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Analyses.R
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Analyses.R
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#####LAKE OKEECHOBEE BACTERIAl primers#####
#https://schuyler-smith.github.io/phylosmith/index.html
#https://david-barnett.github.io/microViz/reference/ps_calc_dominant.html
#https://rdrr.io/github/microbiome/microbiome/man/core_members.html
######## PACKAGES & SUCH ###########
#if (!requireNamespace("devtools", quietly = TRUE)){install.packages("devtools")}
#devtools::install_github("jbisanz/qiime2R")
#install.packages('ggthemes', dependencies = TRUE)
#install.packages('gg_ordiplot')
#install.packages("remotes")
#remotes::install_github("microbiome/microbiome")
#remotes::install_github("gauravsk/ranacapa")
#devtools::install_github("david-barnett/microViz")
#install.packages("remotes")
#remotes::install_github("jfq3/ggordiplots")
#install.packages("GUniFrac")
#devtools::install_github('microsud/microbiomeutilities')
#install.packages("eulerr")
#devtools::install_github("pmartinezarbizu/pairwiseAdonis/pairwiseAdonis")
library(patchwork)
library(pairwiseAdonis)
library(microbiomeutilities)
library(GUniFrac)
library(eulerr)
library(microViz)
library(gclus)
library(ggrepel)
library(ggforce)
library(tibble)
library(BiodiversityR)# also loads vegan
library(ranacapa)
library(microbiome)
library(tidyverse)
library(phyloseq)
library(ggsci)
library(ggthemes)
library(ade4)
library(adegraphics)
library(adespatial)
library(vegan3d)
library(MASS)
library(ellipse)
library(FactoMineR)
library(rrcov)
library(missMDA)
library(Amelia)
library(BiodiversityR)
library(ggordiplots)
library(indicspecies)
#####Determining periods of nutrient limitation#####
library(tidyverse)
library(lubridate)
#read in report
REPORT <- read.csv("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/LakeO_Sites_NP.csv")
#Remove those weird samples
report = REPORT %>% filter(Sample != "FCEB")
#Filter down to appropriate times
x1 = with(report, report[(Date >= "20190620" & Date <= "20201102"),])
#Remove the NAs
report1 = x1 %>% drop_na(TOTAL.N)
report2 = report1 %>% select(-Sample)
#Convert to numeric
report2.5 = report2 |> filter(!is.na(as.numeric(TOTAL.N)))
report2.75 = report2.5 |> filter(!is.na(as.numeric(TPO4)))
report2.75$TOTAL.N = as.numeric(report2.75$TOTAL.N)
report2.75$TPO4 = as.numeric(report2.75$TPO4)
#Make new variable
report3 = report2.75 %>% mutate(TNTP = TOTAL.N / TPO4)
#Convert to date
report3$Date <- ymd(report3$Date)
#Add limitation data
report4 = report3 %>%
mutate(limitation = case_when(TNTP <= 9 ~ 'N',
TNTP > 9 & TNTP < 23 ~ 'Co',
TNTP >= 23 ~ 'P'))
#Plot
plot <- ggplot(data=report4, aes(x=Date, y=TNTP, group = Station, colour = Station)) +
geom_line() +
labs(y= "TN:TP", x = "Date") +
geom_point() +
facet_wrap( ~ Station, ncol=3)
plot
#These are sample dates!!!
dates_vline <- as.Date(c("2019-08-20", "2019-09-17","2019-10-23","2019-12-03","2020-01-14","2020-02-19","2020-04-07","2020-06-09","2020-07-15","2020-09-02"))
#add to plot
plot +
geom_vline(xintercept = as.numeric(dates_vline), col = "black", lwd = 0.2) +
geom_hline(yintercept = c(9,23), col = "red", lwd = 0.5)
#Restart R and clear environment
rm(list=ls())
.rs.restartR()
#####Load phyloseq object and metadata#####
setwd("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Apr2023")
#Reads in total physeq object
physeq_tot = readRDS("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Apr2023/physeq_objects/Physeq_fullApr24.rds")
#x = sample_sums(physeq_Lake2)
#y = taxa_sums(physeq)
#x %>% mean(.)
#Reads in Cyano physeq object
physeq_bar = readRDS("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Apr2023/physeq_objects/Physeq_CyanoMay1.rds")
#x = sample_sums(physeq_Lake2)
#y = taxa_sums(physeq)
#x %>% mean(.)
#These samples suck
physeq <- prune_samples(sample_names(physeq_bar) != "LO2", physeq_bar)
physeq <- prune_samples(sample_names(physeq) != "LO6", physeq)
physeq_alpha = readRDS("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/physeq_objects/LakeO_Cyano_forFD_Jan12.rds")
#View tree, very importatnt to ensure that the tree isnt fucky and polyphletic.
