####china_group_16S analysis, qiime2 version 2017.12
####metadata used for analyses, china_metadata.txt
SampleID	Sample	Type	Treatment	Group	Replicate
BC1	BC1	Bulksoil	Control	BC	1
BC2	BC2	Bulksoil	Control	BC	2
BC3	BC3	Bulksoil	Control	BC	3
BC4	BC4	Bulksoil	Control	BC	4
BC5	BC5	Bulksoil	Control	BC	5
BC6	BC6	Bulksoil	Control	BC	6
BC7	BC7	Bulksoil	Control	BC	7
BC8	BC8	Bulksoil	Control	BC	8
BC9	BC9	Bulksoil	Control	BC	9
BD1	BD1	Bulksoil	Pst	BP	1
BD2	BD2	Bulksoil	Pst	BP	2
BD3	BD3	Bulksoil	Pst	BP	3
BD4	BD4	Bulksoil	Pst	BP	4
BD5	BD5	Bulksoil	Pst	BP	5
BD6	BD6	Bulksoil	Pst	BP	6
BD7	BD7	Bulksoil	Pst	BP	7
BD8	BD8	Bulksoil	Pst	BP	8
BD9	BD9	Bulksoil	Pst	BP	9
C1	C1	Rhizosphere	Control	RC	1
C2	C2	Rhizosphere	Control	RC	2
C3	C3	Rhizosphere	Control	RC	3
C4	C4	Rhizosphere	Control	RC	4
C5	C5	Rhizosphere	Control	RC	5
C6	C6	Rhizosphere	Control	RC	6
C7	C7	Rhizosphere	Control	RC	7
C8	C8	Rhizosphere	Control	RC	8
C9	C9	Rhizosphere	Control	RC	9
D1	D1	Rhizosphere	Pst	RP	1
D2	D2	Rhizosphere	Pst	RP	2
D3	D3	Rhizosphere	Pst	RP	3
D4	D4	Rhizosphere	Pst	RP	4
D5	D5	Rhizosphere	Pst	RP	5
D6	D6	Rhizosphere	Pst	RP	6
D7	D7	Rhizosphere	Pst	RP	7
D8	D8	Rhizosphere	Pst	RP	8
D9	D9	Rhizosphere	Pst	RP	9
#######################################################################DADA2 with single end seqs, paired end loses too many reads
qiime tools import \
  --type 'SampleData[SequencesWithQuality]' \
  --input-path pathogen/single_end_fw \
  --source-format CasavaOneEightSingleLanePerSampleDirFmt \
  --output-path china_demux-single-end.qza
#  
qiime demux summarize \
  --i-data china_demux-single-end.qza \
  --o-visualization china_demux-single-end
#  
###use only fw reads, truncate at 140 to get rid of low quality bases
#
qiime dada2 denoise-single \
  --i-demultiplexed-seqs china_demux-single-end.qza \
  --p-trunc-len 140 \
  --p-trim-left 17 \
  --p-max-ee 2 \
  --p-n-threads 0 \
  --o-table single_feature_table_maxee2 \
  --o-representative-sequences single_rep-seqs_maxee2
#  
#tabulate feature sequences
qiime feature-table tabulate-seqs \
  --i-data single_rep-seqs_maxee2.qza \
  --o-visualization single_rep-seqs_maxee2.qzv
#  
#many features observed in only 1 sample at high count numbers --> possible chimeras missed due to use of only fw reads? 
#
#filter out features that are only present in 1 sample
qiime feature-table filter-features \
  --i-table single_feature_table_maxee2.qza \
  --p-min-samples 2 \
  --o-filtered-table single_feature_table_min2samples
#
#filter sequences from rep-seqs based on single_feature_table_min2samples.qza
qiime feature-table filter-seqs \
  --i-data single_rep-seqs_maxee2.qza \
  --i-table single_feature_table_min2samples.qza \
  --o-filtered-data rep-seqs_min2samples
#
###build towards assigned taxonomies, use silva database, silva_128_release (2017)
#make .qza of the silva database
qiime tools import \
  --input-path 99_otus_16S.fasta \
  --output-path 99_otus_16S.qza \
  --type 'FeatureData[Sequence]'
# 
qiime tools import \
  --type FeatureData[Taxonomy] \
  --source-format HeaderlessTSVTaxonomyFormat \
  --input-path consensus_taxonomy_7_levels.txt \
  --output-path ref-taxonomy.qza
# 
qiime feature-classifier extract-reads \
  --i-sequences 99_otus_16S.qza \
  --p-f-primer CCTACGGGNGGCWGCAG \
  --p-r-primer GACTACHVGGGTATCTAATCC \
  --p-trunc-len 140 \
  --o-reads 99_otus_16S_V3V4_trunc140    ###inspect output for appropriate extraction
#
qiime feature-classifier classify-consensus-vsearch \
  --i-query rep-seqs_min2samples.qza \
  --i-reference-reads 99_otus_16S_V3V4_trunc140.qza \
  --i-reference-taxonomy ref-taxonomy.qza \
  --p-threads 0 \
  --o-classification rep-seqs_min2samples_assigned_taxonomy
################
########prepare files for import into R:phyloseq. note: remove spaces from taxonomy file
################
#####export for use in R:phyloseq	Final_table; Final_table_rep-seqs; Final_table_rep-seqs_assigned_taxonomy
###Done manually, then:
#convert .biom to tsv
biom convert -i single_feature_table_min2samples.biom -o single_feature_table_min2samples.txt --to-tsv
#in excel merge taxonomies with feature table
#convert final table back to .biom
biom convert -i single_feature_table_min2samples.txt -o single_feature_table_min2samples_with_tax.biom --to-hdf5 --table-type="OTU table" --process-obs-metadata taxonomy
################in R
#setwd("C:/Users/3378012/Shared_Folder/China/R")
library("DESeq2")
library("phyloseq")
library("ggplot2")
library("dplyr")
China_phyloseq <- import_biom(BIOMfilename = "single_feature_table_min2samples_with_tax.biom", treefilename = "rep-seqs_min2samples_rooted_phylogeny.nwk", refseqfilename = "rep-seqs_min2samples.fasta")
metadata <- import_qiime_sample_data("china_metadata.txt")
China_phyloseq_complete <- merge_phyloseq(China_phyloseq, metadata)
#change col names in tax_table
#head(tax_table(China_phyloseq_complete))
colnames(tax_table(China_phyloseq_complete)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
###Taxonomy cumulative bar plot, fig 2A
#Taxonomy barblots all replicates shown, got this code somewhere online..
