list(color = "cohort", fill = "cohort"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 1, # Size of dots
show.legend.text = NA) +
facet_wrap(name~.) +
theme_light() +
theme(legend.position = "none") +
ylim(0,6)
# alpha diversity by collection date --------------------------------------
alpha.bac.shan.colldat <- plot_richness(
poodata.bac, x="julian..collection.", measures=c("Shannon"), color="julian..collection.")
ggscatter(alpha.bac.shan.colldat$data, x="julian..collection.", y="value",
add = "reg.line", # Add loess
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 3, # Size of dots
color = "#a09f9e",
show.legend.text = F) +
theme_light() +
theme(legend.position = "none")
ggscatter(alpha.bac.shan.colldat$data, x="julian..collection.", y="value", color = "cohort",
palette = c("#9f5a47","#e29580"),
add = "reg.line", # Add loess
add.params = list(color = "cohort", fill = "cohort"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 1, # Size of dots
show.legend.text = F) +
facet_wrap(name~.) +
ylim(0,6) +
theme_light() +
theme(legend.position = "none") +
theme(strip.background =element_rect(fill="black")) +
theme(strip.text = element_text(colour = 'white')) +
xlab("Collection date") +
ylab("Shannon diversity value")
# alpha diversity by age --------------------------------------------------
alpha.bac.shan.age <- plot_richness(
no_Frostdata.bac, x="age..days.", measures=c("Shannon"))
alpha.bac.shan.age.all <- ggscatter(alpha.bac.shan.age$data, x="age..days.", y="value", color="grey",
add = "reg.line", # Add loess
conf.int = TRUE, # Add confidence interval
add.params = list(color = "black", fill = "black"), # Customize reg. line
#cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 3, # Size of dots
alpha=0.7,
#color = "black",
show.legend.text = NA) +
theme_light() +
theme(legend.position = "none",panel.grid.minor = element_blank(),panel.grid.major = element_blank()) +
xlab("Age (days)") +
ylab("Shannon diversity value")
ggscatter(alpha.bac.shan.age$data, x="age..days.", y="value", color = "#a09f9e",
add = "reg.line", # Add loess
palette = c("#9f5a47","#e29580"),
add.params = list(color = "cohort", fill = "cohort"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = T, # Add correlation coefficient. see ?stat_cor
size = 1, # Size of dots
show.legend.text = F) +
facet_wrap(name~.) +
theme(legend.position = "none") +
ylim(0,6) +
theme_light() +
theme(strip.background =element_rect(fill="black")) +
theme(strip.text = element_text(colour = 'white'))
# linear mod with age ----------------------------------------------------
hist(alpha.bac.shan.age.all$data$value)
mod_age_bac <- lm(alpha.bac.shan.age.all$data$value~alpha.bac.shan.age.all$data$age..days.)
summary(mod_age_bac)
r2(mod_age_bac)
alpha.bac.shan.age.all
# glmm with age and name as random effect
library(lme4)
mod_age_ran_bac <- lmer(value ~ age..days. + (1|name), data=alpha.bac.shan.age.all$data)
fm2 <- lme(value ~ age..days., data=alpha.bac.shan.age.all$data, random = ~ 1|name)
summary(fm2)
library(nlme)
newdat <- expand.grid(name=unique(alpha.bac.shan.age.all$data$name),
age..days.=c(min(alpha.bac.shan.age.all$data$age..days.),
max(alpha.bac.shan.age.all$data$age..days.)))
