ITS2_soil_FA19_ajo
R Packages
The following code uses several packages including vegan for community ecology, tidyr and plyr for organizing data tables and manipulating strings of text, and ggplot for plotting data.
#In this chunk I load all of the requisite packages.
library(vegan)
library(ggplot2)
library(ggpubr)
library(tidyr)
library(plyr)
library(data.table)
library(RColorBrewer)
library(ape)
library(car)
library(Hmisc)
library(corrplot)
library(plotly)## Warning: package 'plotly' was built under R version 3.6.2Data Import and Subsetting
Data Import
Below ITS data from bulk soils collected from 47 Juglans nigra trees are imported into R. This includes a rarefied OTU table from mothur, the sample metadata which indicates the state from which the sample was collected and clone ID, and the taxonomic classifications for each OTU. Samples were rarefied to 25,000 sequences in mothur prior to import, resulting in the loss of two samples from WA. Taxonomic assignments were made in mothur using the UNITE database.
# Below I am importing the rarefied OTU table for ITS2 from the caulosphere. This table was rarefied in mothur prior to importing into R to 25000 sequences per sample.
its2.soil.rareotu.wsingles<-read.table("Mothur_output/soilits2otutable.rarefied_jnigra17_fa19_gw.shared", header=TRUE, sep="\t", row.names=2)
#Next I import the metadata for the particular habitat of interest. In this case I am working with soil. I will use this metadata to subset the larger OTU file above.
its2.soil.met<-read.table("Mothur_output/soilits2metadata_jnigra17_su19_ajo.txt",header=TRUE,sep="\t",fill=TRUE,strip.white=TRUE)
#Next I import the taxonomy file.
its2.taxonomy.soil<-read.table("Mothur_output/soilits2taxonomy_jnigra17_fa19_gw.cons.taxonomy",header=TRUE,sep="\t",fill=TRUE,strip.white=TRUE,row.names=1)
its2.soil.names<-its2.soil.met$GroupData Subsetting
Below subset the OTU table to remove Mothur inserted metadata which indicates the percent identity threshold and the number of OTUs. I then remove singleton OTUs, OTUs that have only one 1 sequence following rarefaction.
#Below I remove the mothur associated metadata present in the first two columns to generate a matrix that consists of only OTU raw counts.
its2.soil.rareotu<-as.data.frame(its2.soil.rareotu.wsingles[,3:10579])
#I am now removing singleton OTUs (OTUs with only 1 sequence following rarefaction). This is to limit the effects of rare species on our distance matrices used during ordination.
its2.soil.rareotu.nosingletons<-as.data.frame(its2.soil.rareotu[,colSums(its2.soil.rareotu)>1])
#The following line is for saving the rarefied OTU table with singletons removed. This file is later reloaded to be used in downstream analyses. This is done to maintain consistency of results because rarefying generates slightly different results each time.
#write.table(its2.soil.rareotu.nosingletons, "/Volumes/AaronOnufrakMac/researchprojects_2019.2022_ajo/j.nigra_microbiome_2017_ajo_gmw/soilits2statistics_jnigra17_su19_ajo/soilits2otutable.rarefied.nosingleton_jnigra17_fa19_gw_ajo.shared",sep="\t", row.names=TRUE)
#I then subset the metadata to remove sample names from the metadata that were removed following rarefaction.
its2.soil.met.rare<-subset(its2.soil.met,its2.soil.met$Group%in%labels(its2.soil.rareotu.wsingles)[[1]])Good’s Coverage
Relative Abundance Charts
To visualize differences in the relative abundance of different taxa I reorganize the OTU table and taxonomy strings to create relative abundance stacked bar charts. Below, stacked bar charts are made for phylum, class, and order levels.
Taxonomy Subsetting
To begin, the taxonomy file is subsetted to include only taxonomy information for OTUs present in the rarefied OTU table with singletons removed. The OTU table is first transposed so that OTU IDs are the row names and sample names are the column names. Then the taxonomic assignments are added to the OTU table. Taxonomic strings are then split by semi-colon into individual columns by Phylum, Class, Order, Family, Genus, and Species.
#Using the rarefied OTU tables I can generate relative abundance charts for the different taxonomic levels.To begin I first reformat the OTU table so that I can do some filtering and add taxonomy information.
its2.soil.rareotu.nosingles<-read.table("Mothur_output/soilits2otutable.rarefied.nosingleton_jnigra17_fa19_gw_ajo.shared", header=TRUE, sep="\t")
#I now transpose my OTU table so that I can begin to add in taxonomy information.
t.its2.soil.rareotu.nosingles<-as.data.frame(t(its2.soil.rareotu.nosingles))
#The below function is asking specifically for the OTU Isoil. These are the row names. I will use these to subset the taxonomy file.
t.its2.soil.rareotu.nosingleslabs<-labels(t.its2.soil.rareotu.nosingles)
its2.soil.taxrare<-subset(its2.taxonomy.soil, rownames(its2.taxonomy.soil) %in% t.its2.soil.rareotu.nosingleslabs[[1]])
#Now I am subsetting the new taxonomy file to include only the taxonomy information stored in column 2.
its2.soil.taxrareinfo<-(its2.soil.taxrare[,2])
#Below I am using the separate function from tidyr to split the taxonomy strings into columns by semi-colon so that I can rename the OTUs to give them more meaning for downstream analyses.
library(tidyr)
#The UNITE database only provides classifications up to species level, thus, new columns are created up to species level.
its2.soil.taxonomylabs<-c("kingdom","phylum","class","order","family","genus","species")
#Below I create a new data frame with a column containing taxonomy information.
t.its2.soil.rareotutabtax<-data.frame(taxonomy=its2.soil.taxrareinfo,t.its2.soil.rareotu.nosingles)
#I then separate the taxonomy strings into new columns labeled with the taxonomy labels saved in the taxonomylabs object. Strings are being separated based on the presence of semi-colons.
t.its2.soil.rareotutabtaxsep<-separate(t.its2.soil.rareotutabtax,into=its2.soil.taxonomylabs,col=taxonomy,sep=";")Relative Abundance OTU Table
I then create a relative abundance OTU table. This table contains taxonomic information and can be searched to look at specific OTUs more easily.
#Using the rarefied OTU tables I can generate relative abundance charts for the different taxonomic levels.To begin I first reformat the OTU table so that I can do some filtering and add taxonomy information.
its2.soil.rareotu.nosingles<-read.table("Mothur_output/soilits2otutable.rarefied.nosingleton_jnigra17_fa19_gw_ajo.shared", header=TRUE, sep="\t")
its2.soil.rareotu.nosingles.table<-decostand(its2.soil.rareotu.nosingles,method="total")
#I now transpose my OTU table so that I can begin to add in taxonomy information.
t.its2.soil.rareotu.nosingles.table<-as.data.frame(t(its2.soil.rareotu.nosingles.table))
#The below function is asking specifically for the OTU Isoil. These are the row names. I will use these to subset the taxonomy file.
t.its2.soil.rareotu.nosingleslabs<-labels(t.its2.soil.rareotu.nosingles.table)
its2.soil.taxrare<-subset(its2.taxonomy.soil, rownames(its2.taxonomy.soil) %in% t.its2.soil.rareotu.nosingleslabs[[1]])
#Now I am subsetting the new taxonomy file to include only the taxonomy information stored in column 2.
its2.soil.taxrareinfo<-(its2.soil.taxrare[,2])
#Below I am using the separate function from tidyr to split the taxonomy strings into columns by semi-colon so that I can rename the OTUs to give them more meaning for downstream analyses.
library(tidyr)
#The UNITE database only provides classifications up to species level, thus, new columns are created up to species level.
its2.soil.taxonomylabs<-c("kingdom","phylum","class","order","family","genus","species")
#Below I create a new data frame with a column containing taxonomy information.
t.its2.soil.rareotutabtax.table<-data.frame(taxonomy=its2.soil.taxrareinfo,t.its2.soil.rareotu.nosingles.table)
#I then separate the taxonomy strings into new columns labeled with the taxonomy labels saved in the taxonomylabs object. Strings are being separated based on the presence of semi-colons.
t.its2.soil.rareotutabtaxsep.table<-separate(t.its2.soil.rareotutabtax.table,into=its2.soil.taxonomylabs,col=taxonomy,sep=";")
library(DT)
datatable(t.its2.soil.rareotutabtaxsep.table,fillContainer = T, height = "500px")Phylum Relative Abundance Chart
Below I generate a phylum relative abundance plot. I first extract the phylum information from the OTU table with taxonomy information. I then clean the taxa names to remove assignment confidence values and the p__ designator that precedes phylum assignment. OTU raw abundance is then summed by phylum and state, phyla that represent less than 1% of the total community are grouped into other, and relative abundance is calculated. Stacked bar charts are made using the melt function and ggplot.
#Below I create a data frame that consists of phylum assignment and the rarefied OTU table.
t.its2.soil.raretabphy<-data.frame(phylum=t.its2.soil.rareotutabtaxsep$phylum,t.its2.soil.rareotu.nosingles)
#I then remove the assignment confidence values contained in parentheses found in each taxonomic string using the sub function and the phylum "p__" designator at the start of each taxon.
t.its2.soil.raretabphy$phylum<-sub("\\(.*)","",x=t.its2.soil.raretabphy$phylum)
t.its2.soil.raretabphy$phylum<-sub("p__","",x=t.its2.soil.raretabphy$phylum)
#The following code is for the generation of phylum relative abundance stacked bar charts. I first sum each OTU by the phylum it belongs to.
library(plyr)
t.its2.soil.rareotutabphy<-as.data.frame(ddply(t.its2.soil.raretabphy, .(phylum),colwise(sum)))
rownames(t.its2.soil.rareotutabphy)<-make.names(t.its2.soil.rareotutabphy$phylum)
t.its2.soil.rareotutabphy<-t.its2.soil.rareotutabphy[,2:46]
#I then transpose the data frame so that sample names are now row names and column names are OTU names.
its2.soil.rareotutabphy<-as.data.frame(t(t.its2.soil.rareotutabphy))
its2.soil.rareotutabphy.state<-data.frame(state=its2.soil.met.rare$State,its2.soil.rareotutabphy)
#I then sum each OTU by state and subset to exclude sample names. This is because the following functions only work on matrices containing numeric data.
its2.soil.rareotutabphy.statesum<-as.data.frame(ddply(its2.soil.rareotutabphy.state, .(state),colwise(sum)))
its2.soil.phylumcols<-its2.soil.rareotutabphy.statesum[,2:16]
#I am now creating an other category. Other includes those OTUs belonging to a phylum that comprises less than 1% of the total community. This new object is referred to as phyothers.
its2.soil.phyoth<-its2.soil.phylumcols[,colSums(its2.soil.phylumcols)/sum(its2.soil.phylumcols)<=0.01]
its2.soil.phyothers<-rowSums(its2.soil.phyoth)
#I then create an object that contains all of the phyla that comprise more than 1% of the total community.
its2.soil.phyreg<-its2.soil.phylumcols[,colSums(its2.soil.phylumcols)/sum(its2.soil.phylumcols)>0.01]
#I then create a new dataframe containing the state information, the other column, and the remaining phyla.
its2.soil.phytot2<-data.frame(state=its2.soil.rareotutabphy.statesum$state,its2.soil.phyreg,Other=its2.soil.phyothers)
#These values are then converted to relative abundance using the decostand function from vegan.
library(vegan)
its2.soil.phyrelabund<-decostand(its2.soil.phytot2[,2:8],method="total")
its2.soil.phyrelabund<-data.frame(state=its2.soil.rareotutabphy.statesum$state,its2.soil.phyrelabund)
#Below I use the melt function from the data.table package to reformat the data into stacked bar graph format.
library(data.table)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
its2.soil.phyrelabundmelt<-melt(its2.soil.phyrelabund, id.vars="state", variable.name="Phylum")
its2.soil.colors.n.phylum <- length(unique(its2.soil.phyrelabundmelt[,'Phylum']))
#Below I generate the relative abundance bar charts in ggplot
its2.soil.phyrel<-ggplot(its2.soil.phyrelabundmelt, aes(x=state, y=value, fill=Phylum))+
geom_bar(stat="identity", show.legend=TRUE, color="black")+
scale_fill_manual(values=colorRampPalette(brewer.pal(9, 'Set1'))(its2.soil.colors.n.phylum)) +
xlab("State") +
ylab("Relative Abundance") +
theme(panel.border = element_blank(),panel.background=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size = 14), axis.ticks.x = element_blank(), axis.text.y = element_text(size = 14), axis.line.y.left = element_line(), axis.title.y = element_text(size = 14))+
scale_y_continuous(breaks=c(0,0.25,0.50,0.75,1),limits=c(0,1.05))+
geom_text(data=NULL,aes(x=0.51, y=1.05,label="Soil: ITS2"),hjust=0,colour="black")
ggplotly(its2.soil.phyrel, tooltip="Phylum")Class Relative Abundance Chart
Below I generate a class relative abundance plot. I first extract the class information from the OTU table that contains taxonomy information. I then clean the taxa names to remove assignment confidence values, the p__ designator that precedes phylum assignment, and c__ designator that precedes class assignment. OTU raw abundance is then summed by class and state, classes that represent less than 1% of the total community are grouped into other, and relative abundance is calculated. Stacked bar charts are made using the melt function and ggplot.
#The following chunk creates a relative abundance bar chart at the class level.
library(plyr)
t.its2.soil.raretabcls<-data.frame(class=t.its2.soil.rareotutabtaxsep$class,t.its2.soil.rareotu.nosingles)
#What I am doing below is cleaning up the class names. These include assignment confidence levels, p__ and c__
t.its2.soil.raretabcls$class<-sub("\\(.*)","",x=t.its2.soil.raretabcls$class)
t.its2.soil.raretabcls$class<-sub("c__","",x=t.its2.soil.raretabcls$class)
t.its2.soil.raretabcls$class<-sub("p__","",x=t.its2.soil.raretabcls$class)
#I then sum the OTUs by class and make the row names the class name.
t.its2.soil.rareotutabcls<-as.data.frame(ddply(t.its2.soil.raretabcls, .(class),colwise(sum)))
rownames(t.its2.soil.rareotutabcls)<-make.names(t.its2.soil.rareotutabcls$class)
t.its2.soil.rareotutabcls<-t.its2.soil.rareotutabcls[,2:46]
#I then transpose the data frame and sum each class by state.
its2.soil.rareotutabcls<-as.data.frame(t(t.its2.soil.rareotutabcls))
its2.soil.rareotutabcls.state<-data.frame(state=its2.soil.met.rare$State,its2.soil.rareotutabcls)
its2.soil.rareotutabcls.statesum<-as.data.frame(ddply(its2.soil.rareotutabcls.state, .(state),colwise(sum)))
#At the class level, UNITE may classify things as phylum_unclassified. These don't provide a lot of information and thus are grouped into a new group called unknown.
its2.soil.clsunknown<-its2.soil.rareotutabcls.statesum[,grep("_unclassified",names(its2.soil.rareotutabcls.statesum))]
its2.soil.clsunknownsum<-rowSums(its2.soil.clsunknown)
#I then use the same grep function as above to pull out OTUs classified to the classlevel. By setting invert=TRUE I select items lacking the strings in the grep command.
its2.soil.clstot<-its2.soil.rareotutabcls.statesum[,grep("_unclassified",names(its2.soil.rareotutabcls.statesum), invert=TRUE)]
#I then subset the data so that only numerical data is present and creating a new class of objects, other, that includes OTUs from classes that represent 1% or less of the total community.
its2.soil.clscols<-its2.soil.clstot[,2:64]
its2.soil.clsoth<-its2.soil.clscols[,colSums(its2.soil.clscols)/sum(its2.soil.clscols)<=0.01]
its2.soil.clsothers<-rowSums(its2.soil.clsoth)
its2.soil.clsreg<-its2.soil.clscols[,colSums(its2.soil.clscols)/sum(its2.soil.clscols)>0.01]
#I then create a new data frame that contains all of the class data and determine relative abundance using the decostand function from vegan.
its2.soil.clstot2<-data.frame(state=its2.soil.clstot$state,its2.soil.clsreg,Other=its2.soil.clsothers,Unclassified=its2.soil.clsunknownsum)
library(vegan)
its2.soil.clsrelabund<-decostand(its2.soil.clstot2[,2:14],method="total")
its2.soil.clsrelabund<-data.frame(state=its2.soil.rareotutabcls.statesum$state,its2.soil.clsrelabund)
#Below I use the melt function from the data.table package to reformat the data into stacked bar graph format.
library(data.table)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
its2.soil.clsrelabundmelt<-melt(its2.soil.clsrelabund, id.vars="state", variable.name="class")
its2.soil.cls.colorCount<- length(unique(its2.soil.clsrelabundmelt[,'class']))
getPalette=colorRampPalette(brewer.pal(9,"Set1"))
#Below I generate the relative abundance bar charts in ggplot
its2.soil.clsrel<-ggplot(its2.soil.clsrelabundmelt, aes(x=state, y=value, fill=class))+
geom_bar(stat="identity",show.legend=TRUE,color="black")+
scale_fill_manual(values=getPalette(its2.soil.cls.colorCount))+
xlab("State") +
ylab("Relative Abundance") +
theme(panel.border = element_blank(),panel.background=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size = 14), axis.ticks.x = element_blank(), axis.text.y = element_text(size = 14), axis.line.y.left = element_line(), axis.title.y = element_text(size = 14))+
scale_y_continuous(breaks=c(0,0.25,0.50,0.75,1),limits=c(0,1.05))+
geom_text(data=NULL,aes(x=0.51, y=1.05,label="Soil: ITS2"),hjust=0,colour="black")
ggplotly(its2.soil.clsrel, tooltip="class")Order Relative Abundance Chart
Below I generate a order relative abundance plot. I first extract the order information from the OTU table that contains taxonomy information. I then clean the taxa names to remove assignment confidence values, the p__ designator that precedes phylum assignment, c__ designator that precedes class assignment, and the o__ designator that precedes order assignment. OTU raw abundance is then summed by order and state, orders that represent less than 1% of the total community are grouped into other, and relative abundance is calculated. Stacked bar charts are made using the melt function and ggplot.
#The following chunk creates a relative abundance bar chart at the order level.
#What I am doing below is cleaning up the order names. These include assignment confidence levels, p__, c__, and o__
library(plyr)
t.its2.soil.raretabord<-data.frame(order=t.its2.soil.rareotutabtaxsep$order,t.its2.soil.rareotu.nosingles)
t.its2.soil.raretabord$order<-sub("\\(.*)","",x=t.its2.soil.raretabord$order)
t.its2.soil.raretabord$order<-sub("o__","",x=t.its2.soil.raretabord$order)
t.its2.soil.raretabord$order<-sub("c__","",x=t.its2.soil.raretabord$order)
t.its2.soil.raretabord$order<-sub("p__","",x=t.its2.soil.raretabord$order)
#I then sum the OTUs by class and make the row names the order name.
t.its2.soil.rareotutabord<-as.data.frame(ddply(t.its2.soil.raretabord, .(order),colwise(sum)))
rownames(t.its2.soil.rareotutabord)<-make.names(t.its2.soil.rareotutabord$order)
t.its2.soil.rareotutabord<-t.its2.soil.rareotutabord[,2:46]
#I then transpose the data frame and sum each order by state.
its2.soil.rareotutabord<-as.data.frame(t(t.its2.soil.rareotutabord))
its2.soil.rareotutabord.state<-data.frame(state=its2.soil.met.rare$State,its2.soil.rareotutabord)
its2.soil.rareotutabord.statesum<-as.data.frame(ddply(its2.soil.rareotutabord.state, .(state),colwise(sum)))
#At the order level, UNITE may classify things as phylum_unclassified or class_unclassified. These don't provide a lot of information and thus are grouped into a new group called unknown.
its2.soil.ordunknown<-its2.soil.rareotutabord.statesum[,grep("_unclassified",names(its2.soil.rareotutabord.statesum))]
#I then use the same grep function as above to pull out OTUs classified to the order level. By setting invert=TRUE I select items lacking the strings in the grep command.
its2.soil.ordunknownsum<-rowSums(its2.soil.ordunknown)
its2.soil.ordtot<-its2.soil.rareotutabord.statesum[,grep("_unclassified",names(its2.soil.rareotutabord.statesum), invert=TRUE)]
#I then subset the data so that only numerical data is present and create a new class of objects, other, that includes OTUs from orders that represent 1% or less of the total community.
its2.soil.ordcols<-its2.soil.ordtot[,2:159]
its2.soil.ordoth<-its2.soil.ordcols[,colSums(its2.soil.ordcols)/sum(its2.soil.ordcols)<=0.01]
its2.soil.ordothers<-rowSums(its2.soil.ordoth)
its2.soil.ordreg<-its2.soil.ordcols[,colSums(its2.soil.ordcols)/sum(its2.soil.ordcols)>0.01]
#I then create a new data frame that contains all of the order data and determine relative abundance using the decostand function from vegan.
library(vegan)
its2.soil.ordtot2<-data.frame(state=its2.soil.ordtot$state,its2.soil.ordreg,Other=its2.soil.ordothers, Unclassified=its2.soil.ordunknownsum)
its2.soil.ordrelabund<-decostand(its2.soil.ordtot2[,2:22],method="total")
its2.soil.ordrelabund<-data.frame(state=its2.soil.rareotutabord.statesum$state,its2.soil.ordrelabund)
#Below I use the melt function from the data.table package to reformat the data into stacked bar graph format.
library(data.table)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
its2.soil.ordrelabundmelt<-melt(its2.soil.ordrelabund, id.vars="state", variable.name="order")
its2.soil.ord.colorCount<- length(unique(its2.soil.ordrelabundmelt[,'order']))
getPalette=colorRampPalette(brewer.pal(9,"Set1"))
#Below I generate the relative abundance bar charts in ggplot
its2.soil.ordrel<-ggplot(its2.soil.ordrelabundmelt, aes(x=state, y=value, fill=order))+
geom_bar(stat="identity",show.legend=TRUE,color="black")+
scale_fill_manual(values=getPalette(its2.soil.ord.colorCount))+
xlab("State") +
ylab("Relative Abundance") +
theme(panel.border = element_blank(),panel.background=element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size = 14), axis.ticks.x = element_blank(), axis.text.y = element_text(size = 14), axis.line.y.left = element_line(), axis.title.y = element_text(size = 14))+
scale_y_continuous(breaks=c(0,0.25,0.50,0.75,1),limits=c(0,1.05))+
geom_text(data=NULL,aes(x=0.5, y=1.05,label="Soil: ITS2"),hjust=0,colour="black")
ggplotly(its2.soil.ordrel, tooltip="order")Relative Abundance Panel
#This chunk creates a panel containing phylum, class, and order relative abundance charts.
library(ggpubr)
its2.soil.relab.panel<-ggarrange(its2.soil.phyrel,its2.soil.clsrel,its2.soil.ordrel, ncol=3,nrow=1,labels=c("A","B","C"))
#ggsave(filename = "its2_soil_relabundpanel.png", plot(its2.soil.relab.panel),dpi=300)OTU Renaming
To give OTUs more meaningful names, taxonomy strings can be subset to change the OTU ID to unique taxonomic ID. Similar to the relative abundance charts, the taxonomy file must first me added to a transposed OTU table, taxonomy strings need to be split into different classification levels, and the lowest level of classification can be used to replace the original OTU ID. For instance, if an OTU received Ascomycota as its highest level of classification, the OTU ID would be changed to Ascomycota1.