Taxonomies <- China_phyloseq_complete %>%
  tax_glom(taxrank = "Phylum") %>%                     # agglomerate at Class level
  transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
  psmelt() %>%                                         # Melt to long format
  #filter(Abundance > 0.02) %>%                         # Filter out low abundance taxa
  arrange(Phylum)
#Set colors for classes
Phylum_colors <- c(
  "#89C5DA", "#74D944", "#DA5724", "#CE50CA", "#D3D93E", "#C0717C", "#CBD588", "#5F7FC7", 
  "#673770", "#3F4921", "#38333E", "#508578", "#D7C1B1", "#689030", "#AD6F3B", "#CD9BCD", 
  "#D14285", "#6DDE88", "#652926", "#7FDCC0", "#C84248", "#8569D5", "#5E738F", "#D1A33D", 
  "#8A7C64", "#00FF66", "#339933", "#993366"
)
#get rid of rank numbers and other ugly things in taxonomic ranks
#Final_table_replicates_class$Kingdom <- gsub("D_0__","", Final_table_replicates_class$Kingdom)
Taxonomies$Phylum <- gsub("D_1__","", Taxonomies$Phylum)
Taxonomies$Phylum <- gsub("_"," ", Taxonomies$Phylum)
#make abundances per group sum to 1 then make percentages
Taxonomies$Abundance = Taxonomies$Abundance / 9
Taxonomies$Abundance = Taxonomies$Abundance * 100
#########Taxonomy barplots summarized per Group
tiff("china_barplot_3.tiff", width = 9, height = 6, units = 'in', res = 300)
china_barplots <- ggplot(Taxonomies, aes(x = Group, y = Abundance, fill = Phylum)) + 
  geom_bar(stat = "identity") +
  scale_fill_manual(values = Phylum_colors) +
  theme(axis.title.x = element_blank()) +
  theme(legend.text=element_text(size=6)) +
  scale_y_continuous(name = "Abundance (%)")
print(china_barplots)
dev.off()
#################################
#PCoA bray-curtis Fig. 2B
theme_set(theme_bw())
China_phyloseq_complete_depth13000 <- rarefy_even_depth(China_phyloseq_complete, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_phyloseq_complete_depth13000.ord <- ordinate(China_phyloseq_complete_depth13000, "PCoA", "bray")
Bray_depth13000_PCoA_1 = plot_ordination(China_phyloseq_complete_depth13000, China_phyloseq_complete_depth13000.ord, type="samples", label="Sample")
print(Bray_depth13000_PCoA_1)
tiff("Bray_depth13000_PCoA_2.tiff", width = 6, height = 4, units = 'in', res = 300)
Bray_depth13000_PCoA_2 <- Bray_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - Bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(Bray_depth13000_PCoA_2)
dev.off()
#################################, use custom gm_mean, as default analysis cannot handle tables with many zeros
#DESeq2 bulksoil, Fig. 2C
China_bulksoil = subset_samples(China_phyloseq_complete, Type == "Bulksoil")
DEseq2_China_bulksoil = phyloseq_to_deseq2(China_bulksoil, ~Treatment)
gm_mean = function(x, na.rm=TRUE){
  exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(DEseq2_China_bulksoil), 1, gm_mean)
DEseq2_China_bulksoil = estimateSizeFactors(DEseq2_China_bulksoil, geoMeans = geoMeans)
DEseq2_China_bulksoil = DESeq(DEseq2_China_bulksoil, fitType="local")
dispersion_plot_China_bulksoil <- plotDispEsts(DEseq2_China_bulksoil)
resultsNames(DEseq2_China_bulksoil)
resLFC_Bulksoil <- lfcShrink(DEseq2_China_bulksoil, coef="Treatment_Pst_vs_Control")
alpha = 0.05
sigtab_Bulksoil = resLFC_Bulksoil[which(resLFC_Bulksoil$padj < alpha), ]
sigtab_Bulksoil = cbind(as(sigtab_Bulksoil, "data.frame"), as(tax_table(China_bulksoil)[rownames(sigtab_Bulksoil), ], "matrix"))
head(sigtab_Bulksoil)
dim(sigtab_Bulksoil)
write.csv(sigtab_Bulksoil, file = "sigtab_China_Bulksoil.csv")
#get rid of rank numbers in taxonomic ranks
sigtab_Bulksoil$Kingdom <- gsub("D_0__","", sigtab_Bulksoil$Kingdom)
sigtab_Bulksoil$Phylum <- gsub("D_1__","", sigtab_Bulksoil$Phylum)
sigtab_Bulksoil$Class <- gsub("D_2__","", sigtab_Bulksoil$Class)
sigtab_Bulksoil$Order <- gsub("D_3__","", sigtab_Bulksoil$Order)
sigtab_Bulksoil$Family <- gsub("D_4__","", sigtab_Bulksoil$Family)
sigtab_Bulksoil$Genus <- gsub("D_5__","", sigtab_Bulksoil$Genus)
sigtab_Bulksoil$Species <- gsub("D_6__","", sigtab_Bulksoil$Species)
#add taxonomies for pretty plotting
sigtab_Bulksoil$Taxonomy <- c("Tumebacillus sp.", "DA101 soil group sp.", "Roseiflexus sp. #1")
#go for plot
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
    scale_fill_brewer(palette = palname, ...)