#This WILL alter your results, assuming you are leveraging these data
#p_p = physeq %>% subset_taxa(Family =="Prochlorococcaceae")
#plot_tree(p_p, color = "Genus", label.tips = "taxa_names")
#plot_tree(physeq_bac, color = "Phylum")
metadata_magic <- read.csv("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/data/LakeO_Bac_Meta_w_DBHYDRO_limitation.csv", header = T, row.names = 1) %>% tibble::rownames_to_column(var = "SampleID") %>%
filter(WaterBody == "Lake") %>% dplyr::filter(SampleID != "LO2") %>% dplyr::filter(SampleID != "LO6")
metadata_magic_L <- read.csv("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/data/LakeO_Bac_Meta_w_DBHYDRO_limitation.csv", header = T, row.names = 1) %>% tibble::rownames_to_column(var = "SampleID") %>%
filter(WaterBody == "Lake") %>% dplyr::filter(SampleID != "LO2") %>% dplyr::filter(SampleID != "LO6") %>% filter(Limitation != "P")
metadata <- read.csv("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/data/LakeO_Bac_Meta_w_DBHYDRO_limitation.csv", header = T, row.names = 1) %>% tibble::rownames_to_column(var = "SampleID") %>% filter(WaterBody == "Lake")
#metadata = as.data.frame(physeq@sam_data)
#These keep reading in as characters, idk why
metadata$TSI = as.numeric(metadata$TSI)
metadata$TN_TP = as.numeric(metadata$TN_TP)
metadata$tot_cya = as.numeric(metadata$tot_cya)
metadata$TN_TP_X = as.numeric(metadata$TN_TP_X)
metadata$Outing = as.factor(metadata$Outing)
metadata$Site = as.factor(metadata$Site)
metadata$Location = as.factor(metadata$Location)
metadata$Limitation = as.character(metadata$Limitation)
physeq@sam_data$Outing = as.factor(physeq@sam_data$Outing)
physeq@sam_data$Location = as.factor(physeq@sam_data$Location)
#For the RDA we cannot have NA values, nor should we have any negative values (Based on a priori knowledge of this particular data set)
#Because of this, we are going to fill in NA values with median based on location AND replaces negative values with 0
#This replaces NA with median based on location AND replaces negative values with 0, and then takes sqrt of the final variables
metadata_RDA = metadata %>%
dplyr::select(-c('SO4_mg.L_X',"CollectionYear","DATE","CollectionDate","DATE","DO_mg.L_X", 'B_mgL', "TN_TP",#"tot_cya",
"NA_mg.L_X","K_mg.L_X","CA_mg.L_X","MG_mg.L_X","CHLA.LC_ug.L_X","PC_ugL","Chl_ugL",
"MCLR_ugL", "MCRR_ugL", "MC_dimethylLR_ugL","MC_980LR_deriv_ugL","Nodularin_811_ugL",
"Anatoxin_a_ugL","Euglenaphycin_ugL",'Anabaenopeptins',
'Time', 'Station_Id','TSI_Chla',
'TSI_TN','TSI_TP','TSI_TN2','TSI_TP2','SPCOND_uS.cm_x','TURB_NTU_X','T.SUS.SD_mg.L_X','NH4_mg.L_X',
'OPO4_mg.L_X','TPO4_mg.L_X','ALKALNTY_mg.L_X','TSI','Cyanopeptide','WaterBody','Secchi_Depth'
)) %>%
#data.table::setDT() %>%
group_by(Site) %>%
mutate_all(funs(ifelse(is.na(.), median(., na.rm = TRUE), .))) %>%
mutate_all(funs(ifelse(. < 0, 0, .))) %>%
mutate_all(funs(ifelse(is.infinite(.), median(., na.rm = TRUE), .))) %>%
mutate("DIN" = (NOx_mgL+NH3_mgL)) %>%
mutate("DIN_DIP" = (NOx_mgL+NH3_mgL)/TRP_mgL) %>%
mutate_all(funs(ifelse(is.infinite(.), median(., na.rm = TRUE), .))) %>%
dplyr::select(-c('NOX_mg.L_X','TOTAL_N_mg.L_X',"TN_mgL","TN_TP_X","Salinity_state","tot_cya",
"DO.","Sample","TRP_mgL", "TP_mgL", "TN_mgL","NOx_mgL","NO2_mgL","NO3_mgL","NH3_mgL","Depth_m")) %>%
mutate_at(c("Photic_Depth","Temp","DO_mgL","pH","Conductivity_SPC","Turbidity_FNU","OP_mgL",
"Al_mgL","Ca_mgL","Cu_mgL","Fe_mgL","K_mgL","Mg_mgL","Mn_mgL","Na_mgL","Zn_mgL",
"DIN","DIN_DIP"), sqrt) %>%
as.data.frame()
#This is a secret tool that will help us later
#metadata_RDA1 = metadata_RDA %>% column_to_rownames("SampleID")
#physeq1@sam_data <- sample_data(metadata_RDA1)
metadata_RDA$Site = as.factor(metadata_RDA$Site)
metadata_RDA$Limitation = as.factor(metadata_RDA$Limitation)
#Based on unifrac beta diversity there appears to be a difference between wet/dry season and between certain locations.