Taxonomy Filtering
Taxonomy is first filtered to include only those OTUs present in the rarefied OTU table with no singletons. Then the OTU table is transposed so that OTU ID is the row name and sample ID is the column name. Taxonomy strings are then added to the OTU table and strings are split by semi-colon.
#Below I am reformatting the OTU table so that I can begin to filter and merge the taxonomy information.
#I first transpose the rarefied OTU table with no singletons.
t.its2.soil.rareotu.nosingles<-as.data.frame(t(its2.soil.rareotu.nosingles))
#The below function is asking specifically for the OTU Isoil. These are the row names. I will use these to subset the taxonomy file.
t.its2.soil.rareotu.nosingleslabs<-labels(t.its2.soil.rareotu.nosingles)
its2.soil.taxrare<-subset(its2.taxonomy.soil, rownames(its2.taxonomy.soil) %in% t.its2.soil.rareotu.nosingleslabs[[1]])
its2.soil.taxrareinfo<-(its2.soil.taxrare[,2])
#Below I am using the separate function from tidyr to split the taxonomy strings into columns by semi-colon so that I can rename the OTUs to give them more meaning for downstream analyses.
library(tidyr)
its2.soil.taxonomylabs<-c("kingdom","phylum","class","order","family","genus","species")
t.its2.soil.rareotutabtax<-data.frame(taxonomy=its2.soil.taxrareinfo,t.its2.soil.rareotu.nosingles)
t.its2.soil.rareotutabtaxsep<-separate(t.its2.soil.rareotutabtax,into=its2.soil.taxonomylabs,col=taxonomy,sep=";")Renaming of OTUs
OTUs are then renamed. Prior to official renaming, taxonomy strings are cleaned so that assignment confidence values and taxon leaders (e.g. p__, c__, etc.) are removed.
#Here I am renaming the OTUs using the make.names function. This will make the OTU name something more informative. I am using species to do this.
t.its2.soil.rareotutabtaxsep$species<-sub("\\(.*)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(_unclassified)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(p__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(c__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(o__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(f__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(g__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(s__)","",x=t.its2.soil.rareotutabtaxsep$species)
rownames(t.its2.soil.rareotutabtaxsep)<-make.names(t.its2.soil.rareotutabtaxsep$species,unique=TRUE)
t.its2.soil.rareotutabtax.rename<-t.its2.soil.rareotutabtaxsep[,8:52]
its2.soil.rareotutabtax.rename<-as.data.frame(t(t.its2.soil.rareotutabtax.rename))Principal Coordinate Analysis (PCoA)
To visually assess differences in the community composition of soil fungal communities by clone and state, I perform a principal coordinate analysis (PCoA). ## Relative Abundance Calculation Prior to conducting a PCoA I first convert my OTU table from raw OTU abundances to relative abundance.
#Below I convert raw abundances to relative abundance using the decostand function from vegan.
library(vegan)
its2.soil.relabund<-decostand(its2.soil.rareotutabtax.rename, method="total")Principal Coordinate Analysis
I then perform the PCoA using the pcoa function from the ape package. Prior to performing the PCoA, a distance matrix needs to be calculated for the OTU table. In this case, I use the bray-curtis method.
#Below I calculate the distance matrix using the bray-curtis method and then perform the principal coordinate analysis on the relativized OTU table.
its2.soil.bacdist<-vegdist(its2.soil.relabund, method="bray")
library(ape)
its2.soil.bac.pcoa<-pcoa(its2.soil.bacdist)
#biplot(its2.soil.bac.pcoa,plot.axes = c(1,2))Plotting of Principal Coordinate Analysis
Then to plot the PCoA, I use the ggplot function. To use ggplot I need to first pull out the site scores for each sample and generate ellipses.
#We need to first subset our metadata table to include only those samples that passed rarefaction.
its2.soil.met.rare<-subset(its2.soil.met,its2.soil.met$Group%in%labels(its2.soil.rareotu.wsingles)[[1]])
#Then we extract PCoA site scores (these will be the x and y coordinates for each sample)
its2.soil.bac.pcoavec<-as.data.frame(its2.soil.bac.pcoa$vectors)
its2.soil.bac.pcoasitescores<-data.frame(PC1=its2.soil.bac.pcoavec$Axis.1, PC2=its2.soil.bac.pcoavec$Axis.2)
#Create a new dataframe that includes the site scores from above with metadata from your study.
its2.soil.bac.pcoagraph<-data.frame(its2.soil.bac.pcoasitescores,PC1=its2.soil.bac.pcoasitescores$PC1, PC2=its2.soil.bac.pcoasitescores$PC2, State=its2.soil.met.rare$State, Clone=its2.soil.met.rare$Clone,group=its2.soil.met.rare$Group)
#This is where you make confidence ellipses. I don't know what all the code means. Just know where to plug in my objects.
its2.soil.bac.pcoaellipse<-ordiellipse(its2.soil.bac.pcoasitescores,its2.soil.bac.pcoagraph$State, display="sites", kind="sd", draw="none")
df_ell.soil <- data.frame()
for(g in levels(its2.soil.bac.pcoagraph$State)){
df_ell.soil <- rbind(df_ell.soil, cbind(as.data.frame(with(its2.soil.bac.pcoagraph[its2.soil.bac.pcoagraph$State==g,], vegan:::veganCovEllipse(its2.soil.bac.pcoaellipse[[g]]$cov,its2.soil.bac.pcoaellipse[[g]]$center,its2.soil.bac.pcoaellipse[[g]]$scale))) ,State=g))}
#Plot PCoA in ggplot
library(ggplot2)
its2.soil.pcoa<-ggplot(its2.soil.bac.pcoagraph, aes(PC1,PC2, colour=State))+
#geom_text(aes(label=group))+
geom_path(data=df_ell.soil, aes(x=PC1, y=PC2, colour=State),size=0.5, linetype=1)+
xlab("PC1 (20.4%)")+
ylab("PC2 (13.7%)")+
geom_point(aes(shape=Clone), size=3.5)+
theme(axis.title.x=element_text(size=14, face="bold"))+
theme(axis.title.y=element_text(size=14, face="bold"))+
theme(axis.text.x=element_text(size=12, face="bold"))+
theme(axis.text.y=element_text(size=12, face="bold"))+
scale_color_manual(values=c("#006600","#3399FF","#FF9900"))+
scale_shape_manual(values=c(16,10,8,7,0,2))+
scale_x_continuous(breaks=c(-0.60,-0.30,0,0.30,0.60),limits=c(-0.60,0.60))+
scale_y_continuous(breaks=c(-0.60,-0.30,0,0.30,0.60),limits=c(-.60,.60))+
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.border=element_rect(colour="black", size=1, fill=NA))+
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.background = element_blank(),
panel.border=element_rect(color="black", size=1, fill=NA))+
geom_text(data=NULL,aes(x=-0.60,y=0.60,label="Soil: ITS2"),hjust=0,colour="black")
its2.soil.pcoa
PERMANOVA
To formally test how well state and clone explain differences in soil fungal community composition, I perform a PERMANOVA using the adonis function from vegan.
#Below I perform permanovas by state and clone using the adonis function from vegan.
library(vegan)
its2.soil.bac.perm.state.inter<-adonis(its2.soil.relabund~its2.soil.met.rare$State*its2.soil.met.rare$Clone, method="bray",permutations=10000)
its2.soil.bac.perm.state.inter##
## Call:
## adonis(formula = its2.soil.relabund ~ its2.soil.met.rare$State * its2.soil.met.rare$Clone, permutations = 10000, method = "bray")
##
## Permutation: free
## Number of permutations: 10000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model
## its2.soil.met.rare$State 2 3.2458 1.62292 6.2555
## its2.soil.met.rare$Clone 4 1.0595 0.26487 1.0209
## its2.soil.met.rare$State:its2.soil.met.rare$Clone 8 2.0825 0.26032 1.0034
## Residuals 30 7.7831 0.25944
## Total 44 14.1710
## R2 Pr(>F)
## its2.soil.met.rare$State 0.22905 9.999e-05 ***
## its2.soil.met.rare$Clone 0.07476 0.3979
## its2.soil.met.rare$State:its2.soil.met.rare$Clone 0.14696 0.4653
## Residuals 0.54923
## Total 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1its2.soil.bac.perm.state.add<-adonis(its2.soil.relabund~its2.soil.met.rare$State+its2.soil.met.rare$Clone, method="bray",permutations=10000)
its2.soil.bac.perm.state.add##
## Call:
## adonis(formula = its2.soil.relabund ~ its2.soil.met.rare$State + its2.soil.met.rare$Clone, permutations = 10000, method = "bray")
##
## Permutation: free
## Number of permutations: 10000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## its2.soil.met.rare$State 2 3.2458 1.62292 6.2511 0.22905 9.999e-05 ***
## its2.soil.met.rare$Clone 4 1.0595 0.26487 1.0202 0.07476 0.4075
## Residuals 38 9.8657 0.25962 0.69619
## Total 44 14.1710 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1its2.soil.bac.perm.state.sing<-adonis(its2.soil.relabund~its2.soil.met.rare$State, method="bray",permutations=10000)
its2.soil.bac.perm.state.sing##
## Call:
## adonis(formula = its2.soil.relabund ~ its2.soil.met.rare$State, permutations = 10000, method = "bray")
##
## Permutation: free
## Number of permutations: 10000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## its2.soil.met.rare$State 2 3.2458 1.62292 6.2391 0.22905 9.999e-05 ***
## Residuals 42 10.9252 0.26012 0.77095
## Total 44 14.1710 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Richness and Diversity
Then to assess the alpha diversity of our samples, I calculate the observed richness and shannon diversity index using the vegan package. This is done using the rarefied OTU table that contains singletons. Singletons are included to keep sampling depth standard between samples.
Richness
Observed OTU richness is first calculated for each sample. Then due to the unbalanced nature of our study design, we use a type 3 ANOVA.
#In this chunk we calculate richness and create a data frame including richness with metadata
#I first import my rarefied OTU table that contains singletons.
its2.soil.rareotu.wsingles<-read.table("Mothur_output/soilits2otutable.rarefied_jnigra17_fa19_gw.shared", header=TRUE, sep="\t")
#I then remove mothur associated metadata present in the first 3 columns.
its2.soil.rareotu<-as.data.frame(its2.soil.rareotu.wsingles[,4:10580])
#Below I calculate observed species richness using the specnumber function from vegan. I then create a new data frame to include sample metadata.
library(vegan)
its2.soil.fungi.rich<-specnumber(its2.soil.rareotu)
its2.soil.richnesstabmet<-data.frame(State=its2.soil.met.rare$State, clone=its2.soil.met.rare$Clone, sample=its2.soil.met.rare$Group,Richness=its2.soil.fungi.rich)
#I first test for a significant interaction between state and clone. Due to the unbalanced design we use a type 3 ANOVA.
library(car)
its2.soil.anova.typ3.rich<-aov((Richness)~State*clone, data=its2.soil.richnesstabmet)
its2.soil.typ3aov.rich<-Anova(its2.soil.anova.typ3.rich,type="III")
its2.soil.typ3aov.rich## Anova Table (Type III tests)
##
## Response: (Richness)
## Sum Sq Df F value Pr(>F)
## (Intercept) 3707408 1 111.5845 1.257e-11 ***
## State 106857 2 1.6081 0.2171
## clone 140589 4 1.0578 0.3944
## State:clone 148394 8 0.5583 0.8029
## Residuals 996753 30
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 3707408.3 | 1 | 111.5845278 | 0.0000000 |
| State | 106856.8 | 2 | 1.6080723 | 0.2170603 |
| clone | 140588.7 | 4 | 1.0578495 | 0.3943935 |
| State:clone | 148394.0 | 8 | 0.5582902 | 0.8029400 |
| Residuals | 996753.3 | 30 | NA | NA |
#The below two lines generate plots to evaluate regression assumptions.
par(mfrow=c(2,2))
plot(its2.soil.anova.typ3.rich)## Warning: not plotting observations with leverage one:
## 34
## Warning: not plotting observations with leverage one:
## 34
#There was no significant interaction between state and clone so we drop the interaction term.
its2.soil.anova.add.rich<-aov((Richness)~State+clone, data=its2.soil.richnesstabmet)
its2.soil.add.aov.rich<-Anova(its2.soil.anova.add.rich,type="III")
its2.soil.add.aov.rich## Anova Table (Type III tests)
##
## Response: (Richness)
## Sum Sq Df F value Pr(>F)
## (Intercept) 6006825 1 199.3275 < 2.2e-16 ***
## State 440957 2 7.3163 0.002051 **
## clone 116249 4 0.9644 0.438209
## Residuals 1145147 38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 6006824.7 | 1 | 199.3274812 | 0.0000000 |
| State | 440957.3 | 2 | 7.3162535 | 0.0020515 |
| clone | 116248.7 | 4 | 0.9643851 | 0.4382089 |
| Residuals | 1145147.4 | 38 | NA | NA |
#Perform Tukey Test
its2.soil.add.aov.rich.tukey<-TukeyHSD(its2.soil.anova.add.rich)
its2.soil.add.aov.rich.tukey.table<-rbind(its2.soil.add.aov.rich.tukey$State,its2.soil.add.aov.rich.tukey$clone)
its2.soil.add.aov.rich.tukey.table## diff lwr upr p adj
## TN-IN -20.111111 -168.1224 127.90017 0.941358890
## WA-IN -248.166667 -412.1370 -84.19631 0.001973266
## WA-TN -228.055556 -385.8361 -70.27500 0.003149589
## 132-130 -169.273810 -426.5035 87.95588 0.343040469
## 272-130 -126.375661 -376.8477 124.09634 0.603575211
## 55-130 -114.597884 -365.0699 135.87412 0.686801354
## WT-130 -107.759921 -344.1377 128.61785 0.689652370
## 272-132 42.898148 -198.6077 284.40402 0.986013657
## 55-132 54.675926 -186.8299 296.18180 0.965937896
## WT-132 61.513889 -165.3414 288.36915 0.935876384
## 55-272 11.777778 -222.5173 246.07289 0.999898761
## WT-272 18.615741 -200.5473 237.77875 0.999190846
## WT-55 6.837963 -212.3250 226.00097 0.999984877| diff | lwr | upr | p adj | |
|---|---|---|---|---|
| TN-IN | -20.111111 | -168.1224 | 127.90017 | 0.9413589 |
| WA-IN | -248.166667 | -412.1370 | -84.19631 | 0.0019733 |
| WA-TN | -228.055556 | -385.8361 | -70.27500 | 0.0031496 |
| 132-130 | -169.273809 | -426.5035 | 87.95588 | 0.3430405 |
| 272-130 | -126.375661 | -376.8477 | 124.09634 | 0.6035752 |
| 55-130 | -114.597884 | -365.0699 | 135.87412 | 0.6868014 |
| WT-130 | -107.759921 | -344.1377 | 128.61785 | 0.6896524 |
| 272-132 | 42.898148 | -198.6077 | 284.40402 | 0.9860137 |
| 55-132 | 54.675926 | -186.8299 | 296.18180 | 0.9659379 |
| WT-132 | 61.513889 | -165.3414 | 288.36915 | 0.9358764 |
| 55-272 | 11.777778 | -222.5173 | 246.07289 | 0.9998988 |
| WT-272 | 18.615741 | -200.5473 | 237.77875 | 0.9991908 |
| WT-55 | 6.837963 | -212.3250 | 226.00097 | 0.9999849 |
#The below two lines generate plots to evaluate regression assumptions.
par(mfrow=c(2,2))
plot(its2.soil.anova.add.rich)
#There was no significant effect of clone so we drop and see just state alone.
its2.soil.anova.sing.rich<-aov((Richness)~State, data=its2.soil.richnesstabmet)
its2.soil.sing.aov.rich<-Anova(its2.soil.anova.sing.rich,type="III")
its2.soil.sing.aov.rich## Anova Table (Type III tests)
##
## Response: (Richness)
## Sum Sq Df F value Pr(>F)
## (Intercept) 12871402 1 428.5719 < 2.2e-16 ***
## State 498418 2 8.2978 0.0009185 ***
## Residuals 1261396 42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 12871401.7 | 1 | 428.571854 | 0.0000000 |
| State | 498418.3 | 2 | 8.297778 | 0.0009185 |
| Residuals | 1261396.1 | 42 | NA | NA |
#Perform Tukey Test
its2.soil.sing.aov.rich.tukey<-TukeyHSD(its2.soil.anova.sing.rich)
its2.soil.sing.aov.rich.tukey.table<-rbind(its2.soil.sing.aov.rich.tukey$State)
its2.soil.sing.aov.rich.tukey.table## diff lwr upr p adj
## TN-IN -20.11111 -167.3057 127.08352 0.941156927
## WA-IN -248.16667 -411.2323 -85.10101 0.001775992
## WA-TN -228.05556 -384.9656 -71.14555 0.002872397| diff | lwr | upr | p adj | |
|---|---|---|---|---|
| TN-IN | -20.11111 | -167.3057 | 127.08352 | 0.9411569 |
| WA-IN | -248.16667 | -411.2323 | -85.10101 | 0.0017760 |
| WA-TN | -228.05556 | -384.9656 | -71.14555 | 0.0028724 |
#The below two lines generate plots to evaluate regression assumptions. I keep these muted because they're annoying if you're bulk running code.
par(mfrow=c(2,2))
plot(its2.soil.anova.sing.rich)
#I then create a box plot for the richness data by state using ggplot.
library(Hmisc)
library(ggpubr)
its2.soil.richness.summarized<-summarize(its2.soil.richnesstabmet$Richness,by=its2.soil.richnesstabmet$State, FUN=max)
its2.soil.richness.summarized<-data.frame(State=its2.soil.richness.summarized$`its2.soil.richnesstabmet$State`,Richness=its2.soil.richness.summarized$`its2.soil.richnesstabmet$Richness`)
library(ggplot2)
library(ggpubr)
#Below I create a boxplot for richness values.
its2.soil.rich<-ggboxplot(its2.soil.richnesstabmet, x="State", y="Richness", outlier.shape=NA)+
geom_jitter(data=its2.soil.richnesstabmet,position=position_jitter(0.2), aes(shape=clone),size=3)+
scale_shape_manual(values=c(16,10,8,7,0,2))+
#geom_text(data=richness.summarized, aes(x=State, y = 25 + Richness, label=Group, fontface="bold"))+
geom_text(data=NULL,aes(x=1, y=1270, label="A"))+
geom_text(data=NULL,aes(x=2, y=1270, label="A"))+
geom_text(data=NULL,aes(x=3, y=810, label="B"))+
geom_text(data=NULL,aes(x=0.5,y=1400,label="Soil: ITS2"),hjust=0)+
theme(axis.line.x=element_blank(), legend.position ="none")
its2.soil.rich
Diversity
Observed OTU richness is first calculated for each sample. Then due to the unbalanced nature of our study design, we use a type 3 ANOVA.
