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Prokaryote Analysis.R
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Prokaryote Analysis.R
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#Title: Crop Management Impacts the Soy (Glycine max) Microbiome
#Authors: Reid Longley, Zachary A. Noel, Gian Maria Niccolo Benucci, Martin I. Chilvers, Frances Trail, Gregory Bonito
#Journal: Submitted to Frontiers in Microbiology
#Prokaryote Script - Reid Longley
options(scipen = 999)
set.seed(9279)
# load packages ---------------------
install.packages("phyloseq")
install.packages("colorspace")
library(colorspace)
library(stringi)
library(rhdf5)
library(zlibbioc)
library(S4Vectors)
library(phyloseq)
library(Biostrings)
library(yaml)
library(colorspace)
library(ggplot2)
library(indicspecies)
library(vegan)
library(phyloseq)
# create a phyloseq object ----------
prok_otus<- read.delim("otu_bac_total.txt",
row.names=1)
head(prok_otus)
prok_otus_phy <-otu_table(prok_otus,
taxa_are_rows = TRUE)
prok_otus_phy
prok_metadata <-read.delim("total_prokaryote_map.txt",
row.names=1)
prok_metadata
prok_metadata_phy <-sample_data(prok_metadata)
prok_taxonomy<-read.delim("total_bac_tax.txt",
header=TRUE,
row.names=1)
prok_taxonomy
prok_taxonomy_phy <- tax_table(as.matrix(prok_taxonomy))
prok_sequences <- readDNAStringSet("total_otus_bac.fasta", format="fasta", seek.first.rec=TRUE, use.names=TRUE)
prok_sequences
physeq_object <- phyloseq(prok_otus_phy,
prok_metadata_phy,
prok_taxonomy_phy,
prok_sequences)
physeq_object
tax_table(physeq_object)
#removing samples from soil dataset which were noted to not dry properly, and had mold growing in envelope. were analyzed, but only produced a few otus
#non organic samples on this list are being switched with the matching control sample from obj 2
otu_table(physeq_object) <- subset(otu_table(physeq_object),
select = -c(T4R5AR2S,T4R6BR2S,T4R2CR2S,T4R5CR2S,T2R6BR2S))
###formatting taxonomy------------------------
colnames(tax_table(physeq_object)) = c(k = "Kingdom", p = "Phylum", c = "Class", o = "Order", f = "Family", g = "Genus", s = "Species")
tax_table(physeq_object)
tax_table(physeq_object)[, "Kingdom"] <- gsub("k:", "", tax_table(physeq_object)[, "Kingdom"])
tax_table(physeq_object)[, "Phylum"] <- gsub("p:", "", tax_table(physeq_object)[, "Phylum"])
tax_table(physeq_object)[, "Class"] <- gsub("c:", "", tax_table(physeq_object)[, "Class"])
tax_table(physeq_object)[, "Order"] <- gsub("o:", "", tax_table(physeq_object)[, "Order"])
tax_table(physeq_object)[, "Family"] <- gsub("f:", "", tax_table(physeq_object)[, "Family"])
tax_table(physeq_object)[, "Genus"] <- gsub("g:", "", tax_table(physeq_object)[, "Genus"])
tax_table(physeq_object)[, "Species"] <- gsub("s:", "", tax_table(physeq_object)[, "Species"])
tax_table(physeq_object)
#remove chloropast,mitochondria,cyanobacteria
physeq_object <- subset_taxa(physeq_object, Phylum!="Chloroplast")
physeq_object <- subset_taxa(physeq_object, Class!="Chloroplast")
physeq_object <- subset_taxa(physeq_object, Order!="Chloroplast")
physeq_object <- subset_taxa(physeq_object, Family!="Chloroplast")
physeq_object <- subset_taxa(physeq_object, Genus!="Chloroplast")
tax_table(physeq_object)
physeq_object <- subset_taxa(physeq_object, Phylum!="Mitochondria")
physeq_object <- subset_taxa(physeq_object, Class!="Mitochondria")
physeq_object <- subset_taxa(physeq_object, Order!="Mitochondria")
physeq_object <- subset_taxa(physeq_object, Family!="Mitochondria")
physeq_object <- subset_taxa(physeq_object, Genus!="Mitochondria")
tax_table(physeq_object)
###remove contaminants--------------------------------------------------
# must split by sample source so that contaminants are removed from each individual run
# using decontnam package to remove contaminants in negative controls by comparing
# distributions to those found in normal samples
physeq_object_stems <- subset_samples(physeq_object, origin%in%c("stem"))
physeq_object_leaves <- subset_samples(physeq_object,origin%in%c("leaves"))
physeq_object_roots <- subset_samples(physeq_object,origin%in%c("root"))
physeq_object_soil <- subset_samples(physeq_object,origin%in%c("soil"))
sample_data(physeq_object_soil)
library(devtools)
library(processx)
devtools::install_github("benjjneb/decontam", force = TRUE)
library(decontam)
sample_data(physeq_object_leaves)
write.csv(sample_data(physeq_object_leaves),file ="sample_check.csv")
#leaves
# check library size distribution
df_leaves <- as.data.frame(sample_data(physeq_object_leaves)) # Put sample_data into a ggplot-friendly data.frame
df_leaves$LibrarySize_leaves <- sample_sums(physeq_object_leaves)
df_leaves <- df_leaves[order(df_leaves$LibrarySize_leaves),]
df_leaves$Index <- seq(nrow(df_leaves))
write.csv(df_leaves, file = "rank_sums_leaves.csv")
ggplot(data=df_leaves, aes(x=Index, y=LibrarySize_leaves, color=Sample_or_Control)) + geom_point()