#Were going to make a subset of wet, dry, and perhaps each location and outing, but need to confirm with beta
physeq_dry = subset_samples(physeq, `Season` == "Dry")
physeq_wet = subset_samples(physeq, `Season` == "Wet")
physeq_N = subset_samples(physeq, `Limitation` == "N")
physeq_Co = subset_samples(physeq, `Limitation` == "Co")
#Subset limitations sans P-limitation
metadata_L = metadata_RDA %>% filter(Limitation != "P")
physeq_L = subset_samples(physeq, `Limitation` != "P")
physeq_L_BAR = subset_samples(physeq_bar, `Limitation` != "P")
physeq_alpha_L = subset_samples(physeq_alpha, `Limitation` != "P")
#p = metadata %>% dplyr::filter(Cyanopeptide == "Y") %>% dplyr::filter(WaterBody == "Lake") %>% dplyr::select("Outing","Site","MCLR_ugL","MCRR_ugL","MC_dimethylLR_ugL","MC_980LR_deriv_ugL","Nodularin_811_ugL","Anatoxin_a_ugL","Anabaenopeptins","Season")
#write_csv(p, "~/Dropbox (UFL)/Lefler_Dissertation/LakeO_Phylo/toxins.csv")
#####Export for network analysis#####
#This is kind of shit show and far from perfect code, but it works.
physeq_tot = readRDS("~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/physeq_objects/LakeO_Feb8.rds")
#Fill in taxa names with lowest classified rank
physeq2 = physeq_tot %>% tax_fix() %>% tax_glom(taxrank="Genus", NArm=F)
c = as.data.frame(physeq2@otu_table)
#Filter out low abundance taxa
physeq_Lake22 = microViz::tax_filter(physeq2,
#min_prevalence = , #must occur in at least TWO samples we have 49 samples, 2/49 = 0.04, not using this since so many diverse environments
min_total_abundance = 408 #THE ASV MUST OCCUR AT LEAST 100 TIMES IN TOTAL ACROSS ALL SAMPLES
) #This manages to be the top 500 ASVs
physeq_Lake22
gfns = as.data.frame(physeq_Lake22@tax_table) #%>% rownames_to_column() %>% dplyr::select(c("Phylum","Genus"))
gg = as.data.frame(physeq_Lake22@otu_table) %>% sjmisc::rotate_df()
gg$abundance <- rowSums(gg)
ggg = gg %>% dplyr::select(abundance)
ff = merge(gfns, ggg, by=0)
fff = ff %>% dplyr::select(c(Phylum, Genus, abundance))
write_csv(fff,"~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/data/ELSA_500_taxonomy.csv")
#Stolen code lol but basically makes your names in "Dolichospermum ASV123"
# generate a vector containing the full taxonomy path for all OTUs
wholetax <- do.call(paste, c(as.data.frame(tax_table(physeq_Lake22))
[("Genus")],
sep = "_")) # to distinguish from "_" within tax ranks
# turn the otu_table into a data.frame
otu_export <- as.data.frame(otu_get(physeq_Lake22))
tmp <- names(otu_export)
# paste wholetax and OTU_ids together
for(i in 1:length(tmp)){
names(tmp)[i] = paste(wholetax[i], sep = "_")
}
# overwrite old names
names(otu_export) <- names(tmp)
head(otu_export)[4]
thing = otu_export %>% tibble::rownames_to_column(var = "SampleID") %>% arrange(SampleID)
otu_export = thing %>% column_to_rownames(var="SampleID")
dune.Hellinger_otu_export <- BiodiversityR::disttransform(otu_export, method='hellinger')
pp = dune.Hellinger_otu_export %>% tibble::rownames_to_column(var = "SampleID")
data_frame_merge_3 <- merge(pp, metadata_RDA,
by = 'SampleID', all = TRUE)
#Makes the row names into what ELSA requires
rownames(data_frame_merge_3) <- paste0("t",data_frame_merge_3$Outing,"r",data_frame_merge_3$Location)
data_frame_merge_3 = data_frame_merge_3 %>% dplyr::select(-c(Outing,Limitation,Location,Site,Season,DO_mgL,SampleID)) %>% sjmisc::rotate_df()
#If youre missing samples youll need to create a column for the missing samples otherwise ELSA wont run
data3 = data_frame_merge_3 %>%
mutate(t3r4 = c(0)) %>%
mutate(t10r1 = c(0)) %>%
mutate(t10r3 = c(0)) %>%
rownames_to_column()
#Ensure that you have NO CHARACTERS
#Write as a tsv
write_tsv(data3, col_names = T, "~/Dropbox (UFL)/Laughinghouse_Lab/MANUSCRIPTS/Lake_Okeechobee_Cyano/Jan2023/ELSA_500_Feb8.txt")
#####BARPLOTS#####
#Phylum
phylum = physeq_tot %>% tax_fix() %>%
ps_mutate(subject_timepoint = interaction(Outing)) %>%
ps_arrange(Outing, desc(Outing)) %>%
comp_barplot(tax_level = "Phylum", n_taxa = 10, facet_by = "Site", nrow = 1, sample_order = "default", label = "subject_timepoint") +