}
mylabels <- c(expression(paste("Uncultured ", italic("DA101 soil group "), "sp.")), 
                expression(paste(italic("Roseiflexus "), "sp. #1")), 
                expression(paste("Uncultured ", italic("Tumebacillus "), "sp.")))
tiff("China_bulksoil_DESeq2.tiff", width = 6, height = 3, units = 'in', res = 300)
China_bulksoil_DESeq2 <- ggplot(sigtab_Bulksoil, aes(x=log2FoldChange, y=Taxonomy)) +
  geom_point(aes(fill="Red"), size=3, colour="black", pch=21) + 
  geom_vline(xintercept=0, linetype="dashed", color = "grey", size=1) +
  theme(legend.position="none") +
  scale_y_discrete(name="", labels = mylabels)
  #ggtitle("Differential abundance Pst vs. control - Bulk soil") +
  #theme(plot.title = element_text(hjust = 0.5))
print(China_bulksoil_DESeq2)
dev.off()
###############################
#DESeq2 Rhizosphere, Fig. 2D
China_Rhizosphere = subset_samples(China_phyloseq_complete, Type == "Rhizosphere")
DEseq2_China_Rhizosphere = phyloseq_to_deseq2(China_Rhizosphere, ~Treatment)
gm_mean = function(x, na.rm=TRUE){
  exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(DEseq2_China_Rhizosphere), 1, gm_mean)
DEseq2_China_Rhizosphere = estimateSizeFactors(DEseq2_China_Rhizosphere, geoMeans = geoMeans)
DEseq2_China_Rhizosphere = DESeq(DEseq2_China_Rhizosphere, fitType="local")
dispersion_plot_China_Rhizosphere <- plotDispEsts(DEseq2_China_Rhizosphere)
resultsNames(DEseq2_China_Rhizosphere)
resLFC_Rhizosphere <- lfcShrink(DEseq2_China_Rhizosphere, coef="Treatment_Pst_vs_Control")
alpha = 0.05
sigtab_Rhizosphere = resLFC_Rhizosphere[which(resLFC_Rhizosphere$padj < alpha), ]
sigtab_Rhizosphere = cbind(as(sigtab_Rhizosphere, "data.frame"), as(tax_table(China_Rhizosphere)[rownames(sigtab_Rhizosphere), ], "matrix"))
head(sigtab_Rhizosphere)
dim(sigtab_Rhizosphere)
#write.csv(sigtab_Rhizosphere, file = "sigtab_China_Rhizosphere.csv")
#get rid of rank numbers in taxonomic ranks
sigtab_Rhizosphere$Kingdom <- gsub("D_0__","", sigtab_Rhizosphere$Kingdom)
sigtab_Rhizosphere$Phylum <- gsub("D_1__","", sigtab_Rhizosphere$Phylum)
sigtab_Rhizosphere$Class <- gsub("D_2__","", sigtab_Rhizosphere$Class)
sigtab_Rhizosphere$Order <- gsub("D_3__","", sigtab_Rhizosphere$Order)
sigtab_Rhizosphere$Family <- gsub("D_4__","", sigtab_Rhizosphere$Family)
sigtab_Rhizosphere$Genus <- gsub("D_5__","", sigtab_Rhizosphere$Genus)
sigtab_Rhizosphere$Species <- gsub("D_6__","", sigtab_Rhizosphere$Species)
sigtab_Rhizosphere$Taxonomy <- c("Family I sp.", "Uncultured Anaerolineaceae sp.", "Uncultured Ardenticatenales sp.", "Roseiflexus spp.  #1 and #2", "Roseiflexus spp.  #1 and #2")
#go for plot
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
    scale_fill_brewer(palette = palname, ...)