#In this chunk we calculate shannon diversity and create a data frame including diversity with metadata
#I first import my rarefied OTU table that contains singletons.
its2.soil.rareotu.wsingles<-read.table("Mothur_output/soilits2otutable.rarefied_jnigra17_fa19_gw.shared", header=TRUE, sep="\t")
#I then remove mothur associated metadata present in the first 3 columns.
its2.soil.rareotu<-as.data.frame(its2.soil.rareotu.wsingles[,4:10580])
#Calculate shannon diversity using the diversity function of vegan
library(vegan)
its2.soil.fungi.div<-diversity (its2.soil.rareotu, index="shannon")
#I then create a new data frame to include sample metadata.
its2.soil.diversity.tabmet<-data.frame(State=its2.soil.met.rare$State, clone=its2.soil.met.rare$Clone, sample=its2.soil.met.rare$Group,Shannon=its2.soil.fungi.div)
#I first test for a significant interaction between state and clone. Due to the unbalanced design we use a type 3 ANOVA.
library(car)
its2.soil.anova.typ3.div<-aov((Shannon)~State*clone, data=its2.soil.diversity.tabmet)
its2.soil.typ3aov.div<-Anova(its2.soil.anova.typ3.div,type="III")
its2.soil.typ3aov.div## Anova Table (Type III tests)
##
## Response: (Shannon)
## Sum Sq Df F value Pr(>F)
## (Intercept) 78.992 1 357.4864 <2e-16 ***
## State 0.616 2 1.3934 0.2638
## clone 0.852 4 0.9641 0.4415
## State:clone 1.426 8 0.8066 0.6020
## Residuals 6.629 30
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 78.9919278 | 1 | 357.4863627 | 0.0000000 |
| State | 0.6157807 | 2 | 1.3933906 | 0.2638378 |
| clone | 0.8521651 | 4 | 0.9641409 | 0.4414951 |
| State:clone | 1.4258873 | 8 | 0.8066256 | 0.6019640 |
| Residuals | 6.6289461 | 30 | NA | NA |
#The below two lines generate plots to evaluate regression assumptions.
par(mfrow=c(2,2))
plot(its2.soil.anova.typ3.div)## Warning: not plotting observations with leverage one:
## 34
## Warning: not plotting observations with leverage one:
## 34
#There was no significant interaction between state and clone so we drop the interaction term.
its2.soil.anova.add.div<-aov((Shannon)~State+clone, data=its2.soil.diversity.tabmet)
its2.soil.add.aov.div<-Anova(its2.soil.anova.add.div,type="III")
its2.soil.add.aov.div## Anova Table (Type III tests)
##
## Response: (Shannon)
## Sum Sq Df F value Pr(>F)
## (Intercept) 135.563 1 639.5409 <2e-16 ***
## State 0.062 2 0.1455 0.8651
## clone 0.290 4 0.3420 0.8479
## Residuals 8.055 38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 135.5630450 | 1 | 639.5409366 | 0.0000000 |
| State | 0.0616875 | 2 | 0.1455105 | 0.8650602 |
| clone | 0.2899649 | 4 | 0.3419893 | 0.8478853 |
| Residuals | 8.0548334 | 38 | NA | NA |
#Perform Tukey test
its2.soil.anova.add.div.tukey<-TukeyHSD(its2.soil.anova.add.div)
its2.soil.anova.add.div.tukey.table<-rbind(its2.soil.anova.add.div.tukey$State,its2.soil.anova.add.div.tukey$clone)
its2.soil.anova.add.div.tukey.table## diff lwr upr p adj
## TN-IN -0.061290633 -0.4538383 0.3312570 0.9233481
## WA-IN -0.117294758 -0.5521683 0.3175787 0.7891054
## WA-TN -0.056004125 -0.4744613 0.3624531 0.9430534
## 132-130 -0.136132631 -0.8183436 0.5460783 0.9784656
## 272-130 -0.266335038 -0.9306236 0.3979535 0.7801585
## 55-130 -0.162022706 -0.8263113 0.5022659 0.9556100
## WT-130 -0.132726557 -0.7596352 0.4941820 0.9732623
## 272-132 -0.130202407 -0.7707115 0.5103067 0.9769500
## 55-132 -0.025890075 -0.6663991 0.6146190 0.9999575
## WT-132 0.003406074 -0.5982474 0.6050596 1.0000000
## 55-272 0.104312332 -0.5170727 0.7256974 0.9886868
## WT-272 0.133608481 -0.4476440 0.7148610 0.9640332
## WT-55 0.029296149 -0.5519564 0.6105487 0.9998977| diff | lwr | upr | p adj | |
|---|---|---|---|---|
| TN-IN | -0.0612906 | -0.4538383 | 0.3312570 | 0.9233481 |
| WA-IN | -0.1172948 | -0.5521683 | 0.3175787 | 0.7891054 |
| WA-TN | -0.0560041 | -0.4744613 | 0.3624531 | 0.9430534 |
| 132-130 | -0.1361326 | -0.8183436 | 0.5460783 | 0.9784656 |
| 272-130 | -0.2663350 | -0.9306236 | 0.3979535 | 0.7801585 |
| 55-130 | -0.1620227 | -0.8263113 | 0.5022659 | 0.9556100 |
| WT-130 | -0.1327266 | -0.7596352 | 0.4941820 | 0.9732623 |
| 272-132 | -0.1302024 | -0.7707115 | 0.5103067 | 0.9769500 |
| 55-132 | -0.0258901 | -0.6663991 | 0.6146190 | 0.9999575 |
| WT-132 | 0.0034061 | -0.5982474 | 0.6050596 | 1.0000000 |
| 55-272 | 0.1043123 | -0.5170727 | 0.7256974 | 0.9886868 |
| WT-272 | 0.1336085 | -0.4476440 | 0.7148610 | 0.9640332 |
| WT-55 | 0.0292961 | -0.5519564 | 0.6105487 | 0.9998977 |
#The below two lines generate plots to evaluate regression assumptions.
par(mfrow=c(2,2))
plot(its2.soil.anova.add.div)
#There was no significant interaction between state and clone so we drop clone from the model.
its2.soil.anova.sing.div<-aov((Shannon)~State, data=its2.soil.diversity.tabmet)
its2.soil.sing.aov.div<-Anova(its2.soil.anova.sing.div,type="III")
its2.soil.sing.aov.div## Anova Table (Type III tests)
##
## Response: (Shannon)
## Sum Sq Df F value Pr(>F)
## (Intercept) 339.46 1 1708.5203 <2e-16 ***
## State 0.09 2 0.2331 0.7931
## Residuals 8.34 42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| Sum Sq | Df | F value | Pr(>F) | |
|---|---|---|---|---|
| (Intercept) | 339.4585065 | 1 | 1708.5202879 | 0.0000000 |
| State | 0.0926265 | 2 | 0.2330981 | 0.7930937 |
| Residuals | 8.3447983 | 42 | NA | NA |
#Perform Tukey test
its2.soil.anova.sing.div.tukey<-TukeyHSD(its2.soil.anova.sing.div)
its2.soil.anova.sing.div.tukey.table<-rbind(its2.soil.anova.sing.div.tukey$State)
its2.soil.anova.sing.div.tukey.table## diff lwr upr p adj
## TN-IN -0.06129063 -0.4398850 0.3173037 0.9184340
## WA-IN -0.11729476 -0.5367105 0.3021210 0.7766769
## WA-TN -0.05600413 -0.4595871 0.3475788 0.9393615| diff | lwr | upr | p adj | |
|---|---|---|---|---|
| TN-IN | -0.0612906 | -0.4398850 | 0.3173037 | 0.9184340 |
| WA-IN | -0.1172948 | -0.5367105 | 0.3021210 | 0.7766769 |
| WA-TN | -0.0560041 | -0.4595871 | 0.3475788 | 0.9393615 |
#The below two lines generate plots to evaluate regression assumptions.
par(mfrow=c(2,2))
plot(its2.soil.anova.sing.div)
#I then create a box plot for the richness data by state using ggplot.
library(Hmisc)
library(ggpubr)
its2.soil.diversity.summarized<-summarize(its2.soil.diversity.tabmet$Shannon,by=its2.soil.diversity.tabmet$State, FUN=max)
its2.soil.diversity.summarized<-data.frame(State=its2.soil.diversity.summarized$`its2.soil.diversity.tabmet$State`,Diversity=its2.soil.diversity.summarized$`its2.soil.diversity.tabmet$Shannon`)
library(ggplot2)
library(ggpubr)
#Below I create a boxplot for richness values.
its2.soil.div<-ggboxplot(its2.soil.diversity.tabmet, x="State", y="Shannon", outlier.shape=NA)+
geom_jitter(data=its2.soil.diversity.tabmet,position=position_jitter(0.2), aes(shape=clone),size=3)+
scale_shape_manual(values=c(16,10,8,7,0,2))+
#geom_text(data=diversity.summarized, aes(x=State, y = 0.5 + Diversity, label=group, fontface="bold"))+
geom_text(data=NULL,aes(x=0.5,y=5.75,label="Soil: ITS2"),hjust=0)+
theme(axis.line.x=element_blank(), legend.position ="none")
its2.soil.div
OTU Renaming
To give OTUs more meaningful names, taxonomy strings can be subset to change the OTU ID to unique taxonomic ID. Similar to the relative abundance charts, the taxonomy file must first me added to a transposed OTU table, taxonomy strings need to be split into different classification levels, and the lowest level of classification can be used to replace the original OTU ID. For instance, if an OTU received Ascomycota as its highest level of classification, the OTU ID would be changed to Ascomycota1.
Taxonomy Filtering
Taxonomy is first filtered to include only those OTUs present in the rarefied OTU table with no singletons. Then the OTU table is transposed so that OTU ID is the row name and sample ID is the column name. Taxonomy strings are then added to the OTU table and strings are split by semi-colon.
#Below I am reformatting the OTU table so that I can begin to filter and merge the taxonomy information.
#I first transpose the rarefied OTU table with no singletons.
t.its2.soil.rareotu.nosingles<-as.data.frame(t(its2.soil.rareotu.nosingles))
#The below function is asking specifically for the OTU Isoil. These are the row names. I will use these to subset the taxonomy file.
t.its2.soil.rareotu.nosingleslabs<-labels(t.its2.soil.rareotu.nosingles)
its2.soil.taxrare<-subset(its2.taxonomy.soil, rownames(its2.taxonomy.soil) %in% t.its2.soil.rareotu.nosingleslabs[[1]])
its2.soil.taxrareinfo<-(its2.soil.taxrare[,2])
#Below I am using the separate function from tidyr to split the taxonomy strings into columns by semi-colon so that I can rename the OTUs to give them more meaning for downstream analyses.
library(tidyr)
its2.soil.taxonomylabs<-c("kingdom","phylum","class","order","family","genus","species")
t.its2.soil.rareotutabtax<-data.frame(taxonomy=its2.soil.taxrareinfo,t.its2.soil.rareotu.nosingles)
t.its2.soil.rareotutabtaxsep<-separate(t.its2.soil.rareotutabtax,into=its2.soil.taxonomylabs,col=taxonomy,sep=";")Renaming of OTUs
OTUs are then renamed. Prior to official renaming, taxonomy strings are cleaned so that assignment confidence values and taxon leaders (e.g. p__, c__, etc.) are removed.
#Here I am renaming the OTUs using the make.names function. This will make the OTU name something more informative. I am using species to do this.
t.its2.soil.rareotutabtaxsep$species<-sub("\\(.*)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(_unclassified)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(p__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(c__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(o__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(f__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(g__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("(s__)","",x=t.its2.soil.rareotutabtaxsep$species)
t.its2.soil.rareotutabtaxsep$species<-sub("_sp","",x=t.its2.soil.rareotutabtaxsep$species)
rownames(t.its2.soil.rareotutabtaxsep)<-make.names(paste(rownames(t.its2.soil.rareotutabtaxsep),t.its2.soil.rareotutabtaxsep$species,sep="_"))
t.its2.soil.rareotutabtax.rename<-t.its2.soil.rareotutabtaxsep[,8:52]
its2.soil.rareotutabtax.rename<-as.data.frame(t(t.its2.soil.rareotutabtax.rename))library(indicspecies)
set.seed(577)
indicspecies.its2.soil<-multipatt(its2.soil.rareotutabtax.rename, its2.soil.met.rare$State,func="IndVal.g",control=how(nperm=10000))
options(max.print=1000000)
sink(file="its2.soil.indicators.txt")
summary(indicspecies.its2.soil,indvalcomp=TRUE)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.05
##
## Total number of species: 6738
## Selected number of species: 1453
## Number of species associated to 1 group: 1174
## Number of species associated to 2 groups: 279
##
## List of species associated to each combination:
##
## Group IN #sps. 369
## A B stat p.value
## Otu00073_Hypocreales 0.8446 1.0000 0.919 0.0015 **
## Otu00086_Sordariomycetes 0.8163 1.0000 0.903 0.0049 **
## Otu00053_Ganodermataceae 0.8142 1.0000 0.902 0.0107 *
## Otu00321_Pleosporales 0.8659 0.9333 0.899 1e-04 ***
## Otu00225_Pleosporales 0.9765 0.8000 0.884 1e-04 ***
## Otu00244_Onygenaceae 0.9486 0.8000 0.871 1e-04 ***
## Otu00588_Paramyrothecium_humicola 0.9267 0.8000 0.861 0.0002 ***
## Otu01357_Hypocreales 1.0000 0.7333 0.856 1e-04 ***
## Otu00158_Lophiotrema_rubi 0.9602 0.7333 0.839 1e-04 ***
## Otu00424_Eurotiomycetes 0.8663 0.8000 0.833 0.0002 ***
## Otu01270_Cystobasidiomycetes 0.8552 0.8000 0.827 0.0002 ***
## Otu00314_Plectosphaerellaceae 0.8430 0.8000 0.821 0.0020 **
## Otu00168_Talaromyces 0.8308 0.8000 0.815 0.0006 ***
## Otu00712_Pyrenochaetopsis_pratorum 0.8168 0.8000 0.808 0.0002 ***
## Otu00062_Plectosphaerella 0.8841 0.7333 0.805 0.0014 **
## Otu00743_Pleosporales 0.8045 0.8000 0.802 0.0002 ***
## Otu00968_Septoglomus_viscosum 0.8759 0.7333 0.801 1e-04 ***
## Otu00524_Pleosporales 0.7353 0.8667 0.798 0.0011 **
## Otu00207_Helotiales 0.8676 0.7333 0.798 0.0002 ***
## Otu00194_Zopfiella_tabulata 0.8600 0.7333 0.794 0.0013 **
## Otu00117_Hypocreales 0.8560 0.7333 0.792 1e-04 ***
## Otu00204_Helotiales 0.7692 0.8000 0.784 0.0010 ***
## Otu00107_Trichocladium_opacum 0.7629 0.8000 0.781 0.0021 **
## Otu00390_Plectosphaerella_cucumerina 0.9068 0.6667 0.778 0.0016 **
## Otu00242_Microbotryomycetes 0.9020 0.6667 0.775 0.0041 **
## Otu00911_Pleosporales 0.9010 0.6667 0.775 0.0002 ***
## Otu00394_Agaricaceae 1.0000 0.6000 0.775 1e-04 ***
## Otu00100_Ascomycota 0.7483 0.8000 0.774 0.0007 ***
## Otu00549_Hypoxylon 0.8125 0.7333 0.772 0.0022 **
## Otu00441_Oidiodendron_truncatum 0.9930 0.6000 0.772 0.0002 ***
## Otu00794_Trichoderma 0.9885 0.6000 0.770 1e-04 ***
## Otu00315_Ascomycota 0.7391 0.8000 0.769 0.0004 ***
## Otu00520_Pleosporales 0.8040 0.7333 0.768 0.0016 **
## Otu00801_Paracremonium 0.9823 0.6000 0.768 0.0004 ***
## Otu00318_Cadophora 0.9769 0.6000 0.766 0.0012 **
## Otu01177_Ascomycota 0.9682 0.6000 0.762 0.0003 ***
## Otu00821_Pleosporales 0.7226 0.8000 0.760 0.0044 **
## Otu01119_Fusariumorotrichioides 0.8649 0.6667 0.759 0.0006 ***
## Otu00170_Didymosphaeriaceae 0.9536 0.6000 0.756 0.0240 *
## Otu00576_Tetracladium 0.9422 0.6000 0.752 0.0015 **
## Otu01325_Thelebolus_globosus 0.8469 0.6667 0.751 0.0019 **
## Otu00715_Ramicandelaber 0.9361 0.6000 0.749 0.0002 ***
## Otu00064_GS10 0.9276 0.6000 0.746 0.0005 ***
## Otu01345_Tetracladium_furcatum 0.9245 0.6000 0.745 0.0005 ***
## Otu00754_Ascomycota 0.8317 0.6667 0.745 0.0007 ***
## Otu00396_Mortierella_ambigua 0.6918 0.8000 0.744 0.0008 ***
## Otu00455_Mortierella_bisporalis 0.8300 0.6667 0.744 0.0026 **
## Otu00879_Pleosporales 0.8263 0.6667 0.742 0.0032 **
## Otu00982_Pleosporales 0.7494 0.7333 0.741 0.0015 **
## Otu00741_Orbiliaceae 0.8227 0.6667 0.741 0.0025 **
## Otu02185_Neosetophoma_samararum 0.9057 0.6000 0.737 0.0003 ***
## Otu00864_Thyridariaceae 0.8077 0.6667 0.734 0.0008 ***
## Otu00592_Mrakia 0.8932 0.6000 0.732 0.0020 **
## Otu00302_Ascomycota 1.0000 0.5333 0.730 0.0002 ***
## Otu00361_Ascomycota 1.0000 0.5333 0.730 0.0003 ***
## Otu01715_Bovista 1.0000 0.5333 