labs(x = "Outing",
y = "Relative Abundance")
Order = physeq_bar %>% tax_fix() %>%
ps_mutate(subject_timepoint = interaction(Outing)) %>%
ps_arrange(Outing, desc(Outing)) %>%
comp_barplot(tax_level = "Order", n_taxa = 10, facet_by = "Site", nrow = 1, sample_order = "default", label = "subject_timepoint") +
labs(x = "Outing",
y = "Relative Abundance")
Family = physeq %>% tax_fix() %>%
ps_mutate(subject_timepoint = interaction(Outing)) %>%
ps_arrange(Outing, desc(Outing)) %>%
comp_barplot(tax_level = "Family", n_taxa = 9, facet_by = "Site", nrow = 1, sample_order = "default", label = "subject_timepoint") +
labs(x = "Outing",
y = "Relative Abundance")
library(patchwork)
(phylum / Order / Family) + plot_annotation(tag_levels = 'A')
#Cyano Genus
Bac_Cyano_Genus = physeq %>% tax_fix() %>%
ps_mutate(subject_timepoint = interaction(Outing)) %>%
ps_arrange(Outing, desc(Outing)) %>%
comp_barplot(tax_level = "Genus", n_taxa = 19, facet_by = "Site", nrow = 1, sample_order = "default", label = "subject_timepoint") +
labs(x = "Outing",
y = "Relative Abundance")
#
#####Season#####
#Faiths PD
dd = metadata %>% filter(WaterBody == "Lake")
FD = picante::pd(physeq_alpha@otu_table, physeq_alpha@phy_tree, include.root=TRUE) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(., dd, by = "SampleID" )%>% filter(WaterBody == "Lake")
FD$Outing = as.factor(FD$Outing)
FD_Season = FD %>%
ggplot(aes(x = Season, y = PD, fill = Season)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
labs(title = "Faith's PD",
x = "Season",
y = "Diversity Measure")
res_aov <- aov(FD$PD ~ FD$Season)
summary(res_aov) #0.84
#Chao
chao = richness(physeq_alpha, index = "chao1", detection = 0) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(.,dd, by = "SampleID" ) %>% filter(WaterBody == "Lake")
chao$Season = as.factor(chao$Season)
C_Season = chao %>%
ggplot(aes(x = Season, y = chao1, fill = Season, group = Season)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
labs(title = "Chao1",
x = "Season",
y = "")
res_aov1 <- aov(chao$chao1 ~ chao$Season)
summary(res_aov1) #0.75
#OLD
#z = plot_richness(physeq_alpha, x = "Season", measures = c("Chao1", "Simpson")) + geom_violin(width=1.4) +
# geom_boxplot(width=0.2, alpha=0.5) + theme_minimal()
#Barplot, ok so like 60% of this is pico
a = physeq_bar %>% tax_fix() %>%
ps_select(Site, Season) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Season") %>%
comp_barplot(tax_level = "Family", n_taxa = 10, bar_width = 0.8) +
coord_flip()
#Lets remove the Pico
physeq_cyanobacteria2 = physeq_bar %>%
subset_taxa(Family !="Prochlorococcaceae")
physeq_cyanobacteria3 = physeq %>%
subset_taxa(Family =="Prochlorococcaceae")
#And try again
b = physeq_cyanobacteria2 %>% tax_fix() %>%
ps_select(Site, Season) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Season") %>%
comp_barplot(tax_level = "Genus", n_taxa = 10, bar_width = 0.8) +
coord_flip()
#Lets just peek at the peek-o
physeq_cyanobacteria3 %>% tax_fix() %>%
ps_select(Site, Season) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Season") %>%
comp_barplot(tax_level = "unique", n_taxa = 19, bar_width = 0.8) +
coord_flip()
physeq_cyanobacteria2 %>% tax_fix() %>%
ps_select(Site, Season) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Season") %>%
comp_barplot(tax_level = "Family", n_taxa = 10, bar_width = 0.8) +
coord_flip()
#If we do log10 transform, it looks like this. This is more significant than hellinger, althought they are both < 0.05
c = physeq %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5) %>%
ord_calc(method = "NMDS") %>%
ord_plot(colour = "Season", size = 2, auto_caption = NA) +
stat_ellipse(aes(colour = Season), type = "norm")
x <- physeq %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5)
# the more permutations you request, the longer it takes
# but also the more stable and precise your p-values become
w <- x %>%
dist_permanova(
seed = 1234, # for set.seed to ensure reproducibility of random process
n_processes = 1, n_perms = 9999, # you should use at least 999!