}
mylabels2 <- c(expression(paste(italic("Family I "), "sp.")), 
                expression(paste(italic("Roseiflexus "), "spp. #2 and #3")), 
				expression(paste("Uncultured ", italic("Anaerolineaceae "), "sp.")),
				expression(paste("Uncultured ", italic("Ardenticatenales "), "sp.")))
tiff("China_Rhizosphere_DESeq2.tiff", width = 6, height = 3, units = 'in', res = 300)
China_Rhizosphere_DESeq2 <- ggplot(sigtab_Rhizosphere, aes(x=log2FoldChange, y=Taxonomy)) +
  geom_point(aes(fill="Red"), size=3, colour="black", pch=21) + 
  geom_vline(xintercept=0, linetype="dashed", color = "grey", size=1) +
  theme(legend.position="none") +
  scale_y_discrete(name="", labels = mylabels2)
  #ggtitle("Differential abundance Pst vs. control - Rhizosphere") +
  #theme(plot.title = element_text(hjust = 0.5))
print(China_Rhizosphere_DESeq2)
dev.off()
####for revision, supplementary figures and statistics, show which ASVs cause separation of BP and BC in absence of fictibacillus and sphingomonas, also redo all other pcoa's, to match format in rest of manuscript
####Fig S3, Statistical outputs were manually added to the figures
#fig S3A, bulksoil pcoa
China_bs = subset_samples(China_phyloseq_complete, Type == "Bulksoil")
China_bs_depth13000 <- rarefy_even_depth(China_bs, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_phyloseq_complete_depth13000.ord <- ordinate(China_bs_depth13000, "PCoA", "bray") #, binary = TRUE)
bray_depth13000_PCoA_1 = plot_ordination(China_phyloseq_complete_depth13000, China_phyloseq_complete_depth13000.ord, type="samples") #, label="Sample")
print(bray_depth13000_PCoA_1)
tiff("bray_depth13000_PCoA_2_bulksoil.tiff", width = 6, height = 4, units = 'in', res = 300)
bray_depth13000_PCoA_2_bulksoil <- bray_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(bray_depth13000_PCoA_2_bulksoil)
dev.off()
#test with permanova (adonis)
df = data.frame(sample_data(China_bs_depth13000))
OTU = t(as(otu_table(China_bs_depth13000),"matrix"))
China_bs_depth13000_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#Fig. S3B, rhizosphere pcoa
China_rhizosphere = subset_samples(China_phyloseq_complete, Type == "Rhizosphere")
China_rhizosphere_depth13000 <- rarefy_even_depth(China_rhizosphere, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_rhizosphere_depth13000.ord <- ordinate(China_rhizosphere_depth13000, "PCoA", "bray")
China_rhizosphere_depth13000_PCoA_1 = plot_ordination(China_rhizosphere_depth13000, China_rhizosphere_depth13000.ord, type="samples") #, label="Sample")
print(China_rhizosphere_depth13000_PCoA_1)
tiff("Bray_depth13000_PCoA_2_rhizosphere.tiff", width = 6, height = 4, units = 'in', res = 300)
Bray_depth13000_PCoA_2_rhizosphere <- China_rhizosphere_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - Bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(Bray_depth13000_PCoA_2_rhizosphere)
dev.off()
#test with permanova (adonis)
df = data.frame(sample_data(China_rhizosphere_depth13000))
OTU = t(as(otu_table(China_rhizosphere_depth13000),"matrix"))
China_rhizosphere_depth13000_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
####Fig S4, Statistical outputs were manually added to the figures
#5cae8eb2f201b91b981b4b69f7bd1acc, fictibacillus asv id
#b2f1e46e34466cda8df8d5014320d4f0, sphingomonas asv id
#asv table without two asvs (ficti and sphingo)
pop_taxa = function(physeq, badTaxa){
  allTaxa = taxa_names(physeq)
  allTaxa <- allTaxa[!(allTaxa %in% badTaxa)]
  return(prune_taxa(allTaxa, physeq))
}
badTaxa = c("5cae8eb2f201b91b981b4b69f7bd1acc", "b2f1e46e34466cda8df8d5014320d4f0")
#from total
China_phyloseq_complete_filter = pop_taxa(China_phyloseq_complete, badTaxa)
#from bulksoil samples only
China_bs_filter = pop_taxa(China_bs, badTaxa)
#from rhizosphere samples only
China_rhizosphere_filter = pop_taxa(China_rhizosphere, badTaxa)
#PCoA's + permanovas without those asv's
#Fig S4C PCoA bray-curtis all samples without fictibacillus and sphingomonas
theme_set(theme_bw())
China_phyloseq_complete_filter_depth13000 <- rarefy_even_depth(China_phyloseq_complete_filter, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_phyloseq_complete_depth13000.ord <- ordinate(China_phyloseq_complete_filter_depth13000, "PCoA", "bray")