ggplot(alpha.bac.shan.age.all$data, aes(x=age..days.,y=value, colour=name)) +
geom_point(size=3) +
geom_line(aes(y=predict(fm2), group=name, size="name")) +
geom_line(data=newdat, aes(y=predict(fm2, level=0, newdata=newdat), size="All")) +
scale_size_manual(name="Predictions", values=c("name"=0.5, "All"=3)) +
theme_bw(base_size=22)
print(p)
summary(mod_age_ran_bac)
r2(mod_age_ran_bac)
# extract the estimates of the fixed effects
fixef(mod_age_ran_bac)
# extract the estimates of the random effects
ranef(mod_age_ran_bac)
r2(mod_age_ran_bac, tolerance=1e-10)
check_singularity(mod_age_ran_bac)
plot(mod_age_ran_bac)
qqnorm(resid(mod_age_ran_bac))
mod_age_bac_check <- lmer(value ~ 1 + (1|name), data=alpha.bac.shan.age$data)
summary(mod_age_bac_check)
r2(mod_age_bac_check)
anova(mod_age_bac_check, mod_age_ran_bac)
AIC(mod_age_bac_check, mod_age_ran_bac)
glm_mod_age_ran_bac <- glmr(value ~ age..days. + (1|name), data=alpha.bac.shan.age.all$data)
summary(glm_mod_age_ran_bac)
check_singularity(glm_mod_age_ran_bac)
r2(glm_mod_age_ran_bac)
plot(mod_age_ran_bac)
qqnorm(resid(mod_age_ran_bac))
###########################################################################
# 6 Bacterial Abundance
# -------------------------------------------------------------------------
# Area plot
bac_poo_collapse_no_norm <- collapse_replicates(
no_Frostdata.bac, method = "sample", replicate_fields = c("name", "daysfromhatch"))
# colors for taxa
bac_fam_col <- c(
"Bradyrhizobiaceae"="#A6CEE3", "Comamonadaceae"="#1F78B4", "Enterobacteriaceae"="#B2DF8A",
"Lachnospiraceae"="#33A02C", "Lactobacillaceae"="#FB9A99", "Moraxellaceae"="#E31A1C",
"Other"="#FDBF6F", "Pseudomonadaceae"="#FF7F00", "Ruminococcaceae"="#CAB2D6",
"Sphingomonadaceae"="#6A3D9A","Unknown"="#FFFF99","#B15928")
# chose specific participants to plot data
pts <- c(
"Monty","Kaitiaki","Python","Sonic","Sojourn","Palindrome","Manawa","Pukukino","Ludo",
"Palomita","Gizmo","Whakaroau", "Moses","KJ","Gummy","Kerrigan","Ata","Adieu", "Knuckles",
"Loki","Frenchie","Sammy","Pippen","Kauri", "Helios","Etrick","Ki Tua","Pakiki","Lonestar",
"Grawp", "Valentin","Leon","Maui","Pihara")
pts_young <- c(
"Monty","Kaitiaki","Python","Sonic","Sojourn","Palindrome", "Manawa","Pukukino","Gizmo",
"Whakaroau","Gummy","Ata","Knuckles","Loki","Frenchie","Sammy","Grawp", "Valentin","Leon",
"Maui","Pihara")
pts_old <- c(
"Ludo","Palomita","Moses","KJ","Kerrigan","Adieu", "Pippen","Kauri","Helios","Etrick",
"Ki Tua","Pakiki","Lonestar")
bac_poo_collapse.namesub <- subset_samples(bac_poo_collapse, name %in% pts)
bac_poo_collapse.ready <- microbiome::transform(bac_poo_collapse.namesub, "compositional")
bac_poo_collapse.namesub.no.norm <- subset_samples(bac_poo_collapse_no_norm, name %in% pts)
bac_poo_collapse.ready.no.norm <- microbiome::transform(bac_poo_collapse.namesub.no.norm, "compositional")
# all cohorts by time (collection date, age, or days from hatch)
area_plot_bac_all_age <- plot_area(
bac_poo_collapse.ready.no.norm,
xvar="age..days.",
level = "phylum", facet.by = "name",
abund.thres = 0.05, prev.thres=0.1,
fill.colors=brewer.pal(12,"Paired"),
ncol=4, nrow=9)
area_plot_bac_all_age +
xlab("Age (days)") +
ylab("Relative Abundance of Bacterial Phyla") +
scale_x_continuous(limits = c(1,132), expand = c(0, 0)) +
scale_y_continuous(labels = scales::percent, expand = expansion(mult=c(0,0))) +
theme(strip.background =element_rect(fill="white")) +
theme(strip.text = element_text(colour = 'black')) +