# filter by prevelance
sample_data(physeq_object_leaves)$is.neg <- sample_data(physeq_object_leaves)$Sample_or_Control == "Control Sample"
contamdf.prev_leaves <- isContaminant(physeq_object_leaves, method="prevalence", neg="is.neg")
table(contamdf.prev_leaves$contaminant)
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa_leaves <- transform_sample_counts(physeq_object_leaves, function(abund) 1*(abund>0))
ps.pa.neg_leaves <- prune_samples(sample_data(ps.pa_leaves)$Sample_or_Control == "Control Sample", ps.pa_leaves)
ps.pa.pos_leaves <- prune_samples(sample_data(ps.pa_leaves)$Sample_or_Control == "True Sample", ps.pa_leaves)
# Make data.frame of prevalence in positive and negative samples
df.pa_leaves <- data.frame(pa.pos_leaves=taxa_sums(ps.pa.pos_leaves), pa.neg_leaves=taxa_sums(ps.pa.neg_leaves),
contaminant=contamdf.prev_leaves$contaminant)
ggplot(data=df.pa_leaves, aes(x=pa.neg_leaves, y=pa.pos_leaves, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
# remove contaminants
ps.noncontam_leaves <- prune_taxa(!contamdf.prev_leaves$contaminant, physeq_object_leaves)
ps.noncontam_leaves # with contaminants removed
otu_table(ps.noncontam_leaves)
#roots
# check library size distribution
df_roots <- as.data.frame(sample_data(physeq_object_roots)) # Put sample_data into a ggplot-friendly data.frame
df_roots$LibrarySize_roots <- sample_sums(physeq_object_roots)
df_roots <- df_roots[order(df_roots$LibrarySize_roots),]
df_roots$Index <- seq(nrow(df_roots))
write.csv(df_roots, file = "rank_sums_roots.csv")
ggplot(data=df_roots, aes(x=Index, y=LibrarySize_roots, color=Sample_or_Control)) + geom_point()
# filter by prevelance
sample_data(physeq_object_roots)$is.neg <- sample_data(physeq_object_roots)$Sample_or_Control == "Control Sample"
contamdf.prev_roots <- isContaminant(physeq_object_roots, method="prevalence", neg="is.neg")
table(contamdf.prev_roots$contaminant)
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa_roots <- transform_sample_counts(physeq_object_roots, function(abund) 1*(abund>0))
ps.pa.neg_roots <- prune_samples(sample_data(ps.pa_roots)$Sample_or_Control == "Control Sample", ps.pa_roots)
ps.pa.pos_roots <- prune_samples(sample_data(ps.pa_roots)$Sample_or_Control == "True Sample", ps.pa_roots)
# Make data.frame of prevalence in positive and negative samples
df.pa_roots <- data.frame(pa.pos_roots=taxa_sums(ps.pa.pos_roots), pa.neg_roots=taxa_sums(ps.pa.neg_roots),
contaminant=contamdf.prev_roots$contaminant)
ggplot(data=df.pa_roots, aes(x=pa.neg_roots, y=pa.pos_roots, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
# remove contaminants
ps.noncontam_roots <- prune_taxa(!contamdf.prev_roots$contaminant, physeq_object_roots)
# with contaminants removed
otu_table(ps.noncontam_roots)
#stems
# check library size distribution
df_stems <- as.data.frame(sample_data(physeq_object_stems)) # Put sample_data into a ggplot-friendly data.frame
df_stems$LibrarySize_stems <- sample_sums(physeq_object_stems)
df_stems <- df_stems[order(df_stems$LibrarySize_stems),]
df_stems$Index <- seq(nrow(df_stems))
write.csv(df_stems, file = "rank_sums_stems.csv")
ggplot(data=df_stems, aes(x=Index, y=LibrarySize_stems, color=Sample_or_Control)) + geom_point()
# filter by prevelance
sample_data(physeq_object_stems)$is.neg <- sample_data(physeq_object_stems)$Sample_or_Control == "Control Sample"
contamdf.prev_stems <- isContaminant(physeq_object_stems, method="prevalence", neg="is.neg")
table(contamdf.prev_stems$contaminant)
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa_stems <- transform_sample_counts(physeq_object_stems, function(abund) 1*(abund>0))
ps.pa.neg_stems <- prune_samples(sample_data(ps.pa_stems)$Sample_or_Control == "Control Sample", ps.pa_stems)
ps.pa.pos_stems <- prune_samples(sample_data(ps.pa_stems)$Sample_or_Control == "True Sample", ps.pa_stems)
# Make data.frame of prevalence in positive and negative samples
df.pa_stems <- data.frame(pa.pos_stems=taxa_sums(ps.pa.pos_stems), pa.neg_stems=taxa_sums(ps.pa.neg_stems),
contaminant=contamdf.prev_stems$contaminant)
ggplot(data=df.pa_stems, aes(x=pa.neg_stems, y=pa.pos_stems, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
# remove contaminants
ps.noncontam_stems <- prune_taxa(!contamdf.prev_stems$contaminant, physeq_object_stems)
ps.noncontam_stems# with contaminants removed
otu_table(ps.noncontam_stems)
# soil
# check library size distribution
df_soil <- as.data.frame(sample_data(physeq_object_soil)) # Put sample_data into a ggplot-friendly data.frame
df_soil$LibrarySize_soil <- sample_sums(physeq_object_soil)
df_soil <- df_soil[order(df_soil$LibrarySize_soil),]
df_soil$Index <- seq(nrow(df_soil))
write.csv(df_soil, file = "rank_sums_soil.csv")
ggplot(data=df_soil, aes(x=Index, y=LibrarySize_soil, color=Sample_or_Control)) + geom_point()