0.730 0.0003 ***
## Otu00535_Clonostachys_intermedia 0.9938 0.5333 0.728 0.0003 ***
## Otu01549_Ascomycota 0.8826 0.6000 0.728 0.0005 ***
## Otu01733_Leucosporidium_golubevii 0.8780 0.6000 0.726 0.0009 ***
## Otu00450_Coprinopsis 0.9845 0.5333 0.725 0.0008 ***
## Otu01083_Helotiales 0.7803 0.6667 0.721 0.0006 ***
## Otu01166_Arachnomyces 0.9750 0.5333 0.721 0.0003 ***
## Otu00299_Trichosporonaceae 0.9658 0.5333 0.718 0.0005 ***
## Otu01502_Glomus_macrocarpum 0.9635 0.5333 0.717 0.0002 ***
## Otu00181_GS11 0.6995 0.7333 0.716 0.0044 **
## Otu00948_Cystofilobasidium_macerans 0.8493 0.6000 0.714 0.0431 *
## Otu01506_Glomus_macrocarpum 0.9550 0.5333 0.714 1e-04 ***
## Otu00663_Eurotiomycetes 0.7637 0.6667 0.714 0.0017 **
## Otu01199_Hypocreales 0.8473 0.6000 0.713 0.0013 **
## Otu00871_Tremellomycetes 0.9477 0.5333 0.711 0.0006 ***
## Otu00545_Schwanniomyces_occidentalis 0.9464 0.5333 0.710 0.0028 **
## Otu00401_Rozellomycota 0.9453 0.5333 0.710 0.0066 **
## Otu01139_Mariannaea_pinicola 0.8393 0.6000 0.710 0.0011 **
## Otu02121_Ganodermataceae 0.8276 0.6000 0.705 0.0013 **
## Otu00024_Stachybotryaceae 0.6690 0.7333 0.700 0.0021 **
## Otu00304_Cephalotrichum_stemonitis 0.8033 0.6000 0.694 0.0054 **
## Otu01426_Pyrenochaetopsis_pratorum 0.7227 0.6667 0.694 0.0017 **
## Otu01591_Melanophyllum_haematospermum 0.8913 0.5333 0.689 0.0010 ***
## Otu01383_Septoglomus_viscosum 0.8802 0.5333 0.685 0.0004 ***
## Otu00961_Onygenaceae 0.7808 0.6000 0.684 0.0020 **
## Otu00319_Ascomycota 0.8761 0.5333 0.684 0.0008 ***
## Otu00283_Orbiliaceae 1.0000 0.4667 0.683 0.0007 ***
## Otu00630_Agaricales 1.0000 0.4667 0.683 0.0007 ***
## Otu00940_Entoloma 1.0000 0.4667 0.683 0.0009 ***
## Otu01427_Chaetomiaceae 1.0000 0.4667 0.683 0.0003 ***
## Otu01467_Conocybe 1.0000 0.4667 0.683 0.0006 ***
## Otu02934_Phallus_rugulosus 1.0000 0.4667 0.683 0.0009 ***
## Otu01164_Magnaporthaceae 0.8712 0.5333 0.682 0.0021 **
## Otu00320_Sordariales 0.9930 0.4667 0.681 0.0016 **
## Otu00536_Halosphaeriaceae 0.6305 0.7333 0.680 0.0140 *
## Otu01525_Entoloma 0.9814 0.4667 0.677 0.0010 ***
## Otu00408_Hypocreales 0.9785 0.4667 0.676 0.0031 **
## Otu00976_Coprinopsis_neophlyctidospora 0.9751 0.4667 0.675 0.0022 **
## Otu00489_Cadophora 0.9717 0.4667 0.673 0.0017 **
## Otu00855_Sordariomycetes 0.9715 0.4667 0.673 0.0012 **
## Otu01099_Sordariomycetes 0.8363 0.5333 0.668 0.0120 *
## Otu02003_Hannaella_oryzae 0.9490 0.4667 0.665 0.0016 **
## Otu01842_Metarhizium_rileyi 0.8276 0.5333 0.664 0.0043 **
## Otu01869_Scleroderma_areolatum 0.9438 0.4667 0.664 0.0031 **
## Otu00950_Aphanoascus_keratinophilus 0.8237 0.5333 0.663 0.0072 **
## Otu01947_Chytridiomycota 0.8214 0.5333 0.662 0.0020 **
## Otu00226_Dothideomycetes 0.8191 0.5333 0.661 0.0097 **
## Otu00560_Sordariales 0.8170 0.5333 0.660 0.0112 *
## Otu00908_GS14 0.7255 0.6000 0.660 0.0227 *
## Otu00510_Trichophaeopsis 0.9317 0.4667 0.659 0.0017 **
## Otu01248_Septoglomus_viscosum 0.7229 0.6000 0.659 0.0030 **
## Otu01537_Mariannaea_superimposita 0.8077 0.5333 0.656 0.0073 **
## Otu02252_Phallaceae 0.7970 0.5333 0.652 0.0040 **
## Otu02595_Zymoseptoria 0.9107 0.4667 0.652 0.0016 **
## Otu01915_Hypocreales 0.9057 0.4667 0.650 0.0072 **
## Otu00873_Lophiostoma 0.7902 0.5333 0.649 0.0064 **
## Otu02166_Coprinopsis 0.9020 0.4667 0.649 0.0025 **
## Otu00687_Colletotrichum 0.9002 0.4667 0.648 0.0187 *
## Otu01085_Goffeauzyma_gastrica 0.7850 0.5333 0.647 0.0157 *
## Otu02119_Chytridiomycota 0.8916 0.4667 0.645 0.0027 **
## Otu01089_Cladophialophora 0.6923 0.6000 0.645 0.0063 **
## Otu01547_Gibellulopsis_chrysanthemi 0.7785 0.5333 0.644 0.0037 **
## Otu00252_Mortierella_bisporalis 0.8859 0.4667 0.643 0.0067 **
## Otu00375_Acremonium_furcatum 0.6857 0.6000 0.641 0.0168 *
## Otu01258_Microdochium_trichocladiopsis 0.8630 0.4667 0.635 0.0078 **
## Otu00348_Ascomycota 0.8610 0.4667 0.634 0.0071 **
## Otu01649_Hypocreales 0.8606 0.4667 0.634 0.0049 **
## Otu02002_Ascomycota 1.0000 0.4000 0.632 0.0007 ***
## Otu02723_Powellomycetaceae 1.0000 0.4000 0.632 0.0008 ***
## Otu02906_Cystobasidium 1.0000 0.4000 0.632 0.0008 ***
## Otu00481_Clavicipitaceae 0.8530 0.4667 0.631 0.0081 **
## Otu00708_GS10 0.7425 0.5333 0.629 0.0269 *
## Otu00739_Eurotiomycetes 0.9850 0.4000 0.628 0.0042 **
## Otu00635_Cadophora 0.6552 0.6000 0.627 0.0104 *
## Otu00802_Agaricales 0.9789 0.4000 0.626 0.0041 **
## Otu00512_Clavicipitaceae 0.9780 0.4000 0.625 0.0079 **
## Otu01963_Lepiota 0.9730 0.4000 0.624 0.0037 **
## Otu01806_Rhizophagus 0.9701 0.4000 0.623 0.0042 **
## Otu00550_Helotiaceae 0.9651 0.4000 0.621 0.0201 *
## Otu01969_Onygenales 0.9635 0.4000 0.621 0.0043 **
## Otu00496_Chaetosphaeria_myriocarpa 0.9634 0.4000 0.621 0.0238 *
## Otu00878_Tulostoma 0.9564 0.4000 0.619 0.0140 *
## Otu01214_Plectosphaerellaceae 0.8186 0.4667 0.618 0.0299 *
## Otu01113_Lophiostoma 0.9538 0.4000 0.618 0.0242 *
## Otu02270_Hannaella_zeae 0.8136 0.4667 0.616 0.0042 **
## Otu00462_GS11 0.8118 0.4667 0.616 0.0318 *
## Otu00776_Ascomycota 0.9424 0.4000 0.614 0.0085 **
## Otu02378_Pleosporales 0.8077 0.4667 0.614 0.0052 **
## Otu01563_GS05 0.8045 0.4667 0.613 0.0031 **
## Otu00980_Sordariomycetes 0.8036 0.4667 0.612 0.0393 *
## Otu01104_Onygenaceae 0.6240 0.6000 0.612 0.0142 *
## Otu01758_Ascomycota 0.9351 0.4000 0.612 0.0052 **
## Otu02611_Cystofilobasidiales 0.9351 0.4000 0.612 0.0025 **
## Otu00962_Volutella 0.8006 0.4667 0.611 0.0295 *
## Otu00493_Mortierella_sclerotiella 0.7982 0.4667 0.610 0.0138 *
## Otu01117_Didymosphaeriaceae 0.9255 0.4000 0.608 0.0044 **
## Otu02446_Glomus_macrocarpum 0.9194 0.4000 0.606 0.0053 **
## Otu02506_Ascomycota 0.6890 0.5333 0.606 0.0093 **
## Otu01931_Ascomycota 0.9165 0.4000 0.605 0.0162 *
## Otu00362_Sordariomycetes 0.9057 0.4000 0.602 0.0134 *
## Otu03010_Cladorrhinum 0.9057 0.4000 0.602 0.0077 **
## Otu02430_Ustilentyloma_graminis 0.9057 0.4000 0.602 0.0046 **
## Otu01036_Minimelanolocus_asiaticus 0.9003 0.4000 0.600 0.0114 *
## Otu00952_Lipomyces_tetrasporus 0.7703 0.4667 0.600 0.0137 *
## Otu00643_Onygenales 0.6652 0.5333 0.596 0.0174 *
## Otu00882_GS19 0.7590 0.4667 0.595 0.0078 **
## Otu01460_Bifiguratus 0.8824 0.4000 0.594 0.0097 **
## Otu02210_Ascomycota 0.8824 0.4000 0.594 0.0079 **
## Otu00752_Eurotiomycetes 0.8717 0.4000 0.590 0.0453 *
## Otu00574_Rozellomycota 0.7390 0.4667 0.587 0.0192 *
## Otu01431_Glomeraceae 0.8571 0.4000 0.586 0.0229 *
## Otu02213_Ascomycota 0.6429 0.5333 0.586 0.0124 *
## Otu01001_Eurotiomycetes 1.0000 0.3333 0.577 0.0029 **
## Otu01029_Ascomycota 1.0000 0.3333 0.577 0.0029 **
## Otu01053_Eurotiomycetes 1.0000 0.3333 0.577 0.0038 **
## Otu01746_Agaricales 1.0000 0.3333 0.577 0.0035 **
## Otu01791_Ascomycota 1.0000 0.3333 0.577 0.0028 **
## Otu02099_Pleosporales 1.0000 0.3333 0.577 0.0029 **
## Otu02164_Rhizophydium 1.0000 0.3333 0.577 0.0034 **
## Otu02620_Dothideomycetes 1.0000 0.3333 0.577 0.0033 **
## Otu02639_Hypocreales 1.0000 0.3333 0.577 0.0034 **
## Otu02646_Ascomycota 1.0000 0.3333 0.577 0.0030 **
## Otu02658_Rhizophagus 1.0000 0.3333 0.577 0.0036 **
## Otu02660_Sordariomycetes 1.0000 0.3333 0.577 0.0025 **
## Otu02798_Ramicandelaber 1.0000 0.3333 0.577 0.0030 **
## Otu02951_Coprinopsis_insignis 1.0000 0.3333 0.577 0.0034 **
## Otu02974_Chaetomiaceae 1.0000 0.3333 0.577 0.0028 **
## Otu03013_Ascomycota 1.0000 0.3333 0.577 0.0033 **
## Otu03139_Sordariomycetes 1.0000 0.3333 0.577 0.0032 **
## Otu03275_Chaetothyriales 1.0000 0.3333 0.577 0.0044 **
## Otu03959_Eurotiomycetes 1.0000 0.3333 0.577 0.0031 **
## Otu00919_GS10 0.8317 0.4000 0.577 0.0374 *
## Otu01850_Pleosporales 0.8317 0.4000 0.577 0.0446 *
## Otu00047_GS11 0.9959 0.3333 0.576 0.0193 *
## Otu01704_Sarocladium_hominis 0.8276 0.4000 0.575 0.0073 **
## Otu01309_Glomeraceae 0.8248 0.4000 0.574 0.0277 *
## Otu01354_Glomeraceae 0.8243 0.4000 0.574 0.0151 *
## Otu01824_Ramicandelaber_taiwanensis 0.8131 0.4000 0.570 0.0205 *
## Otu01462_Aspergillaceae 0.9706 0.3333 0.569 0.0183 *
## Otu00213_Magnaporthaceae 0.6917 0.4667 0.568 0.0387 *
## Otu02184_Amauroascus_aureus 0.9580 0.3333 0.565 0.0076 **
## Otu00886_Onygenales 0.7931 0.4000 0.563 0.0140 *
## Otu00958_Ascomycota 0.9424 0.3333 0.560 0.0196 *
## Otu02076_Perenniporia_delavayi 0.6711 0.4667 0.560 0.0450 *
## Otu02627_Exophiala_moniliae 0.7826 0.4000 0.560 0.0113 *
## Otu01041_Paraglomeromycetes 0.6690 0.4667 0.559 0.0488 *
## Otu01596_Pluteus 0.9324 0.3333 0.558 0.0136 *
## Otu01059_Glomeraceae 0.9317 0.3333 0.557 0.0415 *
## Otu02056_Cylindrocarpon 0.9296 0.3333 0.557 0.0096 **
## Otu01396_Itersonilia_perplexans 0.9261 0.3333 0.556 0.0169 *
## Otu03251_Mortierella 0.9231 0.3333 0.555 0.0107 *
## Otu03673_Ascomycota 0.9231 0.3333 0.555 0.0111 *
## Otu01440_Thermoascus_aurantiacus 0.9215 0.3333 0.554 0.0229 *
## Otu02427_Malassezia_globosa 0.9194 0.3333 0.554 0.0101 *
## Otu02062_Xylariaceae 0.7651 0.4000 0.553 0.0188 *
## Otu02768_Phaeosphaeriaceae 0.9153 0.3333 0.552 0.0096 **
## Otu03346_Steccherinum_bourdotii 0.9153 0.3333 0.552 0.0100 **
## Otu01441_Wardomyces_humicola 0.6535 0.4667 0.552 0.0378 *
## Otu03170_Malassezia_globosa 0.7619 0.4000 0.552 0.0144 *
## Otu00624_Atractiellales 0.9140 0.3333 0.552 0.0294 *
## Otu00409_Sordariomycetes 0.7478 0.4000 0.547 0.0485 *
## Otu01787_Pezizales 0.8936 0.3333 0.546 0.0353 *
## Otu01138_Ramicandelaber_taiwanensis 0.8858 0.3333 0.543 0.0341 *
## Otu02626_Sordariomycetes 0.8848 0.3333 0.543 0.0259 *
## Otu02628_Alfaria 0.7368 0.4000 0.543 0.0315 *
## Otu02116_Ascomycota 0.8837 0.3333 0.543 0.0178 *
## Otu00764_Agaricomycetes 0.8828 0.3333 0.542 0.0175 *
## Otu03667_Stachybotrys 0.8780 0.3333 0.541 0.0116 *
## Otu01238_Helotiales 0.8739 0.3333 0.540 0.0435 *
## Otu02447_Ascomycota 0.8684 0.3333 0.538 0.0197 *
## Otu00825_Pleosporales 0.7172 0.4000 0.536 0.0415 *
## Otu01735_Ascomycota 0.8555 0.3333 0.534 0.0270 *
## Otu02961_Kondoa 0.7059 0.4000 0.531 0.0285 *
## Otu01479_Xylariales 0.8372 0.3333 0.528 0.0486 *
## Otu02807_Rutstroemiaceae 0.8276 0.3333 0.525 0.0254 *
## Otu02903_Lophiostoma_cynaroidis 0.6774 0.4000 0.521 0.0437 *
## Otu00464_Eurotiomycetes 1.0000 0.2667 0.516 0.0116 *
## Otu01011_Agaricomycetes 1.0000 0.2667 0.516 0.0130 *
## Otu01367_Clavicipitaceae 1.0000 0.2667 0.516 0.0120 *
## Otu01413_Dothideomycetes 1.0000 0.2667 0.516 0.0136 *
## Otu01435_Helotiales 1.0000 0.2667 0.516 0.0127 *
## Otu01641_Ascomycota 1.0000 0.2667 0.516 0.0127 *
## Otu01712_Ascomycota 1.0000 0.2667 0.516 0.0115 *
## Otu01756_Ascomycota 1.0000 0.2667 0.516 0.0121 *
## Otu02106_Stachybotrys 1.0000 0.2667 0.516 0.0116 *
## Otu02216_Hypocreales 1.0000 0.2667 0.516 0.0135 *
## Otu02344_Pezizales 1.0000 0.2667 0.516 0.0150 *
## Otu02439_Diversisporaceae 1.0000 0.2667 0.516 0.0120 *
## Otu02532_Diversisporaceae 1.0000 0.2667 0.516 0.0120 *
## Otu02539_Minutisphaera_aspera 1.0000 0.2667 0.516 0.0116 *
## Otu02599_Glomus_aggregatum 1.0000 0.2667 0.516 0.0130 *
## Otu02730_GS09 1.0000 0.2667 0.516 0.0127 *
## Otu02878_Preussia 1.0000 0.2667 0.516 0.0137 *
## Otu02891_Pleosporales 1.0000 0.2667 0.516 0.0127 *
## Otu02995_Septoglomus_viscosum 0.8000 0.3333 0.516 0.0213 *
## Otu02996_GS06 1.0000 0.2667 0.516 0.0126 *
## Otu03053_Ascomycota 1.0000 0.2667 0.516 0.0155 *
## Otu03184_Ascomycota 1.0000 0.2667 0.516 0.0132 *
## Otu03205_Basidiomycota 1.0000 0.2667 0.516 0.0127 *
## Otu03482_Plectosphaerellaceae 1.0000 0.2667 0.516 0.0116 *
## Otu03502_Didymosphaeriaceae 1.0000 0.2667 0.516 0.0127 *
## Otu03702_Nigrograna 1.0000 0.2667 0.516 0.0128 *
## Otu03922_Ascomycota 1.0000 0.2667 0.516 0.0126 *
## Otu04330_GS10 1.0000 0.2667 0.516 0.0138 *
## Otu04945_Cystobasidiomycetes 1.0000 0.2667 0.516 0.0129 *
## Otu00171_Cordana_terrestris 0.9987 0.2667 0.516 0.0395 *
## Otu01065_Ascomycota 0.9902 0.2667 0.514 0.0302 *
## Otu01452_Basidiomycota 0.9822 0.2667 0.512 0.0224 *
## Otu01651_Sordariales 0.9689 0.2667 0.508 0.0298 *
## Otu02622_Clavicipitaceae 0.9580 0.2667 0.505 0.0226 *
## Otu00977_Agaricomycetes 0.9550 0.2667 0.505 0.0455 *
## Otu01474_Capnodiales 0.7633 0.3333 0.504 0.0480 *
## Otu02487_Diversisporaceae 0.9505 0.2667 0.503 0.0293 *
## Otu02992_Erythrobasidium 0.9505 0.2667 0.503 0.0264 *
## Otu02412_Pleosporales 0.9485 0.2667 0.503 0.0357 *
## Otu01948_Branch03 0.9457 0.2667 0.502 0.0264 *
## Otu01950_Pleosporales 0.9398 0.2667 0.501 0.0270 *
## Otu02324_Myxotrichaceae 0.9351 0.2667 0.499 0.0288 *
## Otu02654_Irpex_hacksungii 0.9351 0.2667 0.499 0.0240 *
## Otu02720_Tubeufiaceae 0.9351 0.2667 0.499 0.0307 *
## Otu01088_Stachybotryaceae 0.9288 0.2667 0.498 0.0249 *
## Otu03332_GS05 0.9153 0.2667 0.494 0.0276 *
## Otu02717_Minimelanolocus_asiaticus 0.9123 0.2667 0.493 0.0272 *
## Otu02259_Septoglomus 0.9057 0.2667 0.491 0.0309 *
## Otu03666_Microascaceae 0.8936 0.2667 0.488 0.0300 *
## Otu03033_Melanogaster 0.8864 0.2667 0.486 0.0337 *
## Otu03692_Ascomycota 0.8780 0.2667 0.484 0.0341 *
## Otu03021_Talaromyces_ucrainicus 0.8684 0.2667 0.481 0.0294 *
## Otu01207_Nigrospora_oryzae 0.8583 0.2667 0.478 0.0324 *
## Otu02655_Eurotiomycetes 0.8571 0.2667 0.478 0.0436 *
## Otu04368_Hypocreales 0.8571 0.2667 0.478 0.0410 *
## Otu01835_Microdochium_colombiense 0.8544 0.2667 0.477 0.0388 *
## Otu00785_Ascomycota 0.8488 0.2667 0.476 0.0345 *
## Otu02396_Chaetomiaceae 0.8361 0.2667 0.472 0.0496 *
## Otu03241_Xenasma_rimicola 0.8276 0.2667 0.470 0.0454 *
## Otu03986_Pleosporales 0.8276 0.2667 0.470 0.0423 *
## Otu02860_Hypocreales 0.8000 0.2667 0.462 0.0444 *
## Otu02588_Phyllozyma_linderae 0.7619 0.2667 0.451 0.0497 *
## Otu00590_GS10 1.0000 0.2000 0.447 0.0478 *
## Otu01243_Ascomycota 1.0000 0.2000 0.447 0.0493 *
## Otu01483_Pluteus_nanus 1.0000 0.2000 0.447 0.0458 *
## Otu01532_Ascomycota 1.0000 0.2000 0.447 0.0488 *
## Otu01663_Sordariales 1.0000 0.2000 0.447 0.0482 *
## Otu01747_Rhizophydium 1.0000 0.2000 0.447 0.0443 *
## Otu01894_Hypocreales 1.0000 0.2000 0.447 0.0489 *
## Otu02059_Pyronemataceae 1.0000 0.2000 0.447 0.0482 *
## Otu02133_Pseudorobillarda_phragmitis 1.0000 0.2000 0.447 0.0476 *
## Otu02167_Agaricomycetes 1.0000 0.2000 0.447 0.0478 *
## Otu02187_Ascomycota 