theme(strip.text.x = element_text(size = 12))
area_plot_bac_all_age_genus <- plot_area(ps.rel, xvar="daysfromhatch",
level = "genus",
facet.by = "name",
abund.thres = 0.1,
prev.thres=0.1,
fill.colors=brewer.pal(12,"Paired"),
ncol=6,
nrow=6)
area_plot_bac_all_age_genus +
xlab("Days from Hatch") +
ylab("Relative Abundance of Fungal Family") +
scale_x_continuous(limits = c(677,764), expand = c(0, 0)) +
scale_y_continuous(labels = scales::percent, expand = expansion(mult=c(0,0))) +
theme(strip.background =element_rect(fill="#9f5a47"))+
theme(strip.text = element_text(colour = 'white')) +
theme(strip.text.x = element_text(size = 12))
###########################################################################################
# 7 ORDINATION
# -------------------------------------------------------------------------
plotbeta(Bac.data.prune_kiwi.TMM.keepwild, group = "wild.cap", ellipse=T, method="PCoA")
set.seed(723)
# use plot_beta function from microbial package, only for categorical
# beta diversity, default is bray
# soil vs poo
bac.pcoa.what.TMM <- plotbeta(bac_poo_soil_collapse, group="what.", ellipse=T, method="PCoA")
bac.pcoa.what.TMM + scale_colour_manual(values = colors_scheme_what)
bac.pcoa.soil.TMM <- plotbeta(soildata.bac.TMM, group="location", ellipse=T, method="PCoA")
bac.pcoa.soil.TMM + scale_colour_manual(values = colors_scheme_life_stage)
# just poo, young vs old
bac.pcoa.cohort.TMM <- plotbeta(bac_poo_collapse, group="cohort", ellipse=T, method="PCoA")
bac.pcoa.cohort.TMM + scale_colour_manual(values = color_scheme_cohort)
plot_ly(x=bac.pcoa.cohort.TMM$data$Axis.1, y=bac.pcoa.cohort.TMM$data$Axis.2, z=bac.pcoa.cohort.TMM$data$Axis.3,
type="scatter3d", mode="markers", color=bac.pcoa.cohort.TMM$data$cohort)
# just poo, life stage
bac.pcoa.lifestage.TMM <- plotbeta(bac_poo_collapse, group="location", ellipse=T, method="PCoA")
bac.pcoa.lifestage.TMM + scale_colour_manual(values = colors_scheme_life_stage)
# ordination
older.ordi.bac <- ordinate(bac_old_collapse, method="PCoA", distance="bray", k=2); beep()
young.ordi.bac <- ordinate(bac_young_collapse, method="PCoA", distance="bray", k=2); beep()
all.poo.ordi.bac <- ordinate(bac_poo_collapse, method="PCoA", distance="bray", k=2); beep()
soil.ordi.bac <- ordinate(soildata.bac.TMM, method="PCoA", distance="bray", k=2); beep()
coccid.ordi.bac <- ordinate(poodata.bac.coccid.runsonly.TMM, method="PCoA", distance="bray", k=2); beep()
sample_data(bac_poo_collapse)$disease_history <- forcats::fct_collapse(
sample_data(bac_poo_collapse)$history.of.positive.results,
History=c("diarrhea", "coccidiosis", "coccidiosis, worms","operation, coccidiosis, worms", "dehydrated",
"emphysema, coccidiosis", "coccidiosis, worms, eyes","coccidiosis, hernia",
"emphysema, coccidiosis, worms", "dehydrated, failed weight gain", "coccidiosis, fungal"),
None=c(NA),group_other = T, stringsAsFactors = FALSE)
levels(sample_data(bac_poo_collapse)$disease_history) = c("History", "None")
sample_data(bac_poo_collapse) <- sample_data(bac_poo_collapse) %>%
replace_na(list(current='None'))
# NMDS all samples colored by INSERT VARIABLE
PCoA.time.bac <- plot_ordination(
(bac_poo_collapse),all.poo.ordi.bac,color="age..days.",shape="disease_history") +
geom_point(size=4, alpha=0.7, na.rm = T) +
scale_color_viridis(option = "D") +
scale_shape_manual(values=c(15,19,18))+
theme(axis.text = element_text(size=18), axis.title = element_text(size=20),