# filter by prevelance
sample_data(physeq_object_soil)$is.neg <- sample_data(physeq_object_soil)$Sample_or_Control == "Control Sample"
contamdf.prev_soil <- isContaminant(physeq_object_soil, method="prevalence", neg="is.neg")
table(contamdf.prev_soil$contaminant)
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa_soil <- transform_sample_counts(physeq_object_soil, function(abund) 1*(abund>0))
ps.pa.neg_soil <- prune_samples(sample_data(ps.pa_soil)$Sample_or_Control == "Control Sample", ps.pa_soil)
ps.pa.pos_soil <- prune_samples(sample_data(ps.pa_soil)$Sample_or_Control == "True Sample", ps.pa_soil)
# Make data.frame of prevalence in positive and negative samples
df.pa_soil <- data.frame(pa.pos_soil=taxa_sums(ps.pa.pos_soil), pa.neg_soil=taxa_sums(ps.pa.neg_soil),
contaminant=contamdf.prev_soil$contaminant)
ggplot(data=df.pa_soil, aes(x=pa.neg_soil, y=pa.pos_soil, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
# remove contaminants
ps.noncontam_soil <- prune_taxa(!contamdf.prev_soil$contaminant, physeq_object_soil)
ps.noncontam_soil# with contaminants removed
otu_table(ps.noncontam_soil)
sample_data(ps.noncontam_soil)
# remove negative controls from soil
ps.noncontam_soil <- subset_samples(ps.noncontam_soil, experiment%in%c("obj_1","obj_2"))
otu_table(ps.noncontam_soil) <- otu_table(ps.noncontam_soil)[which(rowSums(otu_table(ps.noncontam_soil)) >= 1),]
ps.noncontam_soil
otu_table(ps.noncontam_soil)
sample_data(ps.noncontam_soil)
ps.noncontam_soil
# export soil otu table to check samples
# removing samples with less than 1000 reads, including samples that had less than 1000 read following sampling
write.csv(otu_table(ps.noncontam_soil), file = "filtering_low_soil.csv")
otu_table(ps.noncontam_soil) <- subset(otu_table(ps.noncontam_soil),
select = -c(T1R6CCR3S,T1R6CBR3S,T1R2CBR3S,T2R1CCR3S,T1R5CR2S
))
sample_data(ps.noncontam_soil)
# export roots otu table to check samples
write.csv(otu_table(ps.noncontam_roots), file = "filtering_low_roots.csv")
otu_table(ps.noncontam_roots) <- subset(otu_table(ps.noncontam_roots),
select = -c(T1R5FAR3R, T1R6CR2R, NEG11R, NEG8R, NEG2R, T1R2AV2R, T1R5CAR6R, T1R6CCR4R, T2R1CAR6R, T1R5FCR6R, T1R6CBR6R))
# export leaves otu table to check samples
write.csv(otu_table(ps.noncontam_leaves), file = "filtering_low_leaves.csv")
otu_table(ps.noncontam_leaves) <- subset(otu_table(ps.noncontam_leaves),
select = -c(T2R6FBR3L,NEG17L,T4R1CV2L,T1R1BV2L,NEG9L,NEG16L,Neg14L,T4R1BR6L,T1R5AR2L,NEG10L,NEG12L,NEG5L,T4R5BV2L,NEG15L,Neg2L,NEG3L,Neg8L,NEG13L,NEG11L,T1R1BR2L, T4R2BV2L, T4R1AV2L))
# export stems otu table to check samples
write.csv(otu_table(ps.noncontam_stems), file = "filtering_low_stems.csv")
otu_table(ps.noncontam_stems) <- subset(otu_table(ps.noncontam_stems),
select = -c(NEG10ST,T4R5BR6ST,NEG13ST,T4R1CV2ST,NEG6ST,T4R6AR6ST,T1R1CBR3ST,NEG15ST,NEG14ST,NEG5ST,T1R2AR6ST,T1R2BR6ST,T4R1AR6ST,NEG2ST,NEG3ST,T4R6CR2ST,NEG9ST,NEG11ST,T2R6FCR6ST,T4R1BR6ST,T1R2FBR4ST, T1R5AV2ST))
###for alpha diversity need data which includes singletons etc. will split the current datasets by objective in order to get read distributions for each objective-----------------------
# soil read distributions
ps.noncontam_soil_obj1 <- subset_samples(ps.noncontam_soil, experiment%in%c("obj_1"))
sample_data(ps.noncontam_soil_obj1)
sums_soil_obj1 <- data.frame(colSums(otu_table(ps.noncontam_soil_obj1)))
colnames(sums_soil_obj1) <- "Sample_totalSeqs_soil"
sums_soil_obj1$Sample <- row.names(sums_soil_obj1)
ggplot(sums_soil_obj1, aes(x=Sample_totalSeqs_soil)) +
geom_histogram(binwidth=500, colour="black", fill="white") +
geom_vline(aes(xintercept=mean(Sample_totalSeqs_soil, na.rm=T)), # Ignore NA values for mean
color="red", linetype="dashed", size=1)
# root read distributions
ps.noncontam_roots_obj1 <- subset_samples(ps.noncontam_roots, experiment%in%c("obj_1"))
sums_roots_obj1 <- data.frame(colSums(otu_table(ps.noncontam_roots_obj1)))
colnames(sums_roots_obj1) <- "Sample_totalSeqs_roots"
sums_roots_obj1$Sample <- row.names(sums_roots_obj1)
ggplot(sums_roots_obj1, aes(x=Sample_totalSeqs_roots)) +
geom_histogram(binwidth=500, colour="black", fill="white") +
geom_vline(aes(xintercept=mean(Sample_totalSeqs_roots, na.rm=T)), # Ignore NA values for mean
color="red", linetype="dashed", size=1)
# stem read distributions
ps.noncontam_stems_obj1 <- subset_samples(ps.noncontam_stems, experiment%in%c("obj_1"))
sums_stems_obj1 <- data.frame(colSums(otu_table(ps.noncontam_stems_obj1)))
colnames(sums_stems_obj1) <- "Sample_totalSeqs_stems"
sums_stems_obj1$Sample <- row.names(sums_stems_obj1)
ggplot(sums_stems_obj1, aes(x=Sample_totalSeqs_stems)) +
geom_histogram(binwidth=500, colour="black", fill="white") +
geom_vline(aes(xintercept=mean(Sample_totalSeqs_stems, na.rm=T)), # Ignore NA values for mean
color="red", linetype="dashed", size=1)