1.0000 0.2000 0.447 0.0461 *
## Otu02201_Pluteus 1.0000 0.2000 0.447 0.0472 *
## Otu02236_Septoglomus 1.0000 0.2000 0.447 0.0463 *
## Otu02260_Chaetomiaceae 1.0000 0.2000 0.447 0.0471 *
## Otu02322_Branch03 1.0000 0.2000 0.447 0.0459 *
## Otu02368_Sordariales 1.0000 0.2000 0.447 0.0488 *
## Otu02480_Ascomycota 1.0000 0.2000 0.447 0.0475 *
## Otu02531_Glomus_compressum 1.0000 0.2000 0.447 0.0489 *
## Otu02554_GS09 1.0000 0.2000 0.447 0.0461 *
## Otu02685_Trematosphaeria 1.0000 0.2000 0.447 0.0493 *
## Otu02853_Pacispora_scintillans 1.0000 0.2000 0.447 0.0496 *
## Otu02854_Dothideomycetes 1.0000 0.2000 0.447 0.0496 *
## Otu02958_Glomus_compressum 1.0000 0.2000 0.447 0.0459 *
## Otu03002_Dictyosporiaceae 1.0000 0.2000 0.447 0.0494 *
## Otu03042_Hypocreales 1.0000 0.2000 0.447 0.0474 *
## Otu03081_Pleosporales 1.0000 0.2000 0.447 0.0492 *
## Otu03085_Glomus_macrocarpum 1.0000 0.2000 0.447 0.0466 *
## Otu03123_Basidiomycota 1.0000 0.2000 0.447 0.0463 *
## Otu03185_Hypocreales 1.0000 0.2000 0.447 0.0459 *
## Otu03192_Helotiales 1.0000 0.2000 0.447 0.0463 *
## Otu03335_Pyrenochaetopsis_leptospora 1.0000 0.2000 0.447 0.0480 *
## Otu03355_Ascomycota 1.0000 0.2000 0.447 0.0472 *
## Otu03394_Ramicandelaber 1.0000 0.2000 0.447 0.0473 *
## Otu03425_Pacispora_scintillans 1.0000 0.2000 0.447 0.0496 *
## Otu03465_Rhizophydiales 1.0000 0.2000 0.447 0.0463 *
## Otu03467_Agaricaceae 1.0000 0.2000 0.447 0.0476 *
## Otu03487_Tremellales 1.0000 0.2000 0.447 0.0493 *
## Otu03622_Tomentella 1.0000 0.2000 0.447 0.0479 *
## Otu03626_Ascomycota 1.0000 0.2000 0.447 0.0482 *
## Otu03636_Atractiellales 1.0000 0.2000 0.447 0.0445 *
## Otu03663_Glomus_macrocarpum 1.0000 0.2000 0.447 0.0473 *
## Otu03691_Didymosphaeriaceae 1.0000 0.2000 0.447 0.0474 *
## Otu03746_Ascomycota 1.0000 0.2000 0.447 0.0492 *
## Otu03767_Rhizophydiales 1.0000 0.2000 0.447 0.0474 *
## Otu03795_GS05 1.0000 0.2000 0.447 0.0481 *
## Otu03820_Pacispora_scintillans 1.0000 0.2000 0.447 0.0496 *
## Otu03848_Phaeosphaeria 1.0000 0.2000 0.447 0.0462 *
## Otu03953_Sarcoporia 1.0000 0.2000 0.447 0.0486 *
## Otu03969_Trechispora 1.0000 0.2000 0.447 0.0479 *
## Otu04075_Sordariales 1.0000 0.2000 0.447 0.0469 *
## Otu04174_Chionosphaeraceae 1.0000 0.2000 0.447 0.0470 *
## Otu04203_Pyxidiophorales 1.0000 0.2000 0.447 0.0450 *
## Otu04229_Chytridiomycetes 1.0000 0.2000 0.447 0.0449 *
## Otu04308_Glomeraceae 1.0000 0.2000 0.447 0.0473 *
## Otu04329_Periconia 1.0000 0.2000 0.447 0.0462 *
## Otu04358_Chytridiomycota 1.0000 0.2000 0.447 0.0443 *
## Otu04362_Nectriaceae 1.0000 0.2000 0.447 0.0496 *
## Otu04404_Glomus_macrocarpum 1.0000 0.2000 0.447 0.0472 *
## Otu04438_Leptospora_rubella 1.0000 0.2000 0.447 0.0477 *
## Otu04457_Pleosporales 1.0000 0.2000 0.447 0.0478 *
## Otu04485_Thelephoraceae 1.0000 0.2000 0.447 0.0470 *
## Otu04512_Conocybe 1.0000 0.2000 0.447 0.0472 *
## Otu04564_Inocybe 1.0000 0.2000 0.447 0.0465 *
## Otu04633_Septoglomus 1.0000 0.2000 0.447 0.0498 *
## Otu04674_Herpotrichiellaceae 1.0000 0.2000 0.447 0.0472 *
## Otu04736_GS06 1.0000 0.2000 0.447 0.0469 *
## Otu04901_Helotiales 1.0000 0.2000 0.447 0.0463 *
## Otu05011_Rhinocladiella 1.0000 0.2000 0.447 0.0478 *
## Otu05138_Cordycipitaceae 1.0000 0.2000 0.447 0.0482 *
## Otu05163_Glomus_macrocarpum 1.0000 0.2000 0.447 0.0486 *
## Otu05296_Ascomycota 1.0000 0.2000 0.447 0.0476 *
## Otu05607_Sebacinales 1.0000 0.2000 0.447 0.0480 *
## Otu05759_Glomus_macrocarpum 1.0000 0.2000 0.447 0.0459 *
## Otu06203_Plectosphaerellaceae 1.0000 0.2000 0.447 0.0432 *
## Otu06209_Mortierella_minutissima 1.0000 0.2000 0.447 0.0467 *
## Otu06436_Hypocreales 1.0000 0.2000 0.447 0.0488 *
##
## Group TN #sps. 371
## A B stat p.value
## Otu00184_Trichomeriaceae 0.9555 0.9444 0.950 1e-04 ***
## Otu00033_Metarhizium_anisopliae 0.9897 0.8889 0.938 0.0014 **
## Otu00087_Penicillium 0.8771 0.9444 0.910 1e-04 ***
## Otu00250_Capnodiales 0.9912 0.8333 0.909 1e-04 ***
## Otu00389_Mycoleptodiscus 0.9254 0.8889 0.907 1e-04 ***
## Otu00072_Thyridariaceae 0.8538 0.9444 0.898 0.0002 ***
## Otu00880_Taphrina_inositophila 0.8454 0.9444 0.894 1e-04 ***
## Otu00271_Rachicladosporium 0.9402 0.8333 0.885 1e-04 ***
## Otu00838_Herpotrichiellaceae 0.8266 0.9444 0.884 0.0002 ***
## Otu00458_Lophiostomataceae 0.9972 0.7778 0.881 1e-04 ***
## Otu01052_Sphaceloma 0.9881 0.7778 0.877 1e-04 ***
## Otu00237_Trichomeriaceae 0.9867 0.7778 0.876 1e-04 ***
## Otu00508_Ascobolus 0.9162 0.8333 0.874 1e-04 ***
## Otu00519_Sympoventuriaceae 0.9147 0.8333 0.873 0.0005 ***
## Otu00485_Pleosporales 0.9130 0.8333 0.872 1e-04 ***
## Otu01082_Pleosporales 0.9774 0.7778 0.872 1e-04 ***
## Otu00417_Thyridariaceae 0.8535 0.8889 0.871 1e-04 ***
## Otu00095_Pleosporales 0.9409 0.7778 0.855 1e-04 ***
## Otu00385_Mortierellomycota 0.8701 0.8333 0.852 0.0003 ***
## Otu00350_Ascomycota 1.0000 0.7222 0.850 1e-04 ***
## Otu00719_Capnodiales 1.0000 0.7222 0.850 1e-04 ***
## Otu00881_Helminthosporium_asterinum 1.0000 0.7222 0.850 1e-04 ***
## Otu00899_Diaporthe 0.8617 0.8333 0.847 0.0002 ***
## Otu00327_Articulospora 0.9869 0.7222 0.844 1e-04 ***
## Otu00606_Phaeosphaeriaceae 0.9162 0.7778 0.844 0.0015 **
## Otu00040_Lophiostomataceae 0.7975 0.8889 0.842 1e-04 ***
## Otu00290_Exophiala 0.8442 0.8333 0.839 1e-04 ***
## Otu00397_Subulicystidium_perlongisporum 0.9670 0.7222 0.836 0.0002 ***
## Otu00565_Pleosporales 0.8815 0.7778 0.828 1e-04 ***
## Otu00637_Pleosporales 0.9434 0.7222 0.825 1e-04 ***
## Otu00058_Lophiostomataceae 0.8163 0.8333 0.825 1e-04 ***
## Otu00626_Articulospora 0.8042 0.8333 0.819 0.0016 **
## Otu00089_Ascomycota 0.8591 0.7778 0.817 1e-04 ***
## Otu00867_Ascomycota 1.0000 0.6667 0.816 1e-04 ***
## Otu00215_Basidiomycota 0.9972 0.6667 0.815 1e-04 ***
## Otu00144_Ascomycota 0.9944 0.6667 0.814 1e-04 ***
## Otu00706_Strelitziana_africana 0.9879 0.6667 0.812 1e-04 ***
## Otu00727_Thelephoraceae 0.8442 0.7778 0.810 0.0003 ***
## Otu00329_Cladophialophora 0.9773 0.6667 0.807 1e-04 ***
## Otu00476_Lophiostomataceae 0.8943 0.7222 0.804 0.0002 ***
## Otu01058_Sporobolomyces 0.8929 0.7222 0.803 0.0002 ***
## Otu01360_Valsonectria_pulchella 0.9609 0.6667 0.800 1e-04 ***
## Otu00152_Xylariales 0.7657 0.8333 0.799 0.0023 **
## Otu00307_Adisciso 0.7609 0.8333 0.796 0.0289 *
## Otu00594_Nigrograna 0.8731 0.7222 0.794 0.0047 **
## Otu01340_Penicillium 0.9341 0.6667 0.789 0.0003 ***
## Otu00239_Penicillium 0.9273 0.6667 0.786 0.0003 ***
## Otu00646_Eurotiomycetes 0.9253 0.6667 0.785 1e-04 ***
## Otu00731_Ochroconis_cordanae 0.8512 0.7222 0.784 0.0010 ***
## Otu00011_GS11 0.8483 0.7222 0.783 0.0002 ***
## Otu00082_Ascomycota 1.0000 0.6111 0.782 0.0002 ***
## Otu00497_Kochiomyces 1.0000 0.6111 0.782 0.0004 ***
## Otu00844_Capnodiales 1.0000 0.6111 0.782 0.0002 ***
## Otu01006_Capnodiales 1.0000 0.6111 0.782 1e-04 ***
## Otu00398_Pleosporales 0.9976 0.6111 0.781 0.0002 ***
## Otu00412_Ascomycota 0.9971 0.6111 0.781 0.0003 ***
## Otu00583_Cladophialophora 0.9957 0.6111 0.780 0.0005 ***
## Otu00042_Rhizophydiales 0.7796 0.7778 0.779 0.0004 ***
## Otu00616_Pleosporales 0.9076 0.6667 0.778 0.0003 ***
## Otu00537_Aspergillus 0.7236 0.8333 0.777 0.0047 **
## Otu01137_Xylogone 0.8333 0.7222 0.776 0.0003 ***
## Otu00400_Thanatephorus_cucumeris 0.9000 0.6667 0.775 1e-04 ***
## Otu00309_Aspergillaceae 0.9756 0.6111 0.772 1e-04 ***
## Otu00487_Auriculariales 0.9720 0.6111 0.771 0.0004 ***
## Otu01182_Xylariales 0.9716 0.6111 0.771 0.0002 ***
## Otu00219_Clavicipitaceae 0.8203 0.7222 0.770 0.0008 ***
## Otu00755_Chaetothyriales 0.8869 0.6667 0.769 0.0002 ***
## Otu00395_Minimedusa_polyspora 0.8856 0.6667 0.768 0.0161 *
## Otu00989_Bionectria_rossmaniae 0.9520 0.6111 0.763 0.0006 ***
## Otu00609_Glomus_macrocarpum 0.8035 0.7222 0.762 0.0020 **
## Otu00733_Diplodia 0.8588 0.6667 0.757 0.0004 ***
## Otu00875_Eurotiomycetes 0.9336 0.6111 0.755 0.0003 ***
## Otu01280_Ascomycota 0.9294 0.6111 0.754 0.0003 ***
## Otu00740_Rhizophydiales 0.8494 0.6667 0.753 0.0006 ***
## Otu00517_Penicillium 0.9183 0.6111 0.749 0.0038 **
## Otu00667_Xylariales 0.9171 0.6111 0.749 0.0047 **
## Otu00885_Onygenales 0.8392 0.6667 0.748 1e-04 ***
## Otu01170_Capnodiales 1.0000 0.5556 0.745 1e-04 ***
## Otu01217_Trichomeriaceae 1.0000 0.5556 0.745 0.0002 ***
## Otu01236_Pleosporales 1.0000 0.5556 0.745 0.0002 ***
## Otu01321_Capnodiales 1.0000 0.5556 0.745 1e-04 ***
## Otu01598_Didymosphaeriaceae 1.0000 0.5556 0.745 0.0002 ***
## Otu01902_Neodevriesia_lagerstroemiae 1.0000 0.5556 0.745 1e-04 ***
## Otu00432_GS11 0.8325 0.6667 0.745 0.0014 **
## Otu00357_Articulospora 0.8308 0.6667 0.744 0.0064 **
## Otu00061_GS11 0.9939 0.5556 0.743 0.0020 **
## Otu00330_Ascomycota 0.9862 0.5556 0.740 0.0006 ***
## Otu00199_Peniophoraceae 0.9805 0.5556 0.738 0.0244 *
## Otu01788_Cladophialophora 0.9746 0.5556 0.736 0.0002 ***
## Otu01184_Pleosporales 0.8838 0.6111 0.735 0.0002 ***
## Otu00990_Lophiostoma_fuckelii 0.9689 0.5556 0.734 0.0010 ***
## Otu01679_GS19 0.8794 0.6111 0.733 0.0007 ***
## Otu00625_Ascomycota 0.9599 0.5556 0.730 0.0015 **
## Otu00036_GS11 0.8639 0.6111 0.727 0.0012 **
## Otu00796_Pleosporales 0.8629 0.6111 0.726 0.0113 *
## Otu00422_Aspergillus 0.9484 0.5556 0.726 0.0098 **
## Otu00686_Pleosporales 0.8611 0.6111 0.725 0.0009 ***
## Otu00812_Capnodiales 0.9455 0.5556 0.725 0.0004 ***
## Otu01003_Chaetothyriaceae 0.9391 0.5556 0.722 0.0003 ***
## Otu01051_Thelephoraceae 0.9386 0.5556 0.722 0.0007 ***
## Otu01879_Ramicandelaber 0.9358 0.5556 0.721 0.0003 ***
## Otu00557_Branch06 0.7185 0.7222 0.720 0.0362 *
## Otu00775_Ascomycota 0.9310 0.5556 0.719 0.0008 ***
## Otu00136_GS11 0.9308 0.5556 0.719 0.0041 **
## Otu00269_Trichothecium 0.9100 0.5556 0.711 0.0007 ***
## Otu01429_Chaetosphaeriaceae 0.9032 0.5556 0.708 0.0012 **
## Otu00607_Endosporium_aviarium 1.0000 0.5000 0.707 0.0005 ***
## Otu00614_Podospora 1.0000 0.5000 0.707 0.0008 ***
## Otu00756_Trichomeriaceae 1.0000 0.5000 0.707 0.0004 ***
## Otu00817_Sphaceloma 1.0000 0.5000 0.707 0.0004 ***
## Otu00863_Pleosporales 1.0000 0.5000 0.707 0.0004 ***
## Otu01049_Helminthosporium 1.0000 0.5000 0.707 0.0007 ***
## Otu01103_Mortierella 1.0000 0.5000 0.707 0.0003 ***
## Otu01526_Lentitheciaceae 1.0000 0.5000 0.707 0.0004 ***
## Otu01568_Talaromyces 1.0000 0.5000 0.707 0.0003 ***
## Otu01742_Orbiliomycetes 1.0000 0.5000 0.707 0.0002 ***
## Otu00129_Penicillium 0.9893 0.5000 0.703 0.0312 *
## Otu01278_Cyphellophora_fusarioides 0.8884 0.5556 0.703 0.0009 ***
## Otu01361_Melanommataceae 0.8856 0.5556 0.701 0.0020 **
## Otu00652_Mortierella_gamsii 0.8739 0.5556 0.697 0.0015 **
## Otu00447_Hirsutella 0.8730 0.5556 0.696 0.0186 *
## Otu01338_Aspergillus 0.9690 0.5000 0.696 0.0018 **
## Otu01386_Ascomycota 0.9682 0.5000 0.696 0.0018 **
## Otu01693_Physcia 0.7895 0.6111 0.695 0.0043 **
## Otu01621_Glomus_macrocarpum 0.7741 0.6111 0.688 0.0020 **
## Otu00460_Talaromyces 0.7087 0.6667 0.687 0.0032 **
## Otu01736_Geastrum 0.9391 0.5000 0.685 0.0013 **
## Otu01352_Taphrina_inositophila 0.7042 0.6667 0.685 0.0031 **
## Otu00255_Clavariaceae 0.9379 0.5000 0.685 0.0012 **
## Otu01530_Ganoderma 0.7652 0.6111 0.684 0.0048 **
## Otu01770_Sympoventuriaceae 0.9341 0.5000 0.683 0.0011 **
## Otu01579_Orbiliomycetes 0.8374 0.5556 0.682 0.0063 **
## Otu00434_Clonostachys_rosea 0.9291 0.5000 0.682 0.0124 *
## Otu01131_Pleosporales 0.9286 0.5000 0.681 0.0016 **
## Otu00922_Chaetothyriaceae 0.8345 0.5556 0.681 0.0022 **
## Otu00431_Agaricomycetes 0.8337 0.5556 0.681 0.0082 **
## Otu00799_Chytridiales 0.9215 0.5000 0.679 0.0014 **
## Otu01765_Arthrocatena_tenebrio 0.8163 0.5556 0.673 0.0025 **
## Otu02142_Ascomycota 0.8989 0.5000 0.670 0.0024 **
## Otu00381_Arachnion_album 0.6724 0.6667 0.670 0.0057 **
## Otu01921_Donadinia_seaveri 0.8065 0.5556 0.669 0.0035 **
## Otu00803_Trichomeriaceae 1.0000 0.4444 0.667 0.0030 **
## Otu00841_Trichomeriaceae 1.0000 0.4444 0.667 0.0014 **
## Otu01010_Ascomycota 1.0000 0.4444 0.667 0.0020 **
## Otu01222_Capnodiales 1.0000 0.4444 0.667 0.0006 ***
## Otu01249_Herpotrichiellaceae 1.0000 0.4444 0.667 0.0008 ***
## Otu01308_Dothideomycetes 1.0000 0.4444 0.667 0.0010 ***
## Otu01319_Didymosphaeriaceae 1.0000 0.4444 0.667 0.0016 **
## Otu01378_Ascomycota 1.0000 0.4444 0.667 0.0010 ***
## Otu01608_Operculomyces_laminatus 1.0000 0.4444 0.667 0.0016 **
## Otu01625_Ascomycota 1.0000 0.4444 0.667 0.0019 **
## Otu01847_Mycosphaerellaceae 1.0000 0.4444 0.667 0.0014 **
## Otu01898_Pleosporales 1.0000 0.4444 0.667 0.0019 **
## Otu01965_Pleosporales 1.0000 0.4444 0.667 0.0010 ***
## Otu02015_Cyphellophora 1.0000 0.4444 0.667 0.0009 ***
## Otu02038_Diaporthe 1.0000 0.4444 0.667 0.0017 **
## Otu02129_Ascomycota 1.0000 0.4444 0.667 0.0013 **
## Otu02690_Cystobasidiomycetes 1.0000 0.4444 0.667 0.0005 ***
## Otu02395_Ganodermataceae 0.8879 0.5000 0.666 0.0008 ***
## Otu01237_Helotiales 0.8840 0.5000 0.665 0.0026 **
## Otu01073_Magnaporthiopsis 0.8834 0.5000 0.665 0.0031 **
## Otu00433_Branch06 0.9918 0.4444 0.664 0.0054 **
## Otu00753_Pleosporales 0.9885 0.4444 0.663 0.0075 **
## Otu00901_Glomeraceae 0.8696 0.5000 0.659 0.0115 *
## Otu01583_Capnodiales 0.9751 0.4444 0.658 0.0034 **
## Otu01875_Branch06 0.9677 0.4444 0.656 0.0036 **
## Otu00810_Clavicipitaceae 0.9665 0.4444 0.655 0.0049 **
## Otu01555_Branch03 0.8456 0.5000 0.650 0.0070 **
## Otu01135_Strelitziana_africana 0.9488 0.4444 0.649 0.0057 **
## Otu00690_Pleosporales 0.7570 0.5556 0.649 0.0079 **
## Otu02289_Coniothyrium 0.8407 0.5000 0.648 0.0029 **
## Otu00860_Chaetothyriales 0.7547 0.5556 0.648 0.0106 *
## Otu01364_Glomus 0.7472 0.5556 0.644 0.0232 *
## Otu00852_Ascomycota 0.9336 0.4444 0.644 0.0079 **
## Otu01638_Agaricomycetes 0.9244 0.4444 0.641 0.0038 **
## Otu00285_Clitopilus 0.9083 0.4444 0.635 0.0132 *
## Otu02329_Lepista_nuda 0.7983 0.5000 0.632 0.0046 **
## Otu02135_Sporobolomyces_symmetricus 0.7911 0.5000 0.629 0.0059 **
## Otu00898_Pleosporales 0.8870 0.4444 0.628 0.0139 *
## Otu00638_Dothideomycetes 0.8851 0.4444 0.627 0.0112 *
## Otu00943_Glomeraceae 0.8832 0.4444 0.627 0.0077 **
## Otu00633_Lecanoromycetes 1.0000 