variables = "Season"
)
# view the permanova results
perm_get(w) %>% as.data.frame()
# Df SumOfSqs R2 F Pr(>F)
#Season 1 0.03811361 0.06347494 3.592186 0.0069
#Residual 53 0.56233774 0.93652506 NA NA
#Total 54 0.60045135 1.00000000 NA NA
#If we do hellinger transform, it looks like this
c = physeq %>% tax_fix() %>%
tax_transform("hellinger", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5) %>%
ord_calc(method = "NMDS") %>%
ord_plot(colour = "Season", size = 2, auto_caption = NA) +
stat_ellipse(aes(colour = Season), type = "norm") +
theme(legend.position = c(0.15, 0.87),
legend.background = element_rect(fill = "white", color = "black"))
c
x1 <- physeq %>% tax_fix() %>%
tax_transform("hellinger", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5)
#> Proportional min_prevalence given: 0.1 --> min 23/222 samples.
# the more permutations you request, the longer it takes
# but also the more stable and precise your p-values become
w1 <- x1 %>%
dist_permanova(
seed = 1234, # for set.seed to ensure reproducibility of random process
n_processes = 1, n_perms = 9999, # you should use at least 999!
variables = "Season"
)
# view the permanova results
perm_get(w1) %>% as.data.frame()
# Df SumOfSqs R2 F Pr(>F)
#Season 1 0.04053127 0.0414625 2.37908 0.034
#Residual 55 0.93700918 0.9585375 NA NA
#Total 56 0.97754045 1.0000000 NA NA
library(patchwork)
qq = (FD_Season | C_Season | c) + plot_layout(guides = 'collect')
(qq) / b + plot_annotation(tag_levels = 'A')
#https://cran.r-project.org/web/packages/indicspecies/vignettes/IndicatorSpeciesAnalysis.html
#install.packages("indicspecies")
library(indicspecies)
library(tidyverse)
genus <- physeq %>% tax_glom(taxrank="Genus", NArm=T) %>% tax_fix() %>%
tax_transform("log10", rank = "Genus")
genus_abund = as.data.frame(genus@otu_table) %>% arrange()
metadata_season1 = metadata_magic %>% arrange(SampleID)
genus_abund_mp = indicspecies::multipatt(genus_abund, metadata_season1$Season, func = "r.g", control = how(nperm=9999))
summary(genus_abund_mp)
Group Dry #sps. 13
stat p.value
Microcystaceae Cluster 2 0.470 0.0003 ***
Aphanizomenon 0.462 0.0007 ***
Woronichinia 0.444 0.0013 **
Regnicoccus 0.441 0.0012 **
Lacustricoccus 0.439 0.0006 ***
Prochlorococcaceae_XX 0.421 0.0014 **
Raphidiopsis 0.383 0.0046 **
Synechocystis 0.380 0.0058 **
Metis 0.345 0.0128 *
Pegethrix 0.316 0.0235 *
Cuspidothrix 0.312 0.0216 *
Leptolyngbyaceae Cluster 1 0.303 0.0285 *
Nodosilinea 0.276 0.0447 *
Group Wet #sps. 3
stat p.value
Synechococcaceae Cluster 1 0.664 0.0001 ***
Apatinema 0.365 0.0062 **
Neocylindrospermum 0.301 0.0228 *
#
#####Limitation#####
#Faiths PD
dd_L = metadata_L
FD_L = picante::pd(physeq_alpha_L@otu_table, physeq_alpha_L@phy_tree, include.root=TRUE) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(., dd_L, by = "SampleID" )
FD_L$Outing = as.factor(FD_L$Outing)
FD_L_plot = FD_L %>%
ggplot(aes(x = Limitation, y = PD, fill = Limitation)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
labs(title = "Faith's PD",
x = "Limitation",
y = "Diversity Measure")
res_aov <- aov(FD_L$PD ~ FD_L$Limitation)
summary(res_aov)
#Chao
chao = richness(physeq_alpha_L, index = "chao1", detection = 0) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(.,dd_L, by = "SampleID" )
chao$Limitation = as.factor(chao$Limitation)
C_L = chao %>%
ggplot(aes(x = Limitation, y = chao1, fill = Limitation, group = Limitation)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
labs(title = "Chao1",
x = "Limitation",
y = "")
res_aov1 <- aov(chao$chao1 ~ chao$Limitation)
summary(res_aov1)
#Barplot, ok so like 50% of this is pico
a2 = physeq_L_BAR %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Limitation") %>%
comp_barplot(tax_level = "Family", n_taxa = 10, bar_width = 0.8) +
coord_flip()
#Lets remove the Pico
physeq_L_cyanobacteria2 = physeq_L_BAR %>%
subset_taxa(Family !="Prochlorococcaceae")
physeq_L_cyanobacteria3 = physeq_L_BAR %>%
subset_taxa(Family =="Prochlorococcaceae")
#And try again
b = physeq_L_cyanobacteria2 %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Limitation") %>%
comp_barplot(tax_level = "Genus", n_taxa = 10, bar_width = 0.8) +
coord_flip()
b2 = physeq_L_BAR %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Limitation") %>%
comp_barplot(tax_level = "Family", n_taxa = 10, bar_width = 0.8) +
coord_flip()
(a / a2) + plot_layout(guides = 'collect') + plot_annotation(tag_levels = 'A')
#Lets just peek at the peek-o
physeq_L_cyanobacteria3 %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Limitation") %>%
comp_barplot(tax_level = "Genus", n_taxa = 30, bar_width = 0.8) +
coord_flip()
physeq %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Limitation") %>%
comp_barplot(tax_level = "Genus", n_taxa = 10, bar_width = 0.8) +
coord_flip()
#If we do log10 transform, it looks like this.