# leaves read distributions
ps.noncontam_leaves_obj1 <- subset_samples(ps.noncontam_leaves, experiment%in%c("obj_1"))
sums_leaves_obj1 <- data.frame(colSums(otu_table(ps.noncontam_leaves_obj1)))
colnames(sums_leaves_obj1) <- "Sample_totalSeqs_leaves"
sums_leaves_obj1$Sample <- row.names(sums_leaves_obj1)
ggplot(sums_leaves_obj1, aes(x=Sample_totalSeqs_leaves)) +
geom_histogram(binwidth=500, colour="black", fill="white") +
geom_vline(aes(xintercept=mean(Sample_totalSeqs_leaves, na.rm=T)), # Ignore NA values for mean
color="red", linetype="dashed", size=1)
# alpha diversity plots
### alpha diversity
# making graph following reviewers reccomendations
library("gridExtra")
library("grid")
library("cowplot")
alpha_supp <- ps.noncontam_soil_obj1
otu_prok <- as.data.frame(otu_table(alpha_supp ))
otu_prok
meta_prok <- as.data.frame(sample_data(alpha_supp ))
alpha_prok <- meta_prok
alpha_prok
alpha_prok$readNO <- sample_sums(alpha_supp)
alpha_prok$Observed <- specnumber(otu_prok, MARGIN = 2)
alpha_prok$Shannon <- diversity(otu_prok, index="shannon", MARGIN = 2)
jevenness_prok <- diversityresult(t(otu_prok), method = "each site", index = "Jevenness")
alpha_prok$Jevenness <- jevenness_prok$Jevenness
#alpha_prok <- alpha_prok[order(alpha_prok$ReadNO), ]
alpha_prok
alpha_prok
alpha_prok$alpha_label <- factor(alpha_prok$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
p <- ggplot(alpha_prok, aes(x=alpha_label, y=readNO,color=Growth_Stage)) +
theme_classic()+
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
geom_point(size = 2, shape = 16) +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold"))+
geom_boxplot()
p
label_names <- c(Observed="Richness", Shannon="Shannon")
label_names
ps.noncontam_alpha_soil <- ps.noncontam_soil_obj1
sample_data(ps.noncontam_alpha_soil)$alpha_label <- factor(sample_data(ps.noncontam_alpha_soil)$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
estimate_richness(ps.noncontam_alpha_soil, split = TRUE, measures = NULL)
alpha_soil_prok = plot_richness(ps.noncontam_alpha_soil, x= "alpha_label",
color="Growth_Stage", measures = c( "Observed")) +
ylim(0,9000) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Observed OTUs") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme(axis.text.x = element_text(angle = 90,vjust =1.5)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme_set(theme_classic())+
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_soil_prok)
alpha_soil_prok_shan = plot_richness(ps.noncontam_alpha_soil, x= "alpha_label",
color="Growth_Stage", measures = c( "Shannon")) +
ylim(1,8) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Shannon Diversity") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90,vjust =1.5)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="bottom") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_soil_prok_shan)
# roots
ps.noncontam_alpha_roots <- ps.noncontam_roots_obj1
sample_data(ps.noncontam_alpha_roots)$alpha_label <- factor(sample_data(ps.noncontam_alpha_roots)$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
estimate_richness(ps.noncontam_alpha_roots, split = TRUE, measures = NULL)
alpha_roots_prok = plot_richness(ps.noncontam_alpha_roots, x= "alpha_label",
color="Growth_Stage", measures = c( "Observed")) +
ylim(0,9000)+
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Observed OTUs") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90,vjust =1.5)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="none") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_roots_prok)
alpha_roots_prok_shan = plot_richness(ps.noncontam_alpha_roots, x= "alpha_label",
color="Growth_Stage", measures = c( "Shannon")) +
ylim(1,8)+
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Shannon Diversity") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90,vjust =1.5)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="none") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.title=element_blank())
plot(alpha_roots_prok_shan)
#stems
ps.noncontam_alpha_stems <- ps.noncontam_stems_obj1
sample_data(ps.noncontam_alpha_stems)$alpha_label <- factor(sample_data(ps.noncontam_alpha_stems)$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
estimate_richness(ps.noncontam_alpha_stems, split = TRUE, measures = NULL)
alpha_stems_prok = plot_richness(ps.noncontam_alpha_stems, x= "alpha_label",
color="Growth_Stage", measures = c("Observed")) +
ylim(0, 1400) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Observed OTUs") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="bottom") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_stems_prok)
alpha_stems_prok_shan = plot_richness(ps.noncontam_alpha_stems, x= "alpha_label",
color="Growth_Stage", measures = c("Shannon")) +
ylim(0,6) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Shannon Diversity") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="bottom") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_stems_prok_shan)