0.3889 0.624 0.0105 *
## Otu00732_Ascomycota 1.0000 0.3889 0.624 0.0077 **
## Otu01022_Ascomycota 1.0000 0.3889 0.624 0.0033 **
## Otu01458_Melanommataceae 1.0000 0.3889 0.624 0.0015 **
## Otu01630_Pleurotaceae 1.0000 0.3889 0.624 0.0040 **
## Otu01701_Leotiomycetes 1.0000 0.3889 0.624 0.0055 **
## Otu01882_Pleosporales 1.0000 0.3889 0.624 0.0051 **
## Otu01937_Chaetothyriales 1.0000 0.3889 0.624 0.0027 **
## Otu02040_Orbiliaceae 1.0000 0.3889 0.624 0.0020 **
## Otu02055_Dothideomycetes 1.0000 0.3889 0.624 0.0032 **
## Otu02387_Orbiliomycetes 1.0000 0.3889 0.624 0.0019 **
## Otu02679_Ascomycota 1.0000 0.3889 0.624 0.0020 **
## Otu01821_Powellomycetaceae 0.8537 0.4444 0.616 0.0055 **
## Otu01726_Glomeraceae 0.9722 0.3889 0.615 0.0066 **
## Otu00971_Trichomeriaceae 0.9720 0.3889 0.615 0.0204 *
## Otu01228_Ascomycota 0.8500 0.4444 0.615 0.0229 *
## Otu01634_Helotiaceae 0.8484 0.4444 0.614 0.0068 **
## Otu01492_Pleosporales 0.9609 0.3889 0.611 0.0029 **
## Otu02016_Laccaria 0.9589 0.3889 0.611 0.0056 **
## Otu01903_Robillarda_sessilis 0.9565 0.3889 0.610 0.0029 **
## Otu01444_Glomus_indicum 0.9551 0.3889 0.609 0.0087 **
## Otu01421_Helotiales 0.8307 0.4444 0.608 0.0183 *
## Otu01635_Didymellaceae 0.9459 0.3889 0.607 0.0083 **
## Otu02342_Claroideoglomeraceae 0.9434 0.3889 0.606 0.0049 **
## Otu02291_Pleurotheciella 0.8252 0.4444 0.606 0.0072 **
## Otu02557_Scolecobasidium_terreum 0.9375 0.3889 0.604 0.0035 **
## Otu00749_Lycoperdon 0.8173 0.4444 0.603 0.0176 *
## Otu01045_Mortierellomycota 0.9309 0.3889 0.602 0.0186 *
## Otu00676_Saksenaea_oblongispora 0.7983 0.4444 0.596 0.0315 *
## Otu01451_Microbotryomycetes 0.7975 0.4444 0.595 0.0108 *
## Otu02498_Cladophialophora 0.7895 0.4444 0.592 0.0150 *
## Otu01322_Auriculariales 0.9016 0.3889 0.592 0.0156 *
## Otu01342_Didymellaceae 0.7887 0.4444 0.592 0.0281 *
## Otu00956_Glomeraceae 0.8904 0.3889 0.588 0.0119 *
## Otu01371_Pleosporales 0.7759 0.4444 0.587 0.0177 *
## Otu01778_Chaetomiaceae 0.7692 0.4444 0.585 0.0159 *
## Otu02030_Pleosporales 0.8784 0.3889 0.584 0.0131 *
## Otu01823_Thyridariaceae 0.8636 0.3889 0.580 0.0191 *
## Otu01849_Pleosporales 0.6716 0.5000 0.580 0.0279 *
## Otu00627_Chaetothyriales 1.0000 0.3333 0.577 0.0058 **
## Otu01107_Herpotrichiellaceae 1.0000 0.3333 0.577 0.0054 **
## Otu01268_Trichomeriaceae 1.0000 0.3333 0.577 0.0052 **
## Otu01353_Glomeraceae 1.0000 0.3333 0.577 0.0047 **
## Otu01395_Ascomycota 1.0000 0.3333 0.577 0.0044 **
## Otu01442_Capnodiales 1.0000 0.3333 0.577 0.0047 **
## Otu01611_Ascomycota 1.0000 0.3333 0.577 0.0046 **
## Otu01739_Pleurotheciella 1.0000 0.3333 0.577 0.0054 **
## Otu01753_Ascomycota 1.0000 0.3333 0.577 0.0053 **
## Otu01766_Orbiliomycetes 1.0000 0.3333 0.577 0.0047 **
## Otu01775_Pleosporales 1.0000 0.3333 0.577 0.0061 **
## Otu01798_Pleosporales 1.0000 0.3333 0.577 0.0049 **
## Otu02108_Glomeraceae 1.0000 0.3333 0.577 0.0056 **
## Otu02117_Strelitziana 1.0000 0.3333 0.577 0.0054 **
## Otu02277_Xylariales 1.0000 0.3333 0.577 0.0052 **
## Otu02336_Sordariomycetes 1.0000 0.3333 0.577 0.0049 **
## Otu02561_Capnodiales 1.0000 0.3333 0.577 0.0053 **
## Otu02711_Ascomycota 1.0000 0.3333 0.577 0.0068 **
## Otu02896_Pleosporales 1.0000 0.3333 0.577 0.0063 **
## Otu02913_Trichomeriaceae 1.0000 0.3333 0.577 0.0068 **
## Otu02941_Ascomycota 1.0000 0.3333 0.577 0.0062 **
## Otu03209_Sympoventuriaceae 1.0000 0.3333 0.577 0.0057 **
## Otu03322_Thelephoraceae 1.0000 0.3333 0.577 0.0057 **
## Otu03943_Eucasphaeria 1.0000 0.3333 0.577 0.0064 **
## Otu00906_Glomeraceae 0.8567 0.3889 0.577 0.0300 *
## Otu00751_Mortierellomycota 0.9926 0.3333 0.575 0.0112 *
## Otu00387_Agaricales 0.9915 0.3333 0.575 0.0135 *
## Otu01109_Lophiostoma 0.9876 0.3333 0.574 0.0127 *
## Otu00830_Ceratobasidiaceae 0.9866 0.3333 0.573 0.0139 *
## Otu01722_Pleosporales 0.8371 0.3889 0.571 0.0382 *
## Otu01648_Glomeraceae 0.9746 0.3333 0.570 0.0091 **
## Otu01703_Diversisporales 0.9735 0.3333 0.570 0.0090 **
## Otu00648_Flagelloscypha_minutissima 0.9660 0.3333 0.567 0.0270 *
## Otu01979_Curvularia 0.9615 0.3333 0.566 0.0118 *
## Otu02222_Orbilia 0.9524 0.3333 0.563 0.0129 *
## Otu01201_Podospora 0.9406 0.3333 0.560 0.0103 *
## Otu02301_Exophiala 0.9375 0.3333 0.559 0.0147 *
## Otu02794_Branch03 0.9302 0.3333 0.557 0.0116 *
## Otu02254_Pseudochaetosphaeronema 0.7895 0.3889 0.554 0.0164 *
## Otu01652_Agaricomycetes 0.9155 0.3333 0.552 0.0452 *
## Otu02143_Tubeufiaceae 0.9124 0.3333 0.551 0.0193 *
## Otu02027_Staphylotrichum_boninense 0.7764 0.3889 0.549 0.0422 *
## Otu01289_Phaeomoniellaceae 0.9055 0.3333 0.549 0.0452 *
## Otu02034_Tremellales 0.9055 0.3333 0.549 0.0146 *
## Otu02203_Articulospora 0.9055 0.3333 0.549 0.0165 *
## Otu02379_Chaetothyriales 0.9055 0.3333 0.549 0.0158 *
## Otu03087_Glomus_macrocarpum 0.9016 0.3333 0.548 0.0138 *
## Otu01432_Ascomycota 0.8952 0.3333 0.546 0.0304 *
## Otu03204_Dioszegia 0.8889 0.3333 0.544 0.0160 *
## Otu02799_Mycosphaerellaceae 0.8621 0.3333 0.536 0.0353 *
## Otu02835_Trechispora 0.8537 0.3333 0.533 0.0212 *
## Otu00813_Phaeoisaria_clematidis 1.0000 0.2778 0.527 0.0239 *
## Otu00832_Endosporium 1.0000 0.2778 0.527 0.0223 *
## Otu00895_Lecanoromycetes 1.0000 0.2778 0.527 0.0213 *
## Otu00910_Ascomycota 1.0000 0.2778 0.527 0.0221 *
## Otu00934_Helotiales 1.0000 0.2778 0.527 0.0197 *
## Otu01145_Ascomycota 1.0000 0.2778 0.527 0.0191 *
## Otu01165_Orbiliaceae 1.0000 0.2778 0.527 0.0144 *
## Otu01169_Basidiomycota 1.0000 0.2778 0.527 0.0216 *
## Otu01218_Ascomycota 1.0000 0.2778 0.527 0.0196 *
## Otu01457_Ascomycota 1.0000 0.2778 0.527 0.0191 *
## Otu01501_Drechslera 1.0000 0.2778 0.527 0.0212 *
## Otu01561_Ascomycota 1.0000 0.2778 0.527 0.0204 *
## Otu01623_Mycosphaerellaceae 1.0000 0.2778 0.527 0.0143 *
## Otu01681_Basidiomycota 1.0000 0.2778 0.527 0.0205 *
## Otu01772_Pleosporales 1.0000 0.2778 0.527 0.0192 *
## Otu02083_Glomus_compressum 1.0000 0.2778 0.527 0.0203 *
## Otu02127_Ascomycota 1.0000 0.2778 0.527 0.0229 *
## Otu02231_Chaetothyriales 1.0000 0.2778 0.527 0.0225 *
## Otu02276_Orbiliomycetes 1.0000 0.2778 0.527 0.0198 *
## Otu02288_Tremellomycetes 1.0000 0.2778 0.527 0.0198 *
## Otu02424_Trichomeriaceae 1.0000 0.2778 0.527 0.0202 *
## Otu02468_Chaetothyriales 1.0000 0.2778 0.527 0.0169 *
## Otu02514_Xylariales 1.0000 0.2778 0.527 0.0190 *
## Otu02535_Setophoma_chromolaenae 1.0000 0.2778 0.527 0.0238 *
## Otu02575_Bifiguratus 1.0000 0.2778 0.527 0.0192 *
## Otu02651_Monoblepharidomycetes 1.0000 0.2778 0.527 0.0204 *
## Otu02725_Agaricus_campestris 1.0000 0.2778 0.527 0.0189 *
## Otu02777_Pleosporales 1.0000 0.2778 0.527 0.0175 *
## Otu02993_Trichomeriaceae 1.0000 0.2778 0.527 0.0188 *
## Otu03046_Ascomycota 1.0000 0.2778 0.527 0.0177 *
## Otu03071_Sordariomycetes 1.0000 0.2778 0.527 0.0184 *
## Otu03103_Endogonales 1.0000 0.2778 0.527 0.0194 *
## Otu03121_Ascomycota 1.0000 0.2778 0.527 0.0198 *
## Otu03277_Capnodiales 1.0000 0.2778 0.527 0.0156 *
## Otu03334_Diaporthe 1.0000 0.2778 0.527 0.0197 *
## Otu03389_Tylospora 1.0000 0.2778 0.527 0.0181 *
## Otu03619_Pseudorobillarda 1.0000 0.2778 0.527 0.0207 *
## Otu04117_Hannaella_kunmingensis 1.0000 0.2778 0.527 0.0184 *
## Otu04163_Ophiognomonia 1.0000 0.2778 0.527 0.0225 *
## Otu04511_Septobasidium 1.0000 0.2778 0.527 0.0224 *
## Otu04616_Ascomycota 1.0000 0.2778 0.527 0.0229 *
## Otu00221_Cordycipitaceae 0.9985 0.2778 0.527 0.0334 *
## Otu00744_Talaromyces_wortmannii 0.9944 0.2778 0.526 0.0355 *
## Otu02732_Ascomycota 0.8252 0.3333 0.524 0.0395 *
## Otu00720_Ascomycota 0.9889 0.2778 0.524 0.0494 *
## Otu01235_Ascomycota 0.9845 0.2778 0.523 0.0384 *
## Otu01776_Ochroconis_icarus 0.8163 0.3333 0.522 0.0458 *
## Otu02573_Ascomycota 0.8163 0.3333 0.522 0.0289 *
## Otu01471_Onygenales 0.9771 0.2778 0.521 0.0386 *
## Otu02153_Ganoderma 0.8140 0.3333 0.521 0.0414 *
## Otu01654_Sphaceloma 0.9716 0.2778 0.519 0.0467 *
## Otu02152_Agaricomycetes 0.9603 0.2778 0.516 0.0394 *
## Otu02115_Ascomycota 0.9483 0.2778 0.513 0.0335 *
## Otu02376_Biatriospora_mackinnonii 0.9406 0.2778 0.511 0.0260 *
## Otu02665_Pleosporales 0.9406 0.2778 0.511 0.0351 *
## Otu01864_Pleosporales 0.9358 0.2778 0.510 0.0447 *
## Otu02672_Spizellomyces_dolichospermus 0.9259 0.2778 0.507 0.0314 *
## Otu02829_Aspergillus 0.9259 0.2778 0.507 0.0327 *
## Otu03049_Ascomycota 0.9155 0.2778 0.504 0.0331 *
## Otu02722_Glomeraceae 0.9143 0.2778 0.504 0.0325 *
## Otu02074_Pleosporales 0.9124 0.2778 0.503 0.0433 *
## Otu02987_Suillus 0.9016 0.2778 0.500 0.0354 *
## Otu03325_Ascomycota 0.8966 0.2778 0.499 0.0454 *
## Otu02824_Rozellomycota 0.8929 0.2778 0.498 0.0375 *
## Otu03068_Glomeraceae 0.8929 0.2778 0.498 0.0368 *
## Otu01025_GS10 0.8907 0.2778 0.497 0.0477 *
## Otu02261_Farlowiella_carmichaeliana 0.8824 0.2778 0.495 0.0391 *
## Otu02960_Phialemoniopsis 0.8824 0.2778 0.495 0.0397 *
## Otu03539_Chionosphaeraceae 0.8824 0.2778 0.495 0.0480 *
## Otu00991_Mortierella 0.8806 0.2778 0.495 0.0358 *
## Otu02471_Thanatephorus_cucumeris 0.8763 0.2778 0.493 0.0450 *
## Otu02443_Diaporthales 0.8696 0.2778 0.491 0.0471 *
## Otu01114_Pleosporales 1.0000 0.2222 0.471 0.0474 *
## Otu01173_Hypocreales_fam_Incertae_sedis 1.0000 0.2222 0.471 0.0448 *
## Otu01240_Spizellomycetales 1.0000 0.2222 0.471 0.0480 *
## Otu01605_Pleosporales 1.0000 0.2222 0.471 0.0428 *
## Otu01670_Ascomycota 1.0000 0.2222 0.471 0.0463 *
## Otu01878_Branch06 1.0000 0.2222 0.471 0.0465 *
## Otu02009_Ascomycota 1.0000 0.2222 0.471 0.0468 *
## Otu02674_Branch06 1.0000 0.2222 0.471 0.0497 *
## Otu02802_Lasiodiplodia 1.0000 0.2222 0.471 0.0486 *
## Otu02834_Ascomycota 1.0000 0.2222 0.471 0.0382 *
## Otu02889_Pleosporales 1.0000 0.2222 0.471 0.0450 *
## Otu02911_Claroideoglomeraceae 1.0000 0.2222 0.471 0.0457 *
## Otu03006_Ascomycota 1.0000 0.2222 0.471 0.0304 *
## Otu03029_Eucasphaeria 1.0000 0.2222 0.471 0.0464 *
## Otu03160_Ascomycota 1.0000 0.2222 0.471 0.0440 *
## Otu03242_Microbotryomycetes 1.0000 0.2222 0.471 0.0434 *
## Otu03248_Brevicellicium_olivascens 1.0000 0.2222 0.471 0.0384 *
## Otu03250_Ascomycota 1.0000 0.2222 0.471 0.0470 *
## Otu03271_Clavicipitaceae 1.0000 0.2222 0.471 0.0463 *
## Otu03699_Ascomycota 1.0000 0.2222 0.471 0.0326 *
## Otu03737_Zasmidium 1.0000 0.2222 0.471 0.0355 *
## Otu03754_Lecanoromycetes 1.0000 0.2222 0.471 0.0433 *
## Otu04263_Sordariomycetes 1.0000 0.2222 0.471 0.0333 *
## Otu04302_Sordariomycetes 1.0000 0.2222 0.471 0.0356 *
## Otu04334_Ascomycota 1.0000 0.2222 0.471 0.0322 *
## Otu04380_Clathraceae 1.0000 0.2222 0.471 0.0303 *
## Otu04610_Ascomycota 1.0000 0.2222 0.471 0.0335 *
## Otu04738_Cryptococcus_cuniculi 1.0000 0.2222 0.471 0.0369 *
##
## Group WA #sps. 434
## A B stat p.value
## Otu00162_Pleosporales 0.9609 1.0000 0.980 1e-04 ***
## Otu00364_Ascomycota 0.9961 0.9167 0.956 1e-04 ***
## Otu00769_Naganishia_randhawae 0.9951 0.9167 0.955 1e-04 ***
## Otu00060_Gibellulopsis_piscis 0.9914 0.9167 0.953 1e-04 ***
## Otu00861_Mortierella_alpina 0.9880 0.9167 0.952 1e-04 ***
## Otu00149_Lophiotrema_rubi 0.9858 0.9167 0.951 1e-04 ***
## Otu00157_Alternaria_subcucurbitae 0.9807 0.9167 0.948 1e-04 ***
## Otu00308_Cystofilobasidium_infirmominiatum 0.9791 0.9167 0.947 1e-04 ***
## Otu00147_Stachybotryaceae 0.9732 0.9167 0.945 1e-04 ***
## Otu00026_Pleosporales 0.9730 0.9167 0.944 1e-04 ***
## Otu01094_Microascales 0.9660 0.9167 0.941 1e-04 ***
## Otu00413_Fusarium_redolens 0.9568 0.9167 0.936 1e-04 ***
## Otu00016_Penicillium_neocrassum 0.9522 0.9167 0.934 0.0165 *
## Otu01358_Mortierella 0.9502 0.9167 0.933 1e-04 ***
## Otu00664_Oidiodendron_truncatum 0.9416 0.9167 0.929 1e-04 ***
## Otu00758_Holtermanniella_takashimae 0.8608 1.0000 0.928 1e-04 ***
## Otu00332_Tetracladium 0.9369 0.9167 0.927 1e-04 ***
## Otu01276_Syncephalis 0.9344 0.9167 0.926 1e-04 ***
## Otu00713_Alternaria_metachromatica 0.9222 0.9167 0.919 1e-04 ***
## Otu00023_Mortierella_alpina 0.9219 0.9167 0.919 0.0008 ***
## Otu00048_Xylariales 0.9148 0.9167 0.916 1e-04 ***
## Otu00787_Naganishia_albida 0.9137 0.9167 0.915 1e-04 ***
## Otu00698_Dendryphion 0.9082 0.9167 0.912 1e-04 ***
## Otu00163_Ascomycota 0.9937 0.8333 0.910 1e-04 ***
## Otu00038_Penicillium_bialowiezense 0.9935 0.8333 0.910 1e-04 ***
## Otu00822_Gymnoascus_reessii 0.8984 0.9167 0.907 1e-04 ***
## Otu00342_Sordariomycetes 0.9882 0.8333 0.907 1e-04 ***
## Otu00430_Chytridiomycota 0.8977 0.9167 0.907 1e-04 ***
## Otu00527_Chrysosporium_pseudomerdarium 0.9809 0.8333 0.904 1e-04 ***
## Otu00526_Nectriaceae 0.9787 0.8333 0.903 1e-04 ***
## Otu00071_Penicillium_polonicum 0.8102 1.0000 0.900 0.0055 **
## Otu00367_Chaetosphaeria 0.8020 1.0000 0.896 0.0009 ***
## Otu00738_Ustilago_hordei 0.8747 0.9167 0.895 1e-04 ***
## Otu00164_Cystofilobasidium_capitatum 0.8705 0.9167 0.893 1e-04 ***
## Otu00849_Sarocladium_kiliense 0.9526 0.8333 0.891 1e-04 ***
## Otu00356_Chytridiomycota 0.9526 0.8333 0.891 1e-04 ***
## Otu01033_Pseudeurotiaceae 0.9476 0.8333 0.889 1e-04 ***
## Otu00518_Filobasidiales 0.8614 0.9167 0.889 1e-04 ***
## Otu00210_Melanommataceae 0.8534 0.9167 0.884 1e-04 ***
## Otu00354_Cephalotrichum_asperulum 0.9380 0.8333 0.884 1e-04 ***
## Otu00759_Pleosporales 0.9256 0.8333 0.878 1e-04 ***
## Otu00446_Acrostalagmus_luteoalbus 0.9152 0.8333 0.873 1e-04 ***
## Otu00665_Pleurotheciella 0.8291 0.9167 0.872 0.0002 ***
## Otu01485_Thelonectria 0.8201 0.9167 0.867 1e-04 ***
## Otu00546_Peziza_buxea 1.0000 0.7500 0.866 1e-04 ***
## Otu01000_Sordariomycetes 1.0000 0.7500 0.866 1e-04 ***
## Otu00340_Paurocotylis 0.9966 0.7500 0.865 1e-04 ***
## Otu00393_Trichoderma 0.8150 0.9167 0.864 1e-04 ***
## Otu00693_Rozellomycota 0.9954 0.7500 0.864 1e-04 ***
## Otu00921_Mortierella_alpina 0.9946 0.7500 0.864 1e-04 ***
## Otu00120_Apodus_deciduus 0.8923 0.8333 0.862 0.0002 ***
## Otu00456_Metarhizium_marquandii 0.8064 0.9167 0.860 1e-04 ***
## Otu01070_Cephalotrichum 0.9843 0.7500 0.859 1e-04 ***
## Otu00371_Chrysosporium_lobatum 0.9706 0.7500 0.853 1e-04 ***
## Otu01016_Helotiales 0.9690 0.7500 0.852 1e-04 ***
## Otu00212_Ascomycota 0.9679 0.7500 0.852 0.0003 ***
## Otu00172_Chrysosporium_pseudomerdarium 