c = physeq_L %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5) %>%
ord_calc(method = "NMDS") %>%
ord_plot(colour = "Limitation", size = 2, auto_caption = NA) +
stat_ellipse(aes(colour = Limitation), type = "norm")
x <- physeq_L %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5)
w <- x %>%
dist_permanova(
seed = 1234, # for set.seed to ensure reproducibility of random process
n_processes = 1, n_perms = 99999, # you should use at least 999!
variables = c("Limitation")
)
# view the permanova results
perm_get(w) %>% as.data.frame()
# Df SumOfSqs R2 F Pr(>F)
#Limitation 1 0.01036068 0.01788898 0.9471711 0.4204
#Residual 52 0.56880472 0.98211102 NA NA
#Total 53 0.57916540 1.00000000 NA NA
library(patchwork)
qq = (FD_L_plot | C_L) + plot_layout(guides = 'collect')
(qq | c) / b + plot_annotation(tag_levels = 'A')
(a / a2)+ plot_layout(guides = 'collect') + plot_annotation(tag_levels = 'A')
#https://cran.r-project.org/web/packages/indicspecies/vignettes/IndicatorSpeciesAnalysis.html
#install.packages("indicspecies")
library(indicspecies)
genus <- physeq_L %>% tax_glom(taxrank="Genus", NArm=F) %>% tax_fix() %>%
tax_transform("hellinger", rank = "Genus")
genus_abund = as.data.frame(genus@otu_table)
metadata_season1_L = metadata_magic_L %>% arrange(SampleID)
genus_abund_mp = indicspecies::multipatt(genus_abund, metadata_season1_L$Limitation, func = "r.g", control = how(nperm=9999))
summary(genus_abund_mp)
# Group N
# stat p.value
#Synechococcaceae Cluster 1 0.403 0.0069 **
#Planktothrix 0.309 0.0271 *
#
#####Site#####
#Barplot, ok so like 50% of this is pico
a2 = physeq_bar %>% tax_fix() %>%
ps_select(Site, Limitation) %>% # avoids lots of phyloseq::merge_samples warnings
phyloseq::merge_samples(group = "Site") %>%
comp_barplot(tax_level = "Genus", n_taxa = 15, bar_width = 0.8) +
coord_flip()
#Faiths PD
dd = metadata %>% filter(WaterBody == "Lake")
FD = picante::pd(physeq_alpha@otu_table, physeq_alpha@phy_tree, include.root=TRUE) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(., dd, by = "SampleID" )%>% filter(WaterBody == "Lake")
FD$Outing = as.factor(FD$Outing)
FD_Site = FD %>%
ggplot(aes(x = Site, y = PD, fill = Site)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
labs(title = "Faith's PD",
x = "Season",
y = "Diversity Measure")
res_aov <- aov(FD$PD ~ FD$Site)
summary(res_aov)
TukeyHSD(res_aov, conf.level=.95)
#Chao
chao = richness(physeq_alpha, index = "chao1", detection = 0) %>% tibble::rownames_to_column(var = "SampleID") %>% full_join(.,dd, by = "SampleID" ) %>% filter(WaterBody == "Lake")
chao$Season = as.factor(chao$Season)
C_Season = chao %>%
ggplot(aes(x = Site, y = chao1, fill = Site, group = Site)) +
geom_violin(width=1) +
geom_boxplot(width=0.2, alpha=0.5) +
#geom_point(position = position_jitter(height = 0.2), alpha = 0.5) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
labs(title = "Chao1",
x = "Site",
y = "")
res_aov1 <- aov(chao$chao1 ~ chao$Site)
summary(res_aov1)
TukeyHSD(res_aov1, conf.level=.95)
#z = plot_richness(physeq, x = "Site", measures = c("Chao1", "Simpson")) + geom_violin(width=1.4) +
# geom_boxplot(width=0.2, alpha=0.5) + theme_minimal() + theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1))
#If we do log10 transform, it looks like this. This is more significant than hellinger, althought they are both < 0.05
c = physeq %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5) %>%
ord_calc(method = "NMDS") %>%
ord_plot(colour = "Site", size = 2) +
stat_ellipse(aes(colour = Site))
x <- physeq %>% tax_fix() %>%
tax_transform("log10", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5)
w <- x %>%
dist_permanova(
seed = 1234, # for set.seed to ensure reproducibility of random process
n_processes = 1, n_perms = 9999, # you should use at least 999!