# leaves
ps.noncontam_alpha_leaves <- ps.noncontam_leaves_obj1
sample_data(ps.noncontam_alpha_leaves)$alpha_label <- factor(sample_data(ps.noncontam_alpha_leaves)$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
estimate_richness(ps.noncontam_alpha_leaves, split = TRUE, measures = NULL)
alpha_leaves_prok = plot_richness(ps.noncontam_alpha_leaves, x= "alpha_label",
color="Growth_Stage", measures = c( "Observed")) +
ylim(0,1400)+
expand_limits(x = 0, y = 0) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Observed OTUs") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.title = element_text(size = 10, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="bottom") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_leaves_prok)
sample_data(ps.noncontam_alpha_leaves)$alpha_label <- factor(sample_data(ps.noncontam_alpha_leaves)$alpha_label,
level=c("Conventional V2","Conventional R2","Conventional R6","No_Till V2","No_Till R2", "No_Till R6","Organic V2", "Organic R2", "Organic R6"))
estimate_richness(ps.noncontam_alpha_leaves, split = TRUE, measures = NULL)
alpha_leaves_prok_shan = plot_richness(ps.noncontam_alpha_leaves, x= "alpha_label",
color="Growth_Stage", measures = c( "Shannon")) +
ylim(0,6)+
expand_limits(x = 0, y = 0) +
geom_boxplot(outlier.colour="black", outlier.fill = "black") +
geom_point(size = 2, shape = 16) +
labs(title="", x="", y = "Shannon Diversity") +
scale_x_discrete("Sample", labels = c("Conventional V2" = "Conventional V2","No_Till V2" = "No-Till V2","Organic V2" = "Organic V2","Conventional R2" = "Conventional R2", "No_Till R2" = "No-Till R2","Organic R2" = "Organic R2", "Conventional R6" = "Conventional R6","No_Till R6" = "No-Till R6", "Organic R6" = "Organic R6" )) +
scale_colour_manual("Growth_Stage",breaks = c("V2","R2","R6"),
values = c("V2"="orange", "R2"="blue", "R6" = "red")) +
theme(plot.title = element_text(size = 10, face = "bold", hjust = 0)) +
theme_set(theme_classic())+
theme(axis.text.x = element_text(angle = 90)) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
#theme(strip.text.x = element_text(size = 9)) +
theme(legend.position="bottom") +
theme(legend.position="bottom") +
theme(axis.text.x = element_text(angle = 90,vjust =1.5,size = 11, face = "bold")) +
theme(axis.text.y = element_text(size = 11, face = "bold")) +
theme(axis.title = element_text(size = 12, face = "bold")) +
theme(legend.key = element_blank(), legend.title = element_text(size = 12)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position="none") +
theme(legend.title=element_blank())
plot(alpha_leaves_prok_shan)
# combine plots
library(ggpubr)
ggarrange(alpha_soil_prok,alpha_soil_prok_shan,alpha_roots_prok,alpha_roots_prok_shan,alpha_stems_prok,alpha_stems_prok_shan,alpha_leaves_prok,alpha_leaves_prok_shan,
#labels = c("A", "B", "C", "D" ),
widths = c(2.0, 2.0, 2.0, 2.0,2.0,2.0,2.0,2.0),
align = "h", ncol = 4, nrow = 2)
###alpha diversity_table---------------------------------------------------
# make data frames of metadata and otu tables for analyzing alpha diversity
otu_soil_prok <- as.data.frame(otu_table(ps.noncontam_soil_obj1))
meta_soil_prok <- as.data.frame(sample_data(ps.noncontam_soil_obj1))
otu_roots_prok <- as.data.frame(otu_table(ps.noncontam_roots_obj1))
meta_roots_prok <- as.data.frame(sample_data(ps.noncontam_roots_obj1))
otu_stems_prok <- as.data.frame(otu_table(ps.noncontam_stems_obj1))
meta_stems_prok <- as.data.frame(sample_data(ps.noncontam_stems_obj1))
otu_leaves_prok <- as.data.frame(otu_table(ps.noncontam_leaves_obj1))
meta_leaves_prok <- as.data.frame(sample_data(ps.noncontam_leaves_obj1))
meta_soil_prok
install.packages(pkgs=c("BiodiversityR", "vegan",
"Rcmdr", "MASS", "mgcv",
"cluster", "RODBC", "rpart", "effects", "multcomp",
"ellipse", "maptree", "sp", "splancs", "spatial",
"akima", "nnet", "dismo", "raster", "rgdal",
"bootstrap", "PresenceAbsence",
"maxlike", "gbm", "randomForest", "gam", "earth", "mda",
"kernlab", "e1071", "glmnet", "sem", "rgl", "relimp",
"lmtest", "leaps", "Hmisc", "colorspace", "aplpack",
"abind", "XLConnect", "car", "markdown", "knitr",
"geosphere", "maptools", "rgeos", "ENMeval", "red"),
dependencies=c("Depends", "Imports"))
install.packages("rgl")
library(rgl)
library(BiodiversityR)
#soil
alpha_div_soil_prok <- meta_soil_prok
alpha_div_soil_prok
alpha_div_soil_prok$readNO <- sample_sums(ps.noncontam_soil_obj1)
alpha_div_soil_prok$Observed <- specnumber(otu_soil_prok, MARGIN = 2)
alpha_div_soil_prok$Shannon <- diversity(otu_soil_prok, index="shannon", MARGIN = 2)
jevenness_prok <- diversityresult(t(otu_soil_prok), method = "each site", index = "Jevenness")
alpha_div_soil_prok$Jevenness <- jevenness_prok$Jevenness
#alpha_div_soil_prok <- alpha_div_soil_prok[order(alpha_div_soil_prok$ReadNO), ]