axis.title.x = element_text(colour = "black"),
axis.title.y = element_text(colour = "black")); PCoA.time.bac
# NMDS all samples colored by INSERT VARIABLE
PCoA.coccidiosis.bac <- plot_ordination(
(poodata.bac.coccid.runsonly.TMM),coccid.ordi.bac,color="current.positive.results.",shape="coccidiosis.status") +
geom_point(size=4, alpha=0.7, na.rm = T) +
scale_shape_manual(values=c(15,19,18))+
theme(axis.text = element_text(size=18), axis.title = element_text(size=20),
axis.title.x = element_text(colour = "black"),
axis.title.y = element_text(colour = "black")); PCoA.coccidiosis.bac
# remove young birds
# NMDS all samples colored by INSERT VARIABLE
PCoA.coccidiosis.bac <- plot_ordination((poodata.bac.coccid.runsonly.TMM),
coccid.ordi.bac,color="coccidiosis.status",
shape="coccidiosis.status") +
geom_point(size=4, alpha=0.7, na.rm = T) +
scale_shape_manual(values=c(15,19,18))+
stat_ellipse(aes(col=coccidiosis.status), size = 1) +
theme(axis.text = element_text(size=18), axis.title = element_text(size=20),
axis.title.x = element_text(colour = "black"),
axis.title.y = element_text(colour = "black"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()); PCoA.coccidiosis.bac
# remove young birds
# remove outliers
# AGO529.0773, AGO529.0742
poodata.bac.coccid.runsonly.TMM
outlier_samples <- c("AG0529.0773", "AG0529.0742")
poodata.bac.coccid.runsonly.TMM.remove.out <-
prune_samples(!(sample_data(poodata.bac.coccid.runsonly.TMM)$samplename %in% outlier_samples),
poodata.bac.coccid.runsonly.TMM)
coccid.ordi.bac.no.out <- ordinate(poodata.bac.coccid.runsonly.TMM.remove.out, method="PCoA", distance="bray", k=2); beep()
PCoA.coccidiosis.bac.no.out <- plot_ordination((poodata.bac.coccid.runsonly.TMM.remove.out),
coccid.ordi.bac.no.out,color="coccidiosis.status",
shape="coccidiosis.status") +
geom_point(size=4, alpha=0.7, na.rm = T) +
scale_shape_manual(values=c(15,19,18))+
stat_ellipse(aes(col=coccidiosis.status), size = 1) +
theme(axis.text = element_text(size=18), axis.title = element_text(size=20),
axis.title.x = element_text(colour = "black"),
axis.title.y = element_text(colour = "black"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()); PCoA.coccidiosis.bac.no.out
###########################################################################
# 8 PermANOVA
# -------------------------------------------------------------------------
# dealing with NAs
no_NAs <- as.vector(data.frame(sample_data(no_Frostdata.bac))$weight.of.food.consumed..grams.)
no_NAs <- (!(no_NAs%in%c(NA)))
no_NAs[is.na(no_NAs)] = FALSE
poodata.bac.food.consumed = prune_samples(no_NAs, no_Frostdata.bac)
# variables to look at sample type, location, cohort, coccidiosis, collection date, food weight
# Pick relative abundances (compositional) and sample metadata
otu <- abundances(soildata.bac.TMM)
meta <- meta(soildata.bac.TMM)
permanova <- adonis(t(otu) ~ location, data = meta, permutations=999, method = "bray")
# P-value
print(as.data.frame(permanova$aov.tab)["what.", "Pr(>F)"])
temp_data_age=subset_samples(Bac.data.prune_kiwi.TMM, what.=="kiwi poo")
temp_data_age=subset_samples(temp_data_age,!is.na(cohort))
otu_age <- abundances(temp_data_age)
meta_age <- meta(temp_data_age)
permanova_age <- adonis(t(otu_age) ~ age..days.,
data = meta_age,
permutations=999, method = "bray")
permanova_age
try_mod <- lm(t(otu_age) ~ age..days.,
data = meta_age)
summary(try_mod)
plot(try_mod)
###########################################################################################