variables = "Site"
)
b = dist_get(x)
b
metadata4 = metadata %>% arrange(SampleID) %>% column_to_rownames(var="SampleID")
pairwise_site = pairwiseAdonis::pairwise.adonis(b, metadata4[,"Site"],perm = 99999, p.adjust.m = "holm")
pairwise_site %>% filter(p.adjusted <= 0.05)
#NA
library(patchwork)
(FD_Site | C_Season | c) / a2 + plot_layout(guides = 'collect') + plot_annotation(tag_levels = 'A')
#https://cran.r-project.org/web/packages/indicspecies/vignettes/IndicatorSpeciesAnalysis.html
#install.packages("indicspecies")
library(indicspecies)
genus <- physeq %>% tax_glom(taxrank="Genus", NArm=T) %>% tax_fix() %>%
tax_transform("log10", rank = "Genus")
genus_abund = as.data.frame(genus@otu_table)
Impact_Site = metadata$Site
genus_abund_mp = multipatt(genus_abund, metadata$Site, duleg = TRUE, func = "r.g", control = how(nperm=9999))
summary(genus_abund_mp)
#Group Clewiston #sps. 1
# stat p.value
#Apatinema 0.643 1e-04 ***
#Group Eagle Bay #sps. 5
# stat p.value
#Metis 0.377 0.0187 *
#Prochlorococcaceae_XX 0.364 0.0302 *
#Snowella 0.360 0.0378 *
#Aphanizomenon 0.358 0.0212 *
#Microcystaceae Family 0.345 0.0256 *
#Group Kissimmee Mouth #sps. 2
# stat p.value
#Radiocystis 0.427 0.0133 *
#Planktothricoides 0.347 0.0326 *
#Group South Lake #sps. 3
# stat p.value
#Prochlorococcus 0.353 0.0294 *
#Prochlorococcaceae_XXX 0.315 0.0379 *
#Parasynechococcus 0.302 0.0443 *
#db-pRDA
physeq %>% tax_fix() %>%
tax_transform("hellinger", rank = "unique") %>%
dist_calc(dist = "gunifrac", gunifrac_alpha = 0.5) %>%
ord_calc(
#constraints = c("DIN_DIP"),
conditions = c("Outing", "Location"),
method = "CAP",
scale_cc = F
) %>%
ord_plot(
colour = "Season", size = 2,
plot_taxa = 1:8
) +
stat_ellipse(aes(colour = Season))
#####
#####Toxin correlations######
physeq_tox = subset_samples(physeq_bar, `MCLR_ugL` >= "0")
physeq_tox2 = physeq_tox %>%
subset_taxa(Family != "Prochlorococcaceae")
physeq_tox2 %>% tax_fix() %>%
tax_transform("log10", zero_replace = "halfmin") %>%
tax_agg("Genus") %>%
cor_heatmap(vars = c("MCLR_ugL", "MCRR_ugL","Nodularin_811_ugL"), cor = "kendall", tax_anno = taxAnnotation(
Prev. = anno_tax_prev(ylim = 0:1)))
#####RDA#####
#This code runs the RDA
#
#Make sure youre using the correct metadata, with NA's filled in
#A quick note
#There is a lot of code just produce one plot, and several objects are made.
#Because of this, each iteration of this code is IDENTICAL
#the final plot is given a specific name
#Transform data and glom as genus
x <- physeq %>% tax_glom(taxrank="Genus", NArm=T) %>% tax_fix() %>%
tax_transform("log10", rank = "Genus")
#Pull otu table as dataframe
x2 = as.data.frame(x@otu_table)
#Arrange based on identifier
x3 = x2 %>% tibble::rownames_to_column() %>% arrange(rowname)
#Subsample metadata, only needed if subsampling ASVs
metadata4 = metadata_RDA %>% filter(SampleID %in% x3$rowname)#Only needed if subsampling metadata
#Protist_Order = Protist_Order %>% filter(rowname %in% metadata4$SampleID)#Only needed if subsampling metadata
#Arrange based on identifier
metadata4 = metadata4 %>% arrange(SampleID) %>% dplyr::select(-c("Limitation","Season","Location"))
#Make the identifiers the row names.