alpha_div_soil_prok
# get descriptive stats
install.packages("psych")
library("psych")
describeBy(alpha_div_soil_prok, alpha_div_soil_prok$Indicator_label)
warnings()
#roots
alpha_div_roots_prok <- meta_roots_prok
alpha_div_roots_prok
alpha_div_roots_prok$readNO <- sample_sums(ps.noncontam_roots_obj1)
alpha_div_roots_prok$Observed <- specnumber(otu_roots_prok, MARGIN = 2)
alpha_div_roots_prok$Shannon <- diversity(otu_roots_prok, index="shannon", MARGIN = 2)
jevenness_prok <- diversityresult(t(otu_roots_prok), method = "each site", index = "Jevenness")
alpha_div_roots_prok$Jevenness <- jevenness_prok$Jevenness
#alpha_div_roots_prok <- alpha_div_roots_prok[order(alpha_div_roots_prok$ReadNO), ]
alpha_div_roots_prok
# get descriptive stats
describeBy(alpha_div_roots_prok, alpha_div_roots_prok$Indicator_label)
#stems
alpha_div_stems_prok <- meta_stems_prok
alpha_div_stems_prok
alpha_div_stems_prok$readNO <- sample_sums(ps.noncontam_stems_obj1)
alpha_div_stems_prok$Observed <- specnumber(otu_stems_prok, MARGIN = 2)
alpha_div_stems_prok$Shannon <- diversity(otu_stems_prok, index="shannon", MARGIN = 2)
jevenness_prok <- diversityresult(t(otu_stems_prok), method = "each site", index = "Jevenness")
alpha_div_stems_prok$Jevenness <- jevenness_prok$Jevenness
#alpha_div_stems_prok <- alpha_div_stems_prok[order(alpha_div_stems_prok$ReadNO), ]
alpha_div_stems_prok
# get descriptive stats
describeBy(alpha_div_stems_prok, alpha_div_stems_prok$Indicator_label)
#leaves
alpha_div_leaves_prok <- meta_leaves_prok
alpha_div_leaves_prok
alpha_div_leaves_prok$readNO <- sample_sums(ps.noncontam_leaves_obj1)
alpha_div_leaves_prok$Observed <- specnumber(otu_leaves_prok, MARGIN = 2)
alpha_div_leaves_prok$Shannon <- diversity(otu_leaves_prok, index="shannon", MARGIN = 2)
jevenness_prok <- diversityresult(t(otu_leaves_prok), method = "each site", index = "Jevenness")
alpha_div_leaves_prok$Jevenness <- jevenness_prok$Jevenness
#alpha_div_leaves_prok <- alpha_div_leaves_prok[order(alpha_div_leaves_prok$ReadNO), ]
alpha_div_leaves_prok
# get descriptive stats
describeBy(alpha_div_leaves_prok, alpha_div_leaves_prok$Indicator_label)
#alpha_div_soil_prok_df <- read.csv(alpha_soil_)
# fix dataframes so they are recognized
write.csv(alpha_div_soil_prok, file = "alpha_soil_check.csv")
alpha_div_soil_prok_df <- read.csv("alpha_soil_check.csv")
write.csv(alpha_div_roots_prok, file = "alpha_roots_prok_check.csv")
alpha_div_roots_prok_df <- read.csv("alpha_roots_prok_check.csv")
write.csv(alpha_div_stems_prok, file = "alpha_stems_prok_check.csv")
alpha_div_stems_prok_df <- read.csv("alpha_stems_prok_check.csv")
write.csv(alpha_div_leaves_prok, file = "alpha_leaves_prok_check.csv")
alpha_div_leaves_prok_df <- read.csv("alpha_leaves_prok_check.csv")
#soil
# get significant differences
install.packages("agricolae")
library("agricolae")
library(dplyr)
alpha_div_soil_prok_df_v2 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "V2",]
alpha_div_soil_prok_df_v2
#soil
# get significant differences
install.packages("agricolae")
library("agricolae")
library(dplyr)
alpha_div_soil_prok_df_v2 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "V2",]
alpha_div_soil_prok_df_v2
#soil v2 rich
aov_prok_soil_v2_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_v2)
summary(aov_prok_soil_v2_rich)
HSD.test(aov_prok_soil_v2_rich, "Indicator_label") -> tukeyHSD_prok_soil_v2_rich
tukeyHSD_prok_soil_v2_rich
#isa_below_fungi_Management_fdr$sign$p.value<-p.adjust(isa_below_fungi_Management_fdr$sign$p.value, "fdr")
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_v2)
pairwise.wilcox.test(alpha_div_soil_prok_df_v2$Observed, alpha_div_soil_prok_df_v2$Indicator_label,
p.adjust.method = "fdr")
#soil R2 rich
alpha_div_soil_prok_df_R2 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R2",]
alpha_div_soil_prok_df_R2
aov_prok_soil_R2_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_R2)
summary(aov_prok_soil_R2_rich)
HSD.test(aov_prok_soil_R2_rich, "Indicator_label") -> tukeyHSD_prok_soil_R2_rich
tukeyHSD_prok_soil_R2_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_R2)
pairwise.wilcox.test(alpha_div_soil_prok_df_R2$Observed, alpha_div_soil_prok_df_R2$Indicator_label,
p.adjust.method = "fdr")
# soil R6 rich
alpha_div_soil_prok_df_R6 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R6",]
alpha_div_soil_prok_df_R6
aov_prok_soil_R6_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_R6)
summary(aov_prok_soil_R6_rich)
HSD.test(aov_prok_soil_R6_rich, "Indicator_label") -> tukeyHSD_prok_soil_R6_rich
tukeyHSD_prok_soil_R6_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_R6)
pairwise.wilcox.test(alpha_div_soil_prok_df_R6$Observed, alpha_div_soil_prok_df_R6$Indicator_label,
p.adjust.method = "fdr")
#soil v2 shan
aov_prok_soil_v2_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_v2)