# 9 BETA DIVERSITY
# -------------------------------------------------------------------------
# remove NA
to_remove <- c(NA, "Tiggy Winkle")
z <- prune_samples(!(sample_data(bac_poo_collapse)$name %in% to_remove), bac_poo_collapse)
y <- subset_samples(Bac.data.prune_kiwi.TMM, !is.na(cohort))
# OTU table after normalization
otu.beta <- otu_table(y)
# Convert to data frame and transpose and calculate using betadiver
# betadiver is for presence/absence may need to try vegdist or dist
data.beta.pa <- betadiver(as.data.frame(t(otu.beta)), "hk") # presence/absence
data.beta <- vegdist(as.data.frame(t(otu.beta)), method = "bray") # abundance
# Calculate beta diversity using distances from above and matching to sample data
data.dispersion <- with(sample_data(y), betadisper(data.beta, age..days., type="centroid"))
data.dispersion.cohort <- with(sample_data(y), betadisper(data.beta, cohort, type="centroid"))
boxplot(data.dispersion.cohort)
# Permutation test
perm <- permutest(data.dispersion.cohort, pairwise=T)
# max age of kiwi
max_age_of_individuals<-read_csv("data/max_age_of_individuals.csv")
max_age_of_individuals<- max_age_of_individuals %>%
arrange(max_age)
max_age_of_individuals$name
# Putting data in long form so ggplot can read
distance.name <- data.frame(distance=data.dispersion.cohort$distances, name=sample_data(y)$name, cohort=sample_data(y)$cohort, age=sample_data(y)$age..days.)
data.dispersion.cohort
# Plotting in ggplot
kiwi.beta <- ggplot(distance.name, aes(x=cohort, y=distance, col=cohort)) +
scale_color_manual(values = color_scheme_cohort) +
xlab("") + ylab("Distance to Centroid") +
geom_boxplot() +
# geom_smooth(method="lm") +
geom_quasirandom(size=3) +
theme(axis.text = element_text(size=18),
axis.title = element_text(size=20),
panel.border = element_rect(colour = "black", fill = NA),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
# axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position = "none");kiwi.beta
kiwi.beta$data$name <- factor(kiwi.beta$data$name,levels=as.vector(max_age_of_individuals$name))
kiwi.beta$data$cohort <- factor(kiwi.beta$data$cohort,levels=as.vector(c("young","old")))
# anova to see if statistically significantly different
temp.aov <- aov(distance~name, data=distance.name)
summary(temp.aov)
temp.HSD <- HSD.test(temp.aov, "cohort", group = T)
###########################################################################
# 10 clamtest
# -------------------------------------------------------------------------
# Bacteria
# Standard simper code
community.kiwi.bac.t2 <- as.matrix(t(otu_table(Bac.data.prune_kiwi)))
environment_data.kiwi.bac2 <- as.matrix(sample_data(Bac.data.prune_kiwi))
write.csv(environment_data.kiwi.bac2, "environment_data_2020_2.csv")
environment_data.kiwi.bac2 <- data.frame(read_csv("environment_data_2020_2.csv"))
row.names(environment_data.kiwi.bac2) <- environment_data.kiwi.bac2$submit_form_code
write.csv(community.kiwi.bac.t2, "community.kiwi.bac2.csv")
community.kiwi.bac2 <- as.matrix(read_csv("community.kiwi.bac2.csv", col_names=T)[,-1])
row.names(community.kiwi.bac2) <- environment_data.kiwi.bac2$submit_form_code
community.kiwi.bac2 <- community.kiwi.bac2[,-1]
# Remove samples with no sequences in it
community.kiwi.bac.no.zero.OTU2 <- community.kiwi.bac2@.Data[, colSums(community.kiwi.bac2@.Data != 0) > 0]
# clamtest
clam_kiwi2 <- clamtest(community.kiwi.bac.no.zero.OTU2, environment_data.kiwi.bac2$what.)