Bray_depth13000_PCoA_1 = plot_ordination(China_phyloseq_complete_filter_depth13000, China_phyloseq_complete_depth13000.ord, type="samples") #, label="Sample")
print(Bray_depth13000_PCoA_1)
tiff("Bray_depth13000_PCoA_2_all_noFictiNoSphingo.tiff", width = 6, height = 4, units = 'in', res = 300)
Bray_depth13000_PCoA_2_all_noFictiNoSphingo <- Bray_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - Bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(Bray_depth13000_PCoA_2_all_noFictiNoSphingo)
dev.off()
#Fig S4A, PCoA bray-curtis bulksoil without fictibacillus and sphingomonas, Statistical outputs were manually added to the figures
theme_set(theme_bw())
China_bs_filter_depth13000 <- rarefy_even_depth(China_bs_filter, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_bs_filter_depth13000.ord <- ordinate(China_bs_filter_depth13000, "PCoA", "bray")
Bray_depth13000_PCoA_1 = plot_ordination(China_bs_filter_depth13000, China_bs_filter_depth13000.ord, type="samples") #, label="Sample")
print(Bray_depth13000_PCoA_1)
tiff("Bray_depth13000_PCoA_2_bulksoil_noFictiNoSphingo.tiff", width = 6, height = 4, units = 'in', res = 300)
Bray_depth13000_PCoA_2_bulksoil_noFictiNoSphingo <- Bray_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - Bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(Bray_depth13000_PCoA_2_bulksoil_noFictiNoSphingo)
dev.off()
#test with permanova (adonis)
df = data.frame(sample_data(China_bs_filter_depth13000))
OTU = t(as(otu_table(China_bs_filter_depth13000),"matrix"))
China_bs_filter_depth13000_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#Fig S4B, PCoA bray-curtis bulksoil without fictibacillus and sphingomonas, Statistical outputs were manually added to the figures
China_rhizosphere_filter_depth13000 <- rarefy_even_depth(China_rhizosphere_filter, sample.size = 13000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
China_rhizosphere_filter_depth13000.ord <- ordinate(China_rhizosphere_filter_depth13000, "PCoA", "bray")
China_rhizosphere_filter_depth13000_PCoA_1 = plot_ordination(China_rhizosphere_filter_depth13000, China_rhizosphere_filter_depth13000.ord, type="samples") #, label="Sample")
print(China_rhizosphere_filter_depth13000_PCoA_1)
tiff("Bray_depth13000_PCoA_2_rhizosphere_NoFictiNoSphingo.tiff", width = 6, height = 4, units = 'in', res = 300)
Bray_depth13000_PCoA_2_rhizosphere_NoFictiNoSphingo <- China_rhizosphere_filter_depth13000_PCoA_1 +
  geom_point(aes(fill=Group), size=4, colour="black", pch=21) +
  #ggtitle("PCoA - Bray-Curtis") +
  #theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.title=element_blank())
print(Bray_depth13000_PCoA_2_rhizosphere_NoFictiNoSphingo)
dev.off()
#test
df = data.frame(sample_data(China_rhizosphere_filter_depth13000))
OTU = t(as(otu_table(China_rhizosphere_filter_depth13000),"matrix"))
China_rhizosphere_filter_depth13000_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
########For supplementary table 3, sequentially filtered out top differentially abundant ASVs (based on p-values from DESeq2 analysis), took ranked ASV's from excel file and (really ugly) added to 'pop_taxa' as string
########then tested with adonis (also visualized in pcoa, but these are not in the manuscript, too bulky
#start with top300 lowest p-values
badTaxa = 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China_deseq_filter_1 = pop_taxa(China_bs_filter, badTaxa)
#check sample_sums for min seq depth
#9000 as depth (keeping all samples)
China_deseq_filter_1_depth9000 <- rarefy_even_depth(China_deseq_filter_1, sample.size = 9000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
#test
df = data.frame(sample_data(China_deseq_filter_1_depth9000))
OTU = t(as(otu_table(China_deseq_filter_1_depth9000),"matrix"))
China_deseq_filter_top300_depth9000_PCoA_2_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#top200 lowest p-values