#THE ORDER OF THE ROW NAMES MUST BE IDENTICAL BETWEEN THE TWO DATAFRAMES
metadata5 = metadata4 %>% column_to_rownames(var="SampleID")
x4 = x3 %>% column_to_rownames(var="rowname")
#Remove the junk variables
rm(list = c("x", "x2", "x3", "metadata4"))
###
Ordination.model2 <- rda(x4 ~ ., data = metadata5, scaling="species")
summary(Ordination.model2)
#Partitioning of variance:
# Inertia Proportion
#Total 11.182 1.0000
#Conditioned 1.324 0.1184
#Constrained 4.161 0.3721
#Unconstrained 5.697 0.5095
anova.cca(Ordination.model2, step = 1000) #p = 0.21
plot2 <- ordiplot(Ordination.model2, choices=c(1,2))
#These six lines of code make it possible to plot species data in ggplot
sites.long2 <- sites.long(plot2, env.data=metadata5)
head(sites.long2)
species.long2 <- species.long(plot2)
axis.long2 <- axis.long(Ordination.model2, choices=c(1, 2))
#These 4 lines of code add env data into ordination plot.
spec.envfit2 <- envfit(plot2, env=metadata5)
spec.data.envfit2 <- data.frame(r=spec.envfit2$vectors$r, p=spec.envfit2$vectors$pvals, spec.envfit2$vectors$arrows)
spec.data.envfit2$FG <- rownames(spec.data.envfit2)
Env_Arrow <-filter(spec.data.envfit2, p <= .1)
Env_Arrow
spec.envfit <- envfit(plot2, env=x4)
spec.data.envfit <- data.frame(r=spec.envfit$vectors$r, p=spec.envfit$vectors$pvals)
species.long2 <- species.long(plot2, spec.data=spec.data.envfit)
species.long2
#Removes taxa that don't make up at least 50% of community variance, i.e. drive diversity
species.long3 <- species.long2[species.long2$r >= .65, ]
species.long3
w1 = ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axis.long2[1, "label"]) +
ylab(axis.long2[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites.long2,
aes(x=axis1, y=axis2, colour= Site),
size=3, position = position_jitter(.05)) + labs(color = "Site", shape = "Site") +
geom_segment(data=species.long3,
aes(x=0, y=0, xend=axis1*1, yend=axis2*1),
colour="blue", size=0.3, arrow=arrow()) +
geom_text_repel(data=species.long3,
aes(x=axis1*1, y=axis2*1, label=labels),
colour="blue") +
geom_segment(data = Env_Arrow, mapping = aes(x=0, y=0, xend=RDA1*1, yend=RDA2*1),
size = .5, color = "red", arrow=arrow(length=unit(2, "cm")*Env_Arrow$r)) +
geom_text_repel(data = Env_Arrow, aes(x=RDA1, y=RDA2, label= FG), colour = "red")+
#ggforce::geom_mark_ellipse(data=sites.long2, aes(x=axis1, y=axis2, colour=Limitation)) +
theme_minimal() +
coord_fixed(ratio=1)
Ordination.model2 <- rda(x4 ~ ., data = metadata5, scaling="species")
(fit <- envfit(Ordination.model2, metadata5, perm = 999))
metadata6 = metadata5 %>% dplyr::select(-c("Site","Outing"))
x = bioenv(x5, metadata6, method = "spearman", partial = c("Site", "Outing"),
metric = "euclidean",
parallel = 6)
#Photic_Depth DO_mgL OP_mgL Cu_mgL
summary(x)
#####CDMV_RDA#####
#This is just a general genus level RDA
#Transform data and glom as genus
x <- physeq %>% tax_glom(taxrank="Genus", NArm=T) %>% tax_fix() %>%
tax_transform("log10", rank = "Genus")
#Pull otu table as dataframe
x2 = as.data.frame(x@otu_table)
#Arrange based on identifier
x3 = x2 %>% tibble::rownames_to_column() %>% arrange(rowname)
#Subsample metadata, only needed if subsampling ASVs
metadata4 = metadata_RDA %>% filter(SampleID %in% x3$rowname)#Only needed if subsampling metadata
#Protist_Order = Protist_Order %>% filter(rowname %in% metadata4$SampleID)#Only needed if subsampling metadata
#Arrange based on identifier
metadata4 = metadata4 %>% arrange(SampleID) %>% dplyr::select(-c("Limitation","Season","Location"))
#Make the identifiers the row names.
#THE ORDER OF THE ROW NAMES MUST BE IDENTICAL BETWEEN THE TWO DATAFRAMES