summary(clam_kiwi2)
saveRDS(clam_kiwi2, "clam_analysis_kiwi_2020_2.RDS")
readRDS("clam_analysis_kiwi_2020_2.RDS")
#c("soil"="#fdb462","kiwi poo"="#b3de69")
base_plot_kiwi <- plot(clam_kiwi2, "Soil OTU Abundances", "Kiwi OTU Abundances", "Bacterial Species Classification",
pch = c(19,19,19,19),
col.points = c("tan3","#b3de69","#fdb462","grey"),cex = 1, las = 1,
col.lines = c("#bf812d","#b3de69","grey"), lty = c(2,2,2), lwd = 3, position = NULL)
legend("bottomright", inset=c(-0.135,0.00),legend = c("Rare", "Kiwi specialist", "Soil specialist", "Generalist"),
col = c("grey","#b3de69","#bf812d","tan3"), pch = c(19,19,19,19), cex = 0.8)
clam_kiwi2_result_df <- as.data.frame(clam_kiwi2)
colnames(clam_kiwi2_result_df) <- c("OTUs", "Kiwi", "Soil", "Classes")
gg_plot_kiwi <- ggplot(
clam_kiwi2_result_df, aes(Soil, Kiwi, col=Classes)) +
geom_point(size=3, alpha=0.7) +
scale_color_manual(values=c("Specialist_soil"="#fdb462",
"Specialist_kiwi poo"="#b3de69",
"Generalist"="tan3",
"Too_rare"="grey")) +
scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))) +
scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))) +
xlab("Soil OTU Abundances") +
ylab("Kiwi OTU Abundances") +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank())
###########################################################################
# Calculate Bray-Curtis ---------------------------------------------------
dist.matrix <- phyloseq::distance(no_Frostdata.bac,"bray")
bray_curtis_vegan <- vegdist(community.kiwi.bac, method="bray", binary=F, diag=F, upper=F, na.rm=F)
# Do I need to calculate bray-curtis values per individual?
# Not sure how this is being calculated
bray_values <- data.frame(bray=with(sample_data(youngerdata.bac), as.numeric(as.matrix(as.dist(vegdist(t(otu_table(youngerdata.bac)), method="bray"), upper = TRUE)))),
name=sample_data(youngerdata.bac)$name,
daysfromhatch=sample_data(youngerdata.bac)$daysfromhatch)
bray_values <- data.frame(name=meta(youngerdata.bac)$name, daysfromhatch=meta(youngerdata.bac)$daysfromhatch,
bray=as.numeric(phyloseq::distance(youngerdata.bac, method="bray")))
ggplot(bray_values,aes(daysfromhatch, bray, fill=name, color=name))+geom_point()+facet_wrap(name~.)
###########################################################################################
# 11 TEMPORAL BETA DIVERSITY
# -------------------------------------------------------------------------
library(MicrobeDS)
library(microbiome)
library(dplyr)
library(vegan)
# Pick the metadata for this subject and sort the
# samples by time
# Pick the data and modify variable names
pseq <- no_Frostdata.bac
# change kiwi name
s <- "Kauri" # Selected subject
# Let us pick a subset, change kiwi name
pseq <- subset_samples(no_Frostdata.bac, name == "Kauri")
# Rename variables
sample_data(pseq)$subject <- sample_data(pseq)$name
sample_data(pseq)$sample <- sample_data(pseq)$samplename
# Order the entries by time
df <- meta(pseq) %>% arrange(daysfromhatch)
# Calculate the beta diversity between each time point and
# the baseline (first) time point
beta <- c() # Baseline similarity
s0 <- subset(df, daysfromhatch == 716)$sample # change the daysfromhatch to the min value of this specific bird
# Let us transform to relative abundance for Bray-Curtis calculations
a <-microbiome::abundances(microbiome::transform(pseq, "compositional"))
for (tp in df$daysfromhatch[-1]) {
# Pick the samples for this subject
# If the same time point has more than one sample,
# pick one at random
st <- sample(subset(df, daysfromhatch == tp)$sample, 1)
# Beta diversity between the current time point and baseline
b <- vegdist(rbind(a[, s0], a[, st]), method = "bray")
# Add to the list
beta <- rbind(beta, c(tp, b))
}
colnames(beta) <- c("daysfromhatch", "beta")
beta <- as.data.frame(beta)
# kiwi individual beta diversity
p <- ggplot(beta, aes(x = daysfromhatch, y = beta)) +
geom_point() +
geom_line() +
geom_smooth() +
labs(x = "Time (Days)", y = "Beta diversity (Bray-Curtis)")
print(p)