badTaxa = c("53587d28a1692b10bf6b6d932841131f","7fda79c32b678117f4ceaf09f710ad44","9e2867930ec32c2a73ab77767ba6e218","07bf216e89205a0e6a63f952c3996f23","3d29b9843e95e4bfb96ee7d81261e2c6","b9afe7b4ba6bab57e6973b9b4fde2cdd","5cae8eb2f201b91b981b4b69f7bd1acc","f54a856ca7048211cae0ef88e2c5cf3e","ab357d8171859db1c6c2d6c802c4f18f","b8ecc68f0dc5304cb2710913e800cd72","9bb8d5ae48127d618e282e565a31e98f","233a018d2a76674663715e666fbffaae","a7b83e8c54f214a28f13e4eb48615d96","4e461d12e074c85e80b51c23b99cc2ba","5dd4682ed83f7e6392462813ee543824","52228dae641b5fa845c3c571dc85b8af","e73b8dd3b8a41e25a59075485a50d55a","0125fe204188a6842290cbd01f9b07e1","1223635b08a25b27f9ccd81849071ebb","0e8617e8bebfed3db29cf9511babf441","bf59ecb349f9818dd8e716f3ccfa537c","a0253ab4eb0d319960ef4f6a7a7c347f","75a2ae5479e17040b569e45242713596","57eb602b26476bd04b87009bfba3a419","cff2b588efaa9f38cf253502a4fdcd3f","e3344ac12b0b52d246264d06382f1b5d","22601df75c7fe53890dc0aaffcfb9fd5","ef75f2f9675e80eb14546df81154d79a","95132ff808fe2194b6448e4688749521","a5d8a2212b27af08de3f8159459407db","40d7b045e04faa4c926998422c0d9f98","e0b24a4ff65fd55da5192594f7c6f479","f5ad3ec16fa225d98986a2bfd30234bc","639528e19c12773a612c447a86ac2bf5","4f78af21c5ec9ba6274646b4eea7b670","019641f3590bd6a9b8e76c5f1cb002cd","83479353c695cf737e2fd0870943a1e7","2f64b0730946a7cd3c20efbe161bca31","e5dfd52caa44685ae00ace5a2dc74316","61add216e2740e2ebc7dc009d3d924e7","3ae14928e84a28a94e23c875041d2196","167086a8c2b0f1767e0bcb1fd44f4aa4","029bb987c216425a7795a6c9a35d630e","f3b45b41bb193ae01d651ba377a4cc20","3985301d8ea473d54415fd9e91f705b3","201bf3f7c8ce3a6e83b081253d2a2832","a4c180bfc4056c9a07576b726a4a9bd2","b2f1e46e34466cda8df8d5014320d4f0","d7af0932b05ff587484784d5e56b753b","e31fde76c1206d50dee910fb3a16fd96","0b8e34ef5217190301d478ee2a4507b3","50b9541a662555d6394726e21c9a0a40","af268a4a98e6277c59f834280b64b1a4","0e84a243b0b29a7efdffbeb8f0f808aa","2fd50eb71f22e112d117c588f5c01f15","754aca08d84c9cc22140b6753cb6065f","2b44d8c62126bd0eb4091b40256ad3bf","86a599fc58ac541c0bc5a9c57bf63703","45cff4b524957a7c55e1dbe052a3d446","cac1667e816b3352ee4419330c6ad52a","e65384470152850cd40546994a022bcd","ca32e45b01b66af8e8d40cedfa17e622","5165f10b47e3605037145f7e4b0244b0","aeca7ddf0d465be26a291bd33dcfc4a1","55c40a20bdd972b4616827ae1ba8c038","ae11e6ec62ae44a886c6f17f006248a3","9fa1bf9b0a25e6bccc272535a4405768","29a4d410acd6541dcbcbc47b04d195d3","007e13fd50c6a6df3002e777209206e0","798350daedcd6c4bd9d27eb2b0efcf6c","97891bd8874d43841dac437fca997f4b","213c20106d9a9ae195c03ad90d514df1","5bfaacf92b70a3f5269c4af6701c8b04","8896b547bb1f1a2aa4d0c5d5feff5a89","2651e5b5486d90020de16aa8a434c53f","a5896b8bb153e2e0852b707502651cde","bed58983ed7447c2364263b1a93f2f4e","d4eeffa24460ff6b9ead2ea83d96815e","93335c7d22ca6b2d746b03cb6b1b36c1","1edca6d14b015b59a737339f132ed297","6ef296b8c3287fbaa25d2a1ed7212de7","4b359808d73e6ddb8b41cb1ec767cc02","8b1c5e9428e1d7d562e7911f6d67f12c","f05067da08a9b9d560cfc51c57ca1cb7","f2688a13e28084747a1eac1523c395b4","817923a453402df62370fdb2b2961f49","d4b98a2fb991075bc24d84f11abdf052","2e62da366f1778effe3d66995506b659","7c6908005b8524c38629d8206cdf7b7a","fd029182ca5d637f97a56c06fc491f1e","4eb6abeae57d5069414626dbb2f5ae2b","f21a4954ea661a8d45cbc302ba901c80","e7928c132e4f41dea904ea4e2254cd08","c442f4bee31bb10c77f995658e5726d1","4e26762a3ed6484497e52e1f639ee99a","8672ad9406d3ff6308648a1a041896a8","b900d9e5db9a62089d9731768006d96f","3ee85754d7293701cb0c8934d49f8e20","655e015a91f2f93c17eb07bbf15a5ecd","6207f38e45f6d0d2523b6a8132e59bff","01c44761db7b4bfe36c70de699a7bca7","d94f01ea672f9b1974e50dbcbe880f67","6821da822f9aedead47a9a5c9e9c22df","ed055ce4d435d92bac82a099e5a0da6d","b1de14f025cd2145cbb7535567203c7c","c1e69b59a89437730192b64a93781d97","09ee6d54f47135da4d0762af52aeba58","d47932edc43e863facf82340649dc11e","a4d15254a407a465228a9da4ba43871a","5ba38a6d5fb7325a210099e54e8328db","ef7aa722597c95899016cbf200d094bb","b08c58f22f9419f3b002a526a15d5cab","404cebeb2e4ba9d4cc68230452e9f0d2","6f0a3d380093b34b9ba52d0a75e0215c","16094a16ab3a6a47445c54a3bb7a2ee9","c3390798b0469321c4867db4d6f1ad6c","5c61a8b1c2c358332d60f84f777713d1","fbc76e103c328384cee07144334cc45f","e9c9e1266dcc172d1c993b246a3e4ccb","78d02c168b0801847032892f49d7ea72","f9d5b8fce62a6eef00d1496b59acdb8d","a5039ad5769736eb7599842f42ab399d","