summary(aov_prok_soil_v2_shan)
HSD.test(aov_prok_soil_v2_shan, "Indicator_label") -> tukeyHSD_prok_soil_v2_shan
tukeyHSD_prok_soil_v2_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_v2)
pairwise.wilcox.test(alpha_div_soil_prok_df_v2$Shannon, alpha_div_soil_prok_df_v2$Indicator_label,
p.adjust.method = "fdr")
#soil R2 shan
alpha_div_soil_prok_df_R2 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R2",]
alpha_div_soil_prok_df_R2
aov_prok_soil_R2_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_R2)
summary(aov_prok_soil_R2_shan)
HSD.test(aov_prok_soil_R2_shan, "Indicator_label") -> tukeyHSD_prok_soil_R2_shan
tukeyHSD_prok_soil_R2_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_R2)
pairwise.wilcox.test(alpha_div_soil_prok_df_R2$Shannon, alpha_div_soil_prok_df_R2$Indicator_label,
p.adjust.method = "fdr")
# soil R6 shan
alpha_div_soil_prok_df_R6 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R6",]
alpha_div_soil_prok_df_R6
aov_prok_soil_R6_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_R6)
summary(aov_prok_soil_R6_shan)
HSD.test(aov_prok_soil_R6_shan, "Indicator_label") -> tukeyHSD_prok_soil_R6_shan
tukeyHSD_prok_soil_R6_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_R6)
pairwise.wilcox.test(alpha_div_soil_prok_df_R6$Shannon, alpha_div_soil_prok_df_R6$Indicator_label,
p.adjust.method = "fdr")
#soil v2 even
aov_prok_soil_v2_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_v2)
summary(aov_prok_soil_v2_even)
HSD.test(aov_prok_soil_v2_even, "Indicator_label") -> tukeyHSD_prok_soil_v2_even
tukeyHSD_prok_soil_v2_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_v2)
pairwise.wilcox.test(alpha_div_soil_prok_df_v2$Jevenness, alpha_div_soil_prok_df_v2$Indicator_label,
p.adjust.method = "fdr")
#soil R2 even
alpha_div_soil_prok_df_R2 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R2",]
alpha_div_soil_prok_df_R2
aov_prok_soil_R2_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_R2)
summary(aov_prok_soil_R2_even)
HSD.test(aov_prok_soil_R2_even, "Indicator_label") -> tukeyHSD_prok_soil_R2_even
tukeyHSD_prok_soil_R2_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_R2)
pairwise.wilcox.test(alpha_div_soil_prok_df_R2$Jevenness, alpha_div_soil_prok_df_R2$Indicator_label,
p.adjust.method = "fdr")
# soil R6 even
alpha_div_soil_prok_df_R6 <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Growth_Stage == "R6",]
alpha_div_soil_prok_df_R6
aov_prok_soil_R6_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_R6)
summary(aov_prok_soil_R6_even)
HSD.test(aov_prok_soil_R6_even, "Indicator_label") -> tukeyHSD_prok_soil_R6_even
tukeyHSD_prok_soil_R6_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_R6)
pairwise.wilcox.test(alpha_div_soil_prok_df_R6$Jevenness, alpha_div_soil_prok_df_R6$Indicator_label,
p.adjust.method = "fdr")
#must also split into management systems
alpha_div_soil_prok_df_Conventional <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Management == "Conventional",]
alpha_div_soil_prok_df_Conventional
alpha_div_soil_prok_df_No_Till <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Management == "No-Till",]
alpha_div_soil_prok_df_No_Till
alpha_div_soil_prok_df_Organic <- alpha_div_soil_prok_df[alpha_div_soil_prok_df$Management == "Organic",]
alpha_div_soil_prok_df_Organic
#soil Conventional rich
aov_prok_soil_Conventional_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_Conventional)
summary(aov_prok_soil_Conventional_rich)
HSD.test(aov_prok_soil_Conventional_rich, "Indicator_label") -> tukeyHSD_prok_soil_Conventional_rich
tukeyHSD_prok_soil_Conventional_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_Conventional)
pairwise.wilcox.test(alpha_div_soil_prok_df_Conventional$Observed, alpha_div_soil_prok_df_Conventional$Indicator_label,
p.adjust.method = "fdr")
#soil No_Till rich
aov_prok_soil_No_Till_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_No_Till)
summary(aov_prok_soil_No_Till_rich)
HSD.test(aov_prok_soil_No_Till_rich, "Indicator_label") -> tukeyHSD_prok_soil_No_Till_rich
tukeyHSD_prok_soil_No_Till_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_No_Till)
pairwise.wilcox.test(alpha_div_soil_prok_df_No_Till$Observed, alpha_div_soil_prok_df_No_Till$Indicator_label,
p.adjust.method = "fdr")
# soil Organic rich
alpha_div_soil_prok_df_Organic
aov_prok_soil_Organic_rich <- aov(Observed ~ Indicator_label, data=alpha_div_soil_prok_df_Organic)
summary(aov_prok_soil_Organic_rich)
HSD.test(aov_prok_soil_Organic_rich, "Indicator_label") -> tukeyHSD_prok_soil_Organic_rich
tukeyHSD_prok_soil_Organic_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_soil_prok_df_Organic)
pairwise.wilcox.test(alpha_div_soil_prok_df_Organic$Observed, alpha_div_soil_prok_df_Organic$Indicator_label,
p.adjust.method = "fdr")
#soil Conventional shan
aov_prok_soil_Conventional_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_Conventional)