45fdf375c4a964fbf5eb965f097485f8","d80b417cf2fe7b04b404d5813098b142","e75022bea113048a1bad2cec5846905a","4e04febd3a3983247eb88dd815aa33b1","9141c1de3ccb2918552c1c5727eed176","8c7ec0b8dd47f7614880b8ff885d4cbd","4419fa67aaa6c9b5f75de54d9365025f","eebc937c9fd06ca0fe0b053248e2c74e","4781794c81d5356398b635b4e00beb6a","614277f8393e91e5b09870828898d070","c5416eb0fc0b034ca45b2de9cebb1cdc","9805a194d437750496f60fb1d797c332","9925b633cf5955b4670dc8a35165ba36","2e3a740cd22e6cf2f87ee82f42af1441","b73545121ed32b1b7aa887e22a771a43","0f293a3aca2d198102905083698334f4","58a940b4a99e9ec71c7fa567afc0c0d4","c62d9cc1d8647a0ccef99419966fa87e","f907c54dc07470732ac24e6728ef6743","ec6d082ce319677281371a57651ebde7","925b1e36c06ebe13f2abfc0a3d1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China_deseq_filter_2 = pop_taxa(China_bs_filter, badTaxa)
#check sample_sums for min seq depth
#9000 as depth (keeping all samples)
China_deseq_filter_2_depth9000 <- rarefy_even_depth(China_deseq_filter_2, sample.size = 9000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
#test
df = data.frame(sample_data(China_deseq_filter_2_depth9000))
OTU = t(as(otu_table(China_deseq_filter_2_depth9000),"matrix"))
China_deseq_filter_top200_depth9000_PCoA_2_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#top100 lowest p-values
badTaxa = c("53587d28a1692b10bf6b6d932841131f","7fda79c32b678117f4ceaf09f710ad44","9e2867930ec32c2a73ab77767ba6e218","07bf216e89205a0e6a63f952c3996f23","3d29b9843e95e4bfb96ee7d81261e2c6","b9afe7b4ba6bab57e6973b9b4fde2cdd","5cae8eb2f201b91b981b4b69f7bd1acc","f54a856ca7048211cae0ef88e2c5cf3e","ab357d8171859db1c6c2d6c802c4f18f","b8ecc68f0dc5304cb2710913e800cd72","9bb8d5ae48127d618e282e565a31e98f","233a018d2a76674663715e666fbffaae","a7b83e8c54f214a28f13e4eb48615d96","4e461d12e074c85e80b51c23b99cc2ba","5dd4682ed83f7e6392462813ee543824","52228dae641b5fa845c3c571dc85b8af","e73b8dd3b8a41e25a59075485a50d55a","0125fe204188a6842290cbd01f9b07e1","1223635b08a25b27f9ccd81849071ebb","0e8617e8bebfed3db29cf9511babf441","bf59ecb349f9818dd8e716f3ccfa537c","a0253ab4eb0d319960ef4f6a7a7c347f","75a2ae5479e17040b569e45242713596","57eb602b26476bd04b87009bfba3a419","cff2b588efaa9f38cf253502a4fdcd3f","e3344ac12b0b52d246264d06382f1b5d","22601df75c7fe53890dc0aaffcfb9fd5","ef75f2f9675e80eb14546df81154d79a","95132ff808fe2194b6448e4688749521","a5d8a2212b27af08de3f8159459407db","40d7b045e04faa4c926998422c0d9f98","e0b24a4ff65fd55da5192594f7c6f479","f5ad3ec16fa225d98986a2bfd30234bc","639528e19c12773a612c447a86ac2bf5","4f78af21c5ec9ba6274646b4eea7b670","019641f3590bd6a9b8e76c5f1cb002cd","83479353c695cf737e2fd0870943a1e7","2f64b0730946a7cd3c20efbe161bca31","e5dfd52caa44685ae00ace5a2dc74316","61add216e2740e2ebc7dc009d3d924e7","3ae14928e84a28a94e23c875041d2196","167086a8c2b0f1767e0bcb1fd44f4aa4","029bb987c216425a7795a6c9a35d630e","f3b45b41bb193ae01d651ba377a4cc20","3985301d8ea473d54415fd9e91f705b3","201bf3f7c8ce3a6e83b081253d2a2832","a4c180bfc4056c9a07576b726a4a9bd2","b2f1e46e34466cda8df8d5014320d4f0","d7af0932b05ff587484784d5e56b753b","e31fde76c1206d50dee910fb3a16fd96","0b8e34ef5217190301d478ee2a4507b3","50b9541a662555d6394726e21c9a0a40","af268a4a98e6277c59f834280b64b1a4","0e84a243b0b29a7efdffbeb8f0f808aa","2fd50eb71f22e112d117c588f5c01f15","754aca08d84c9cc22140b6753cb6065f","2b44d8c62126bd0eb4091b40256ad3bf","86a599fc58ac541c0bc5a9c57bf63703","45cff4b524957a7c55e1dbe052a3d446","cac1667e816b3352ee4419330c6ad52a","e65384470152850cd40546994a022bcd","ca32e45b01b66af8e8d40cedfa17e622","5165f10b47e3605037145f7e4b0244b0","aeca7ddf0d465be26a291bd33dcfc4a1","55c40a20bdd972b4616827ae1ba8c038","ae11e6ec62ae44a886c6f17f006248a3","9fa1bf9b0a25e6bccc272535a4405768","29a4d410acd6541dcbcbc47b04d195d3","007e13fd50c6a6df3002e777209206e0","798350daedcd6c4bd9d27eb2b0efcf6c","97891bd8874d43841dac437fca997f4b","213c20106d9a9ae195c03ad90d514df1","5bfaacf92b70a3f5269c4af6701c8b04","8896b547bb1f1a2aa4d0c5d5feff5a89","2651e5b5486d90020de16aa8a434c53f","a5896b8bb153e2e0852b707502651cde","bed58983ed7447c2364263b1a93f2f4e","d4eeffa24460ff6b9ead2ea83d96815e","93335c7d22ca6b2d746b03cb6b1b36c1","1edca6d14b015b59a737339f132ed297","6ef296b8c3287fbaa25d2a1ed7212de7","4b359808d73e6ddb8b41cb1ec767cc02","8b1c5e9428e1d7d562e7911f6d67f12c","f05067da08a9b9d560cfc51c57ca1cb7","f2688a13e28084747a1eac1523c395b4","817923a453402df62370fdb2b2961f49","d4b98a2fb991075bc24d84f11abdf052","2e62da366f1778effe3d66995506b659","7c6908005b8524c38629d8206cdf7b7a","fd029182ca5d637f97a56c06fc491f1e","4eb6abeae57d5069414626dbb2f5ae2b","f21a4954ea661a8d45cbc302ba901c80","e7928c132e4f41dea904ea4e2254cd08","c442f4bee31bb10c77f995658e5726d1","4e26762a3ed6484497e52e1f639ee99a","8672ad9406d3ff6308648a1a041896a8","b900d9e5db9a62089d9731768006d96f","3ee85754d7293701cb0c8934d49f8e20","655e015a91f2f93c17eb07bbf15a5ecd","6207f38e45f6d0d2523b6a8132e59bff")
China_deseq_filter_3 = pop_taxa(China_bs_filter, badTaxa)
#check sample_sums for min seq depth
#9000 as depth (keeping all samples)
China_deseq_filter_3_depth9000 <- rarefy_even_depth(China_deseq_filter_3, sample.size = 9000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
#test
df = data.frame(sample_data(China_deseq_filter_3_depth9000))
OTU = t(as(otu_table(China_deseq_filter_3_depth9000),"matrix"))
China_deseq_filter_top100_depth9000_PCoA_2_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#top50 lowest p-values
badTaxa = c("53587d28a1692b10bf6b6d932841131f","7fda79c32b678117f4ceaf09f710ad44","9e2867930ec32c2a73ab77767ba6e218","07bf216e89205a0e6a63f952c3996f23","3d29b9843e95e4bfb96ee7d81261e2c6","b9afe7b4ba6bab57e6973b9b4fde2cdd","5cae8eb2f201b91b981b4b69f7bd1acc","f54a856ca7048211cae0ef88e2c5cf3e","ab357d8171859db1c6c2d6c802c4f18f","b8ecc68f0dc5304cb2710913e800cd72","9bb8d5ae48127d618e282e565a31e98f","233a018d2a76674663715e666fbffaae","a7b83e8c54f214a28f13e4eb48615d96","4e461d12e074c85e80b51c23b99cc2ba","5dd4682ed83f7e6392462813ee543824","52228dae641b5fa845c3c571dc85b8af","e73b8dd3b8a41e25a59075485a50d55a","0125fe204188a6842290cbd01f9b07e1","1223635b08a25b27f9ccd81849071ebb","0e8617e8bebfed3db29cf9511babf441","bf59ecb349f9818dd8e716f3ccfa537c","a0253ab4eb0d319960ef4f6a7a7c347f","75a2ae5479e17040b569e45242713596","57eb602b26476bd04b87009bfba3a419","cff2b588efaa9f38cf253502a4fdcd3f","e3344ac12b0b52d246264d06382f1b5d","22601df75c7fe53890dc0aaffcfb9fd5","ef75f2f9675e80eb14546df81154d79a","95132ff808fe2194b6448e4688749521","a5d8a2212b27af08de3f8159459407db","40d7b045e04faa4c926998422c0d9f98","e0b24a4ff65fd55da5192594f7c6f479","f5ad3ec16fa225d98986a2bfd30234bc","639528e19c12773a612c447a86ac2bf5","4f78af21c5ec9ba6274646b4eea7b670","019641f3590bd6a9b8e76c5f1cb002cd","83479353c695cf737e2fd0870943a1e7","2f64b0730946a7cd3c20efbe161bca31","e5dfd52caa44685ae00ace5a2dc74316","61add216e2740e2ebc7dc009d3d924e7","3ae14928e84a28a94e23c875041d2196","167086a8c2b0f1767e0bcb1fd44f4aa4","029bb987c216425a7795a6c9a35d630e","f3b45b41bb193ae01d651ba377a4cc20","3985301d8ea473d54415fd9e91f705b3","201bf3f7c8ce3a6e83b081253d2a2832","a4c180bfc4056c9a07576b726a4a9bd2","b2f1e46e34466cda8df8d5014320d4f0","d7af0932b05ff587484784d5e56b753b","e31fde76c1206d50dee910fb3a16fd96")
China_deseq_filter_4 = pop_taxa(China_bs_filter, badTaxa)
#check sample_sums for min seq depth
#9000 as depth (keeping all samples)
China_deseq_filter_4_depth9000 <- rarefy_even_depth(China_deseq_filter_4, sample.size = 9000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
#test
df = data.frame(sample_data(China_deseq_filter_4_depth9000))
OTU = t(as(otu_table(China_deseq_filter_4_depth9000),"matrix"))
China_deseq_filter_top50_depth9000_PCoA_2_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")
#top400 lowest p-values
badTaxa = 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China_deseq_filter_5 = pop_taxa(China_bs_filter, badTaxa)
#check sample_sums for min seq depth
#8000 as depth (keeping all samples)
China_deseq_filter_5_depth8000 <- rarefy_even_depth(China_deseq_filter_5, sample.size = 8000, rngseed = 711, replace = FALSE, trimOTUs = TRUE, verbose = TRUE)
#test
df = data.frame(sample_data(China_deseq_filter_5_depth8000))
OTU = t(as(otu_table(China_deseq_filter_5_depth8000),"matrix"))
China_deseq_filter_top400_depth8000_PCoA_2_adonis <- adonis(OTU ~ Treatment, data = df, method = "bray")