summary(aov_prok_soil_Conventional_shan)
HSD.test(aov_prok_soil_Conventional_shan, "Indicator_label") -> tukeyHSD_prok_soil_Conventional_shan
tukeyHSD_prok_soil_Conventional_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_Conventional)
pairwise.wilcox.test(alpha_div_soil_prok_df_Conventional$Shannon, alpha_div_soil_prok_df_Conventional$Indicator_label,
p.adjust.method = "fdr")
#soil No_Till shan
aov_prok_soil_No_Till_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_No_Till)
summary(aov_prok_soil_No_Till_shan)
HSD.test(aov_prok_soil_No_Till_shan, "Indicator_label") -> tukeyHSD_prok_soil_No_Till_shan
tukeyHSD_prok_soil_No_Till_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_No_Till)
pairwise.wilcox.test(alpha_div_soil_prok_df_No_Till$Shannon, alpha_div_soil_prok_df_No_Till$Indicator_label,
p.adjust.method = "fdr")
# soil Organic shan
alpha_div_soil_prok_df_Organic
aov_prok_soil_Organic_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_soil_prok_df_Organic)
summary(aov_prok_soil_Organic_shan)
HSD.test(aov_prok_soil_Organic_shan, "Indicator_label") -> tukeyHSD_prok_soil_Organic_shan
tukeyHSD_prok_soil_Organic_shan
kruskal.test(Shannon ~ Indicator_label, data = alpha_div_soil_prok_df_Organic)
pairwise.wilcox.test(alpha_div_soil_prok_df_Organic$Shannon, alpha_div_soil_prok_df_Organic$Indicator_label,
p.adjust.method = "fdr")
#soil Conventional even
aov_prok_soil_Conventional_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_Conventional)
summary(aov_prok_soil_Conventional_even)
HSD.test(aov_prok_soil_Conventional_even, "Indicator_label") -> tukeyHSD_prok_soil_Conventional_even
tukeyHSD_prok_soil_Conventional_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_Conventional)
pairwise.wilcox.test(alpha_div_soil_prok_df_Conventional$Jevenness, alpha_div_soil_prok_df_Conventional$Indicator_label,
p.adjust.method = "fdr")
#soil No_Till even
aov_prok_soil_No_Till_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_No_Till)
summary(aov_prok_soil_No_Till_even)
HSD.test(aov_prok_soil_No_Till_even, "Indicator_label") -> tukeyHSD_prok_soil_No_Till_even
tukeyHSD_prok_soil_No_Till_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_No_Till)
pairwise.wilcox.test(alpha_div_soil_prok_df_No_Till$Jevenness, alpha_div_soil_prok_df_No_Till$Indicator_label,
p.adjust.method = "fdr")
# soil Organic even
alpha_div_soil_prok_df_Organic
aov_prok_soil_Organic_even <- aov(Jevenness ~ Indicator_label, data=alpha_div_soil_prok_df_Organic)
summary(aov_prok_soil_Organic_even)
HSD.test(aov_prok_soil_Organic_even, "Indicator_label") -> tukeyHSD_prok_soil_Organic_even
tukeyHSD_prok_soil_Organic_even
kruskal.test(Jevenness ~ Indicator_label, data = alpha_div_soil_prok_df_Organic)
pairwise.wilcox.test(alpha_div_soil_prok_df_Organic$Jevenness, alpha_div_soil_prok_df_Organic$Indicator_label,
p.adjust.method = "fdr")
alpha_div_roots_prok_df_v2 <- alpha_div_roots_prok_df[alpha_div_roots_prok_df$Growth_Stage == "V2",]
alpha_div_roots_prok_df_v2
#roots v2 rich
aov_prok_roots_v2_rich <- aov(Observed ~ Indicator_label, data=alpha_div_roots_prok_df_v2)
summary(aov_prok_roots_v2_rich)
HSD.test(aov_prok_roots_v2_rich, "Indicator_label") -> tukeyHSD_prok_roots_v2_rich
tukeyHSD_prok_roots_v2_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_roots_prok_df_v2)
pairwise.wilcox.test(alpha_div_roots_prok_df_v2$Observed, alpha_div_roots_prok_df_v2$Indicator_label,
p.adjust.method = "fdr")
#roots R2 rich
alpha_div_roots_prok_df_R2 <- alpha_div_roots_prok_df[alpha_div_roots_prok_df$Growth_Stage == "R2",]
alpha_div_roots_prok_df_R2
aov_prok_roots_R2_rich <- aov(Observed ~ Indicator_label, data=alpha_div_roots_prok_df_R2)
summary(aov_prok_roots_R2_rich)
HSD.test(aov_prok_roots_R2_rich, "Indicator_label") -> tukeyHSD_prok_roots_R2_rich
tukeyHSD_prok_roots_R2_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_roots_prok_df_R2)
pairwise.wilcox.test(alpha_div_roots_prok_df_R2$Observed, alpha_div_roots_prok_df_R2$Indicator_label,
p.adjust.method = "fdr")
# roots R6 rich
alpha_div_roots_prok_df_R6 <- alpha_div_roots_prok_df[alpha_div_roots_prok_df$Growth_Stage == "R6",]
alpha_div_roots_prok_df_R6
aov_prok_roots_R6_rich <- aov(Observed ~ Indicator_label, data=alpha_div_roots_prok_df_R6)
summary(aov_prok_roots_R6_rich)
HSD.test(aov_prok_roots_R6_rich, "Indicator_label") -> tukeyHSD_prok_roots_R6_rich
tukeyHSD_prok_roots_R6_rich
kruskal.test(Observed ~ Indicator_label, data = alpha_div_roots_prok_df_R6)
pairwise.wilcox.test(alpha_div_roots_prok_df_R6$Observed, alpha_div_roots_prok_df_R6$Indicator_label,
p.adjust.method = "fdr")
#roots v2 shan
aov_prok_roots_v2_shan <- aov(Shannon ~ Indicator_label, data=alpha_div_roots_prok_df_v2)
summary(aov_prok_roots_v2_shan)
HSD.test(aov_prok_roots_v2_shan, "Indicator_label") -> tukeyHSD_prok_roots_v2_shan