0.8642 0.8333 0.849 0.0104 *
## Otu00505_Tetracladium 0.7855 0.9167 0.849 0.0002 ***
## Otu00525_Chaetothyriales 0.9578 0.7500 0.848 1e-04 ***
## Otu00452_Ascomycota 0.7795 0.9167 0.845 0.0002 ***
## Otu01020_Tranzscheliella_yupeitaniae 0.9504 0.7500 0.844 1e-04 ***
## Otu00548_Hypocreales 0.8533 0.8333 0.843 1e-04 ***
## Otu01216_Stachybotrys_chartarum 0.9432 0.7500 0.841 1e-04 ***
## Otu00139_Operculomyces_laminatus 0.8447 0.8333 0.839 0.0002 ***
## Otu00523_Coniochaeta 0.7634 0.9167 0.837 0.0002 ***
## Otu00130_Pyrenochaeta 0.7624 0.9167 0.836 1e-04 ***
## Otu00415_Leohumicola 0.9269 0.7500 0.834 0.0003 ***
## Otu00541_Chaetomiaceae 0.9261 0.7500 0.833 1e-04 ***
## Otu00295_Plectosphaerellaceae 0.8278 0.8333 0.831 0.0003 ***
## Otu01140_Phaeosphaeriaceae 0.8974 0.7500 0.820 1e-04 ***
## Otu01781_Tranzscheliella 0.8947 0.7500 0.819 1e-04 ***
## Otu00818_Clonostachys 1.0000 0.6667 0.816 1e-04 ***
## Otu01582_Fusarium_solani 1.0000 0.6667 0.816 1e-04 ***
## Otu02327_Onygenales 1.0000 0.6667 0.816 1e-04 ***
## Otu00559_Ascomycota 0.9975 0.6667 0.815 1e-04 ***
## Otu00705_Chaetothyriales 0.9956 0.6667 0.815 1e-04 ***
## Otu00451_Ascomycota 0.8844 0.7500 0.814 1e-04 ***
## Otu00629_Mortierellomycota 0.9919 0.6667 0.813 1e-04 ***
## Otu00514_Pyrenochaeta 0.9859 0.6667 0.811 1e-04 ***
## Otu01234_Arachnomyces 0.7878 0.8333 0.810 1e-04 ***
## Otu01204_Rozellomycota 0.9568 0.6667 0.799 1e-04 ***
## Otu01906_Buckleyzyma_aurantiaca 0.9459 0.6667 0.794 1e-04 ***
## Otu00115_Lipomyces_lipofer 0.9207 0.6667 0.783 0.0003 ***
## Otu00436_Auriculariales 0.7338 0.8333 0.782 0.0009 ***
## Otu01523_Capnodiales 0.9067 0.6667 0.777 1e-04 ***
## Otu00804_Gymnoascaceae 0.7221 0.8333 0.776 0.0005 ***
## Otu01400_Leucosphaerina 0.9011 0.6667 0.775 1e-04 ***
## Otu01251_Clavatospora_longibrachiata 0.7207 0.8333 0.775 1e-04 ***
## Otu00034_Sordariales 0.8996 0.6667 0.774 0.0040 **
## Otu00700_Cladophialophora_chaetospira 0.7982 0.7500 0.774 0.0073 **
## Otu01026_Metarhizium_marquandii 0.7977 0.7500 0.773 1e-04 ***
## Otu00887_Chrysosporium_lobatum 0.7143 0.8333 0.772 0.0006 ***
## Otu00333_Gymnostellatospora_japonica 0.8851 0.6667 0.768 0.0008 ***
## Otu00146_Subulicystidium_perlongisporum 1.0000 0.5833 0.764 1e-04 ***
## Otu00374_Cladophialophora 1.0000 0.5833 0.764 1e-04 ***
## Otu00529_Eurotiomycetes 1.0000 0.5833 0.764 1e-04 ***
## Otu00815_Monoblepharidomycetes 1.0000 0.5833 0.764 1e-04 ***
## Otu01062_Coprinopsis_candidolanata 1.0000 0.5833 0.764 1e-04 ***
## Otu01412_Herpotrichiellaceae 1.0000 0.5833 0.764 1e-04 ***
## Otu02173_Leptosphaeria 1.0000 0.5833 0.764 1e-04 ***
## Otu02444_Absidia_pseudocylindrospora 1.0000 0.5833 0.764 1e-04 ***
## Otu00280_Chaetothyriales 0.9973 0.5833 0.763 1e-04 ***
## Otu00076_Leotiomycetes 0.9920 0.5833 0.761 0.0008 ***
## Otu01150_Ascomycota 0.9909 0.5833 0.760 1e-04 ***
## Otu00618_Auriculariales 0.9854 0.5833 0.758 1e-04 ***
## Otu00650_Coniochaeta_ligniaria 0.8592 0.6667 0.757 0.0008 ***
## Otu00600_Monocillium 0.6234 0.9167 0.756 0.0120 *
## Otu00182_Chaetomium 0.9767 0.5833 0.755 0.0004 ***
## Otu00784_Acremonium_fusidioides 0.7589 0.7500 0.754 0.0197 *
## Otu01247_Alfaria 0.9744 0.5833 0.754 1e-04 ***
## Otu00933_Pleosporales 0.8521 0.6667 0.754 0.0037 **
## Otu01007_Mortierella 0.7547 0.7500 0.752 0.0006 ***
## Otu00502_Paraphaeosphaeria 0.9689 0.5833 0.752 0.0006 ***
## Otu00506_Trichoderma 0.9683 0.5833 0.752 0.0008 ***
## Otu00466_Rhizophlyctis_rosea 0.8467 0.6667 0.751 0.0002 ***
## Otu01057_Phialemonium_inflatum 0.9595 0.5833 0.748 0.0005 ***
## Otu00405_Herpotrichiellaceae 0.9570 0.5833 0.747 0.0006 ***
## Otu01512_Ascomycota 0.8261 0.6667 0.742 0.0004 ***
## Otu01017_Dactylella_arnaudii 0.9420 0.5833 0.741 0.0002 ***
## Otu00563_Schizothecium_glutinans 0.9413 0.5833 0.741 0.0015 **
## Otu00870_Schizothecium_dakotense 0.8218 0.6667 0.740 0.0012 **
## Otu01195_Pseudeurotiaceae 0.7143 0.7500 0.732 0.0006 ***
## Otu02450_Rhizophagus_irregularis 0.9184 0.5833 0.732 0.0003 ***
## Otu00437_Microdochium 0.6354 0.8333 0.728 0.0042 **
## Otu00819_Ophiocordycipitaceae 0.8867 0.5833 0.719 0.0116 *
## Otu00568_Helotiales_fam_Incertae_sedis 0.8852 0.5833 0.719 1e-04 ***
## Otu01584_Sordariomycetes 0.8789 0.5833 0.716 0.0007 ***
## Otu00156_Aspergillaceae 0.6824 0.7500 0.715 0.0014 **
## Otu00256_Powellomyces_hirtus 0.8704 0.5833 0.713 0.0015 **
## Otu00195_Ascomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu00339_Podospora 1.0000 0.5000 0.707 1e-04 ***
## Otu00657_Gymnoascaceae 1.0000 0.5000 0.707 1e-04 ***
## Otu00711_Ascomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu00714_Eurotiomycetes 1.0000 0.5000 0.707 1e-04 ***
## Otu00856_Pleosporales 1.0000 0.5000 0.707 1e-04 ***
## Otu00907_Chaetosphaeria 1.0000 0.5000 0.707 0.0002 ***
## Otu00938_Corynespora 1.0000 0.5000 0.707 1e-04 ***
## Otu01012_Operculomyces_laminatus 1.0000 0.5000 0.707 1e-04 ***
## Otu01168_Myxotrichaceae 1.0000 0.5000 0.707 1e-04 ***
## Otu01271_Taphrinales 1.0000 0.5000 0.707 1e-04 ***
## Otu01377_Penicillium 1.0000 0.5000 0.707 1e-04 ***
## Otu01447_Ascomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu01585_Trechispora 1.0000 0.5000 0.707 1e-04 ***
## Otu01586_Basidiomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu01601_Ascomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu01629_Ajellomycetaceae 1.0000 0.5000 0.707 1e-04 ***
## Otu01790_Ascomycota 1.0000 0.5000 0.707 1e-04 ***
## Otu01975_Monoblepharidomycetes 1.0000 0.5000 0.707 1e-04 ***
## Otu02050_Eurotiomycetes 1.0000 0.5000 0.707 1e-04 ***
## Otu02081_Gibellulopsis_piscis 1.0000 0.5000 0.707 1e-04 ***
## Otu02470_Chionosphaeraceae 1.0000 0.5000 0.707 0.0004 ***
## Otu02525_Oidiodendron_griseum 1.0000 0.5000 0.707 1e-04 ***
## Otu02594_Glarea 1.0000 0.5000 0.707 0.0002 ***
## Otu02625_Pezizaceae 1.0000 0.5000 0.707 0.0003 ***
## Otu02956_Pleurophoma 1.0000 0.5000 0.707 0.0006 ***
## Otu00383_Davidhawksworthia 0.9980 0.5000 0.706 0.0002 ***
## Otu00090_Ascomycota 0.9978 0.5000 0.706 0.0002 ***
## Otu00736_Ochroconis 0.7471 0.6667 0.706 0.0004 ***
## Otu00786_Leotiomycetes 0.9947 0.5000 0.705 1e-04 ***
## Otu00056_Sordariomycetes 0.9939 0.5000 0.705 0.0030 **
## Otu00270_Sordariales 0.9847 0.5000 0.702 0.0011 **
## Otu01191_Exophiala 0.8430 0.5833 0.701 0.0013 **
## Otu00774_Auxarthron_umbrinum 0.9806 0.5000 0.700 1e-04 ***
## Otu00963_Subulicystidium_perlongisporum 0.9804 0.5000 0.700 0.0003 ***
## Otu00041_Psathyrella 0.9760 0.5000 0.699 0.0036 **
## Otu00619_Subulicystidium_perlongisporum 0.9755 0.5000 0.698 0.0009 ***
## Otu01976_Pleosporales 0.8333 0.5833 0.697 0.0006 ***
## Otu01527_Geminibasidium_hirsutum 0.9641 0.5000 0.694 0.0003 ***
## Otu00261_Trichoderma 0.9608 0.5000 0.693 0.0132 *
## Otu00240_Cistella 0.9606 0.5000 0.693 0.0153 *
## Otu00465_Pleosporales 0.9604 0.5000 0.693 0.0007 ***
## Otu01224_Podospora 0.9569 0.5000 0.692 0.0011 **
## Otu02836_Penicillium_parvulum 0.9512 0.5000 0.690 0.0004 ***
## Otu01334_Rhizophagus_irregularis 0.7079 0.6667 0.687 0.0230 *
## Otu01161_Ascomycota 0.9410 0.5000 0.686 0.0006 ***
## Otu00543_Acaulium_acremonium 0.9345 0.5000 0.684 0.0029 **
## Otu01828_Rhodosporidiobolus_colostri 0.7965 0.5833 0.682 0.0021 **
## Otu01610_Vishniacozyma_dimennae 0.6908 0.6667 0.679 0.0015 **
## Otu02619_Sordariomycetes 0.9184 0.5000 0.678 0.0008 ***
## Otu00595_Metarhizium_marquandii 0.7872 0.5833 0.678 0.0257 *
## Otu01904_Basidiomycota 0.9124 0.5000 0.675 0.0014 **
## Otu01874_Rhizophagus_irregularis 0.7692 0.5833 0.670 0.0055 **
## Otu01705_Ramicandelaber_taiwanensis 0.8710 0.5000 0.660 0.0009 ***
## Otu01543_Eurotiomycetes 0.7464 0.5833 0.660 0.0075 **
## Otu00658_Mortierella 0.6522 0.6667 0.659 0.0268 *
## Otu00929_Ochroconis_tshawytschae 0.7371 0.5833 0.656 0.0103 *
## Otu00235_Chaetothyriales 1.0000 0.4167 0.645 0.0007 ***
## Otu00591_Ascomycota 1.0000 0.4167 0.645 0.0011 **
## Otu00680_Rozellomycota 1.0000 0.4167 0.645 0.0009 ***
## Otu00703_Spizellomyces 1.0000 0.4167 0.645 0.0004 ***
## Otu00768_Ascomycota 1.0000 0.4167 0.645 0.0011 **
## Otu00824_Eurotiomycetes 1.0000 0.4167 0.645 0.0007 ***
## Otu00905_Chrysozymaceae 1.0000 0.4167 0.645 0.0004 ***
## Otu00924_Didymellaceae 1.0000 0.4167 0.645 0.0005 ***
## Otu01143_Mortierellomycota 1.0000 0.4167 0.645 0.0007 ***
## Otu01156_Mortierella 1.0000 0.4167 0.645 0.0010 ***
## Otu01183_Chaetothyriales 1.0000 0.4167 0.645 0.0008 ***
## Otu01290_Leotiomycetes 1.0000 0.4167 0.645 0.0004 ***
## Otu01437_Pleosporales 1.0000 0.4167 0.645 0.0010 ***
## Otu01599_Ascomycota 1.0000 0.4167 0.645 0.0007 ***
## Otu01600_Penicillium 1.0000 0.4167 0.645 0.0008 ***
## Otu01669_Glomus_macrocarpum 1.0000 0.4167 0.645 0.0008 ***
## Otu01696_Preussia_terricola 0.8333 0.5000 0.645 0.0027 **
## Otu01957_Diutina_catenulata 1.0000 0.4167 0.645 0.0005 ***
## Otu01985_Ajellomycetaceae 1.0000 0.4167 0.645 0.0006 ***
## Otu02130_Schizothecium 1.0000 0.4167 0.645 0.0005 ***
## Otu02146_Eurotiomycetes 1.0000 0.4167 0.645 0.0007 ***
## Otu02172_Stemphylium 1.0000 0.4167 0.645 0.0013 **
## Otu02374_Helotiaceae 1.0000 0.4167 0.645 0.0014 **
## Otu02398_Leotiomycetes 1.0000 0.4167 0.645 0.0005 ***
## Otu02411_Orbilia_auricolor 1.0000 0.4167 0.645 0.0009 ***
## Otu02414_Kluyveromyces_lactis 1.0000 0.4167 0.645 0.0006 ***
## Otu02477_Clavatospora_longibrachiata 1.0000 0.4167 0.645 0.0007 ***
## Otu02500_Solicoccozyma_aeria 1.0000 0.4167 0.645 0.0007 ***
## Otu02545_Phallus_hadriani 1.0000 0.4167 0.645 0.0008 ***
## Otu02553_Basidiomycota 1.0000 0.4167 0.645 0.0007 ***
## Otu02837_Schizothecium_inaequale 1.0000 0.4167 0.645 0.0007 ***
## Otu02867_Eurotiomycetes 1.0000 0.4167 0.645 0.0007 ***
## Otu02914_Pholiota_flavida 1.0000 0.4167 0.645 0.0012 **
## Otu02981_Chrysozymaceae 1.0000 0.4167 0.645 0.0003 ***
## Otu03178_Chrysosporium_pseudomerdarium 1.0000 0.4167 0.645 0.0006 ***
## Otu03189_Filobasidium 1.0000 0.4167 0.645 0.0004 ***
## Otu03225_Solicoccozyma 1.0000 0.4167 0.645 0.0008 ***
## Otu00477_Rhizopogon_mohelnensis 0.9976 0.4167 0.645 0.0004 ***
## Otu00677_Chaetothyriales 0.9962 0.4167 0.644 0.0008 ***
## Otu00534_Aspergillaceae 0.9949 0.4167 0.644 0.0015 **
## Otu01148_Coprinellus 0.9880 0.4167 0.642 0.0008 ***
## Otu01406_Hypocreales 0.9854 0.4167 0.641 0.0011 **
## Otu00972_Ascomycota 0.9833 0.4167 0.640 0.0007 ***
## Otu01110_Talaromyces_helicus 0.9809 0.4167 0.639 0.0006 ***
## Otu00208_Ascomycota 0.9807 0.4167 0.639 0.0087 **
## Otu00449_Ascomycota 0.9739 0.4167 0.637 0.0048 **
## Otu01071_Auriculariales 0.9735 0.4167 0.637 0.0009 ***
## Otu01038_Auriculariales 0.9732 0.4167 0.637 0.0038 **
## Otu01136_Coniochaetaceae 0.8072 0.5000 0.635 0.0023 **
## Otu01311_Xylariales 0.7962 0.5000 0.631 0.0022 **
## Otu01613_Mucor_circinelloides 0.9500 0.4167 0.629 0.0030 **
## Otu00522_Mortierella_fimbricystis 0.9405 0.4167 0.626 0.0305 *
## Otu01399_Fusarium_redolens 0.6716 0.5833 0.626 0.0083 **
## Otu02339_Solicoccozyma 0.6667 0.5833 0.624 0.0052 **
## Otu02229_Chaetothyriales 0.9322 0.4167 0.623 0.0037 **
## Otu01720_GS07 0.9184 0.4167 0.619 0.0054 **
## Otu02848_Papiliotrema_terrestris 0.9130 0.4167 0.617 0.0016 **
## Otu01188_Chrysozymaceae 0.9079 0.4167 0.615 0.0029 **
## Otu01627_Pulvinula 0.9011 0.4167 0.613 0.0064 **
## Otu02361_Filobasidium_wieringae 0.7453 0.5000 0.610 0.0024 **
## Otu01277_Sordariomycetes 0.8916 0.4167 0.609 0.0153 *
## Otu00454_Rhizoctonia_fusispora 0.8797 0.4167 0.605 0.0100 **
## Otu00888_Parasola 0.8785 0.4167 0.605 0.0152 *
## Otu01982_Mucor_racemosus 0.8710 0.4167 0.602 0.0059 **
## Otu01588_Mortierella_fimbricystis 0.7232 0.5000 0.601 0.0074 **
## Otu01529_Xylariales 0.8560 0.4167 0.597 0.0068 **
## Otu01349_Cystobasidiomycetes 0.8540 0.4167 0.597 0.0238 *
## Otu02804_Coprinus_cordisporus 0.8537 0.4167 0.596 0.0041 **
## Otu02574_Penicillium 0.7087 0.5000 0.595 0.0148 *
## Otu01147_Hormonema_viticola 0.5243 0.6667 0.591 0.0321 *
## Otu01777_Dokmaia 0.8333 0.4167 0.589 0.0034 **
## Otu00721_Spizellomyces 0.8184 0.4167 0.584 0.0156 *
## Otu03341_Glomus 0.8140 0.4167 0.582 0.0035 **
## Otu02230_Coniochaeta_cateniformis 0.8000 0.4167 0.577 0.0096 **
## Otu00238_Ascomycota 1.0000 0.3333 0.577 0.0035 **
## Otu00369_Conocybe_juniana 1.0000 0.3333 0.577 0.0035 **
## Otu00584_Clonostachys 1.0000 0.3333 0.577 0.0035 **
## Otu00669_Saitozyma_flava 1.0000 0.3333 0.577 0.0036 **
## Otu00709_Podospora 1.0000 0.3333 0.577 0.0035 **
## Otu00798_Pleosporales 1.0000 0.3333 0.577 0.0035 **
## Otu00859_Cryptovalsa_ampelina 1.0000 0.3333 0.577 0.0030 **
## Otu00868_Ascomycota 1.0000 0.3333 0.577 0.0035 **
## Otu00918_Aspergillaceae 1.0000 0.3333 0.577 0.0037 **
## Otu01348_Ascomycota 1.0000 0.3333 0.577 0.0035 **
## Otu01376_Coprinellus 1.0000 0.3333 0.577 0.0037 **
## Otu01450_Ascomycota 1.0000 0.3333 0.577 0.0042 **
## Otu01622_Myrmecridium 1.0000 0.3333 0.577 0.0035 **
## Otu01748_Ascomycota 1.0000 0.3333 0.577 0.0030 **
## Otu01855_Fusarium_solani 1.0000 0.3333 0.577 0.0037 **
## Otu01877_Pleurophoma 1.0000 0.3333 0.577 0.0041 **
## Otu01923_Ramicandelaber 1.0000 0.3333 0.577 0.0032 **
## Otu02052_Endogonales 1.0000 0.3333 0.577 0.0043 **
## Otu02075_Plectosphaerellaceae 1.0000 0.3333 0.577 0.0035 **
## Otu02141_Chrysosporium_carmichaelii 1.0000 0.3333 0.577 0.0035 **
## Otu02365_Mortierella_dichotoma 1.0000 0.3333 0.577 0.0051 **
## Otu02390_Ascomycota 1.0000 0.3333 0.577 0.0035 **
## Otu02393_Ascomycota 1.0000 0.3333 0.577 0.0027 **
## Otu02425_Orbiliomycetes 1.0000 0.3333 0.577 0.0044 **
## Otu02476_Basidioascus_undulatus 1.0000 0.3333 0.577 0.0044 **
## Otu02512_Microascaceae 1.0000 0.3333 0.577 0.0035 **
## Otu02536_Phaeosphaeriaceae 1.0000 0.3333 0.577 0.0042 **
## Otu02560_Mortierella_beljakovae 1.0000 0.3333 0.577 0.0031 **
## Otu02616_Diversisporales 1.0000 0.3333 0.577 0.0032 **
## Otu02644_Eurotiomycetes 1.0000 0.3333 0.577 0.0035 **
## Otu02735_Phialemonium 1.0000 0.3333 0.577 0.0037 **
## Otu02743_Thyridariaceae 1.0000 0.3333 0.577 0.0039 **
## Otu02757_Onygenales 1.0000 0.3333 0.577 0.0028 **
## Otu02767_Ascomycota 1.0000 0.3333 0.577 0.0029 **
## Otu02842_Sordariomycetes 1.0000 0.3333 0.577 0.0028 **
## Otu02855_Sordariales 1.0000 0.3333 0.577 0.0042 **
## Otu02924_Chaetomiaceae 1.0000 0.3333 0.577 0.0044 **
## Otu02950_Mortierellomycota 1.0000 0.3333 0.577 0.0042 **
## Otu03011_Verrucariaceae 1.0000 0.3333 0.577 0.0037 **
## Otu03301_Cystobasidiomycetes 1.0000 0.3333 0.577 0.0037 **
## Otu03464_Absidia_pseudocylindrospora 1.0000 0.3333 0.577 0.0038 **
## Otu03520_Rhizophydiales 1.0000 0.3333 0.577 0.0041 **
## Otu03526_Lentitheciaceae 1.0000 0.3333 0.577 0.0028 **
## Otu03569_Cercophora_samala 1.0000 0.3333 0.577 0.0046 **
## Otu03643_Microascaceae 1.0000 0.3333 0.577 0.0035 **
## Otu03827_Chaetomiaceae 1.0000 0.3333 0.577 0.0035 **
## Otu03863_Tetracladium 1.0000 0.3333 0.577 0.0037 **
## Otu03870_Microascaceae 1.0000 0.3333 0.577 0.0034 **
## Otu03918_Acremonium_alternatum 1.0000 0.3333 0.577 0.0044 **
## Otu03975_Suillus_lakei 1.0000 0.3333 0.577 0.0035 **
## Otu04357_Glomus_macrocarpum 1.0000 0.3333 0.577 0.0030 **
## Otu02448_Ascomycota 0.7965 0.4167 0.576 0.0089 **
## Otu01524_Ascomycota 0.9878 0.3333 0.574 0.0045 **
## Otu00975_Ascomycota 0.9847 0.3333 0.573 0.0076 **
## Otu01394_Ascomycota 0.9843 0.3333 0.573 0.0086 **
## Otu00816_Microascus_verrucosus 0.9770 0.3333 0.571 0.0091 **
## Otu00927_Aspergillaceae 0.9750 0.3333 0.570 0.0091 **
## Otu00483_Slopeiomyces_cylindrosporus 0.9732 0.3333 0.570 0.0253 *
## Otu01870_Clavicipitaceae 0.9701 0.3333 0.569 0.0085 **
## Otu00998_Helotiales 0.9699 0.3333 0.569 0.0078 **
## Otu00809_Lasiosphaeriaceae 0.9643 0.3333 0.567 0.0182 *
## Otu01044_Clonostachys 0.9592 0.3333 0.565 0.0277 *
## Otu02653_Trichoderma 0.9524 0.3333 0.563 0.0087 **
## Otu02465_Cetraspora_gilmorei 0.9494 0.3333 0.563 0.0082 **
## Otu01825_Rozellomycota 0.9474 0.3333 0.562 0.0140 *
## Otu03452_Acremonium_tubakii 0.7500 0.4167 0.559 0.0109 *
## Otu01193_Pluteus 0.9239 0.3333 0.555 0.0267 *
## Otu02519_Trichoderma 0.9231 0.3333 0.555 0.0081 **
## Otu02755_Herpotrichiellaceae 0.9231 0.3333 0.555 0.0100 **
## Otu01522_Oidiodendron 0.9206 0.3333 0.554 0.0362 *
## Otu03329_Lepista 0.9184 0.3333 0.553 0.0082 **
## Otu02384_Dominikia_difficilevidera 0.9140 0.3333 0.552 0.0092 **
## Otu01093_Fusarium 0.9059 0.3333 0.550 0.0491 *
## Otu01219_Hypocreales 0.9056 0.3333 0.549 0.0319 *
## Otu02239_Powellomycetaceae 0.7229 0.4167 0.549 0.0398 *
## Otu02381_Clavatospora_longibrachiata 0.8824 0.3333 0.542 0.0174 *
## Otu03583_Melanommataceae 0.8571 0.3333 0.535 0.0116 *
## Otu00596_Aspergillaceae 0.8541 0.3333 0.534 0.0133 *
## Otu01128_Cercophora 0.8435 0.3333 0.530 0.0421 *
## Otu01694_Mortierella_beljakovae 0.8182 0.3333 0.522 0.0199 *
## Otu01858_Microascaceae 0.8091 0.3333 0.519 0.0408 *
## Otu01536_Cadophora 0.8036 0.3333 0.518 0.0217 *
## Otu02521_Schizothecium 0.8025 0.3333 0.517 0.0223 *
## Otu01297_Sordariales 0.6376 0.4167 0.515 0.0275 *
## Otu01773_Aspergillus_collinsii 0.7895 0.3333 0.513 0.0301 *
## Otu03243_Pleosporales 0.7895 0.3333 0.513 0.0282 *
## Otu02809_Halosphaeriaceae 0.7812 0.3333 0.510 0.0260 *
## Otu02988_Suillus_pseudobrevipes 0.7714 0.3333 0.507 0.0269 *
## Otu02566_Helotiaceae 0.7627 0.3333 0.504 0.0378 *
## Otu00126_Chaetothyriales 1.0000 0.2500 0.500 0.0166 *
## Otu00344_Psathyrellaceae 1.0000 0.2500 0.500 0.0176 *
## Otu00384_Chytridiomycota 1.0000 0.2500 0.500 0.0151 *
## Otu00533_Sordariales 1.0000 0.2500 0.500 0.0172 *
## Otu00575_Conocybe 1.0000 0.2500 0.500 0.0169 *
## Otu00782_Mortierella_hyalina 1.0000 0.2500 0.500 0.0158 *
## Otu00836_Magnaporthaceae 1.0000 0.2500 0.500 0.0164 *
## Otu01125_Pseudorobillarda_phragmitis 1.0000 0.2500 0.500 0.0156 *
## Otu01186_GS11 1.0000 0.2500 0.500 0.0172 *
## Otu01337_Agaricaceae 1.0000 0.2500 0.500 0.0148 *
## Otu01456_Rhizophydium_globosum 1.0000 0.2500 0.500 0.0172 *
## Otu01728_Hymenoscyphus_albidus 1.0000 0.2500 0.500 0.0187 *
## Otu01792_Gaeumannomyces_radicicola 1.0000 0.2500 0.500 0.0172 *
## Otu01960_Cistella 1.0000 0.2500 0.500 0.0171 *
## Otu01986_Ascomycota 1.0000 0.2500 0.500 0.0164 *
## Otu02054_Coniothyriaceae 1.0000 0.2500 0.500 0.0161 *
## Otu02132_Chaetothyriales 1.0000 0.2500 0.500 0.0178 *
## Otu02176_Mortierella_fatshederae 1.0000 0.2500 0.500 0.0159 *
## Otu02351_Stachybotryaceae 1.0000 0.2500 0.500 0.0172 *
## Otu02422_Pleosporales 1.0000 0.2500 0.500 0.0156 *
## Otu02582_Conocybe 1.0000 0.2500 0.500 0.0137 *
## Otu02663_Agaricomycetes 1.0000 0.2500 0.500 0.0172 *
## Otu02701_Archaeorhizomycetes 1.0000 0.2500 0.500 0.0156 *
## Otu02793_Glomus_macrocarpum 1.0000 0.2500 0.500 0.0161 *
## Otu02872_GS10 1.0000 0.2500 0.500 0.0178 *
## Otu02888_Ophiosphaerella_korrae 1.0000 0.2500 0.500 0.0164 *
## Otu03188_Halomycetaceae 1.0000 0.2500 0.500 0.0158 *
## Otu03196_Protomyces_inouyei 1.0000 0.2500 0.500 0.0152 *
## Otu03233_Leptosphaeria 1.0000 0.2500 0.500 0.0161 *
## Otu03328_Torula_herbarum 1.0000 0.2500 0.500 0.0172 *
## Otu03331_Dothideales 1.0000 0.2500 0.500 0.0166 *
## Otu03365_Ascomycota 1.0000 0.2500 0.500 0.0176 *
## Otu03386_Basidiomycota 1.0000 0.2500 0.500 0.0171 *
## Otu03524_Cetraspora_gilmorei 1.0000 0.2500 0.500 0.0171 *
## Otu03544_Aspergillaceae 1.0000 0.2500 0.500 0.0164 *
## Otu03654_Xylariales 1.0000 0.2500 0.500 0.0137 *
## Otu03659_Crocicreas_epicalamia 1.0000 0.2500 0.500 0.0161 *
## Otu03669_Ascomycota 1.0000 0.2500 0.500 0.0164 *
## Otu03678_Clavicipitaceae 1.0000 0.2500 0.500 0.0164 *
## Otu03684_Clavatospora_longibrachiata 1.0000 0.2500 0.500 0.0160 *
## Otu03711_Penicillium_nodositatum 1.0000 0.2500 0.500 0.0162 *
## Otu03778_Glomus_macrocarpum 1.0000 0.2500 0.500 0.0153 *
## Otu03803_Phaeoacremonium_inflatipes 1.0000 0.2500 0.500 0.0151 *
## Otu03867_Vishniacozyma_heimaeyensis 1.0000 0.2500 0.500 0.0161 *
## Otu03900_Aspergillaceae 1.0000 0.2500 0.500 0.0161 *
## Otu03939_Dematiopleospora 1.0000 0.2500 0.500 0.0140 *
## Otu03996_Plectosphaerellaceae 1.0000 0.2500 0.500 0.0164 *
## Otu04060_Myxotrichaceae 1.0000 0.2500 0.500 0.0137 *
## Otu04062_Ascomycota 1.0000 0.2500 0.500 0.0148 *
## Otu04104_Hypocreales 1.0000 0.2500 0.500 0.0157 *
## Otu04122_Claroideoglomus 1.0000 0.2500 0.500 0.0164 *
## Otu04141_Entoloma_vindobonense 1.0000 0.2500 0.500 0.0173 *
## Otu04148_Chaetomiaceae 1.0000 0.2500 0.500 0.0172 *
## Otu04176_Onygenales 1.0000 0.2500 0.500 0.0164 *
## Otu04213_Chionosphaeraceae 1.0000 0.2500 0.500 0.0172 *
## Otu04274_Vishniacozyma_dimennae 1.0000 0.2500 0.500 0.0154 *
## Otu04279_Hypocreales 1.0000 0.2500 0.500 0.0162 *
## Otu04345_Stropharia_coronilla 1.0000 0.2500 0.500 0.0173 *
## Otu04488_Mortierella_exigua 1.0000 0.2500 0.500 0.0167 *
## Otu04513_Ascomycota 1.0000 0.2500 0.500 0.0170 *
## Otu04589_Diversisporaceae 1.0000 0.2500 0.500 0.0141 *
## Otu04725_Branch03 1.0000 0.2500 0.500 0.0162 *
## Otu04733_Clavatospora_longibrachiata 1.0000 0.2500 0.500 0.0162 *
## Otu04735_Clavariaceae 1.0000 0.2500 0.500 0.0162 *
## Otu04787_Glomus_macrocarpum 1.0000 0.2500 0.500 0.0171 *
## Otu04876_Ramicandelaber 1.0000 0.2500 0.500 0.0168 *
## Otu04895_Subulicystidium_perlongisporum 1.0000 0.2500 0.500 0.0171 *
## Otu05227_Microascales 1.0000 0.2500 0.500 0.0172 *
## Otu05263_Solicoccozyma_aeria 1.0000 0.2500 0.500 0.0137 *
## Otu05294_Glomus_macrocarpum 1.0000 0.2500 0.500 0.0156 *
## Otu05332_Septoglomus 1.0000 0.2500 0.500 0.0141 *
## Otu05538_Chrysosporium_pseudomerdarium 1.0000 0.2500 0.500 0.0171 *
## Otu05606_Penicillium_coprobium 1.0000 0.2500 0.500 0.0172 *
## Otu06419_Coniochaeta 1.0000 0.2500 0.500 0.0148 *
## Otu00640_Ascomycota 0.9853 0.2500 0.496 0.0393 *
## Otu01637_Pseudorobillarda_phragmitis 0.9817 0.2500 0.495 0.0250 *
## Otu01710_Pleosporales 0.9804 0.2500 0.495 0.0252 *
## Otu01620_Septoglomus 0.9802 0.2500 0.495 0.0213 *
## Otu01890_Preussia 0.9649 0.2500 0.491 0.0372 *
## Otu02068_Agaricomycetes 0.9615 0.2500 0.490 0.0384 *
## Otu02004_Coprinopsis 0.9574 0.2500 0.489 0.0311 *
## Otu03944_Hypocreales 0.7143 0.3333 0.488 0.0236 *
## Otu02037_Agaricomycetes 0.7025 0.3333 0.484 0.0372 *
## Otu03200_Pleosporales 0.9322 0.2500 0.483 0.0276 *
## Otu03105_Vermispora 0.9184 0.2500 0.479 0.0485 *
##
## Group IN+TN #sps. 151
## A B stat p.value
## Otu00054_Purpureocillium_lilacinum 0.9775 1.0000 0.989 1e-04 ***
## Otu00167_Fusarium_solani 0.9976 0.9697 0.984 1e-04 ***
## Otu00050_Penicillium_cainii 0.9919 0.9697 0.981 1e-04 ***
## Otu00085_Trichodermairale 0.9912 0.9697 0.980 1e-04 ***
## Otu00180_Teichosporaceae 0.9866 0.9697 0.978 1e-04 ***
## Otu00365_Calvatia_cyathiformis 0.9859 0.9697 0.978 1e-04 ***
## Otu00039_Metarhizium_marquandii 0.9553 1.0000 0.977 1e-04 ***
## Otu00002_Metarhizium_anisopliae 0.9732 0.9697 0.971 1e-04 ***
## Otu00035_Chaetomium 0.9721 0.9697 0.971 1e-04 ***
## Otu00049_Paraconiothyrium 0.9424 1.0000 0.971 1e-04 ***
## Otu00007_Chaetomiaceae 0.9947 0.9394 0.967 1e-04 ***
## Otu00540_Lycoperdon_pyriforme 0.9760 0.9394 0.958 1e-04 ***
## Otu00070_Didymellaceae 0.9718 0.9394 0.955 1e-04 ***
## Otu00096_Penicillium_brasilianum 0.9715 0.9394 0.955 1e-04 ***
## Otu00123_Fusicolla_aquaeductuum 0.9698 0.9394 0.954 1e-04 ***
## Otu00311_Periconia_macrospinosa 1.0000 0.9091 0.953 1e-04 ***
## Otu00317_Halosphaeriaceae 0.9975 0.9091 0.952 1e-04 ***
## Otu00021_Penicillium_thiersii 0.9321 0.9697 0.951 1e-04 ***
## Otu00689_Ganoderma_adspersum 0.9929 0.9091 0.950 1e-04 ***
## Otu00098_Rhizophydiales 0.9437 0.9394 0.942 1e-04 ***
## Otu00175_Penicillium 0.9701 0.9091 0.939 0.0002 ***
## Otu00249_Pyronemataceae 1.0000 0.8788 0.937 1e-04 ***
## Otu00442_Pestalotiopsis 0.9347 0.9394 0.937 1e-04 ***
## Otu00125_Pleosporales 0.9991 0.8788 0.937 1e-04 ***
## Otu00119_Idriella 0.8858 0.9697 0.927 1e-04 ***
## Otu00133_Lophiotrema_rubi 0.9335 0.9091 0.921 0.0013 **
## Otu00289_Pleosporales 0.9569 0.8788 0.917 1e-04 ***
## Otu00710_Neopestalotiopsis_foedans 0.9868 0.8485 0.915 1e-04 ***
## Otu00185_Talaromyces_trachyspermus 0.9752 0.8485 0.910 0.0002 ***
## Otu00372_Paraphoma 0.9717 0.8485 0.908 1e-04 ***
## Otu00479_Glomeraceae 0.9334 0.8788 0.906 0.0004 ***
## Otu00515_Glomeromycota 0.9771 0.8182 0.894 1e-04 ***
## Otu00704_Pleosporales 0.9929 0.7879 0.884 1e-04 ***
## Otu01028_Pleosporales 0.9880 0.7879 0.882 1e-04 ***
## Otu00388_Penicillium_sumatraense 0.9680 0.7879 0.873 0.0006 ***
## Otu00623_Veronaea_japonica 0.9614 0.7879 0.870 0.0010 ***
## Otu01019_Herpotrichiellaceae 0.9610 0.7879 0.870 1e-04 ***
## Otu00198_Ceratobasidiaceae 0.9930 0.7576 0.867 1e-04 ***
## Otu00558_Minutisphaera_aspera 0.9547 0.7879 0.867 0.0005 ***
## Otu00504_Phaeoacremonium 0.9911 0.7576 0.866 0.0004 ***
## Otu00067_Mortierella 0.9520 0.7879 0.866 0.0068 **
## Otu00598_Pleosporales 0.9896 0.7576 0.866 1e-04 ***
## Otu00243_Roussoella_solani 1.0000 0.7273 0.853 1e-04 ***
## Otu01402_Hypoxylon 1.0000 0.7273 0.853 1e-04 ***
## Otu00480_Chaetothyriaceae 0.9918 0.7273 0.849 0.0002 ***
## Otu00585_Cucurbitariaceae 0.9777 0.7273 0.843 0.0012 **
## Otu00501_Clonostachys_rosea 1.0000 0.6970 0.835 1e-04 ***
## Otu00683_Chaetothyriales 1.0000 0.6970 0.835 1e-04 ***
## Otu01144_Phialemoniopsis_ocularis 1.0000 0.6970 0.835 0.0002 ***
## Otu00298_Chaetomiaceae 0.9575 0.7273 0.834 0.0011 **
## Otu00805_Helotiales 0.9914 0.6970 0.831 0.0005 ***
## Otu00604_Hypocreales 0.9898 0.6970 0.831 0.0019 **
## Otu00722_Pseudeurotium_hygrophilum 1.0000 0.6667 0.816 0.0008 ***
## Otu00305_Cucurbitariaceae 0.9954 0.6667 0.815 0.0008 ***
## Otu00352_Pleosporales 0.9916 0.6667 0.813 0.0015 **
## Otu00316_Lophiostomataceae 0.9825 0.6667 0.809 0.0011 **
## Otu00276_Mucor_moelleri 0.8995 0.7273 0.809 0.0056 **
## Otu00474_Pleosporales 0.9804 0.6667 0.808 0.0006 ***
## Otu00931_Phaeoacremonium_scolyti 0.9763 0.6667 0.807 0.0026 **
## Otu00615_Biatriospora 0.8780 0.7273 0.799 0.0081 **
## Otu00597_Eurotiomycetes 1.0000 0.6364 0.798 0.0010 ***
## Otu00941_Phaeomoniellales 1.0000 0.6364 0.798 0.0004 ***
## Otu01032_Eurotiomycetes 1.0000 0.6364 0.798 0.0003 ***
## Otu01118_Halosphaeriaceae 1.0000 0.6364 0.798 0.0003 ***
## Otu00104_Mortierella_exigua 0.9065 0.6970 0.795 0.0074 **
## Otu00932_Pleosporales 0.9893 0.6364 0.793 0.0014 **
## Otu00420_Pleosporales 0.9887 0.6364 0.793 0.0042 **
## Otu00214_Pyrenochaetopsis 0.8925 0.6970 0.789 0.0082 **
## Otu00294_Glomus_compressum 0.9761 0.6364 0.788 0.0026 **
## Otu00641_Coniochaeta_fasciculata 0.9702 0.6364 0.786 0.0248 *
## Otu00845_Capronia_semi.immersa 0.9620 0.6364 0.782 0.0047 **
## Otu00291_Cucurbitariaceae 0.8742 0.6970 0.781 0.0145 *
## Otu00188_Sordariomycetes 1.0000 0.6061 0.778 0.0111 *
## Otu00681_Phaeomoniellales 1.0000 0.6061 0.778 0.0005 ***
## Otu00542_Glomus_indicum 0.9419 0.6364 0.774 0.0157 *
## Otu00347_Geminibasidium 0.9775 0.6061 0.770 0.0069 **
## Otu00779_Vishniacozyma_heimaeyensis 0.8801 0.6667 0.766 0.0411 *
## Otu00403_Chaetomium 1.0000 0.5758 0.759 0.0040 **
## Otu01350_Sclerotiniaceae 1.0000 0.5758 0.759 0.0020 **
## Otu01446_Rigidoporus_pouzarii 1.0000 0.5758 0.759 0.0012 **
## Otu00670_Diaporthe 0.9408 0.6061 0.755 0.0272 *
## Otu00914_Articulospora 0.8878 0.6364 0.752 0.0302 *
## Otu00827_Papiliotrema_frias 0.8844 0.6364 0.750 0.0130 *
## Otu00281_Bifiguratus 0.9718 0.5758 0.748 0.0133 *
## Otu00668_Onygenales 0.9030 0.6061 0.740 0.0195 *
## Otu00495_Agaricomycetes 1.0000 0.5455 0.739 0.0038 **
## Otu00507_Acrocalymma 1.0000 0.5455 0.739 0.0041 **
## Otu01079_Pleosporales 1.0000 0.5455 0.739 0.0027 **
## Otu00555_Mycosphaerellaceae 0.9856 0.5455 0.733 0.0097 **
## Otu01269_Tubeufiaceae 0.9256 0.5758 0.730 0.0115 *
## Otu01344_Vishniacozyma 0.8770 0.6061 0.729 0.0224 *
## Otu00642_Dothideomycetes 0.9523 0.5455 0.721 0.0196 *
## Otu00143_Cyberlindnera_sargentensis 1.0000 0.5152 0.718 0.0091 **
## Otu00335_Tetracladium 1.0000 0.5152 0.718 0.0064 **
## Otu00904_Geastrum_triplex 1.0000 0.5152 0.718 0.0114 *
## Otu01607_Coprinellus 1.0000 0.5152 0.718 0.0030 **
## Otu01668_Hypholoma_lateritium 1.0000 0.5152 0.718 0.0061 **
## Otu00141_Arthropsis_hispanica 0.9902 0.5152 0.714 0.0219 *
## Otu00682_Mariannaea 0.9631 0.5152 0.704 0.0296 *
## Otu01380_Nemania 0.9599 0.5152 0.703 0.0116 *
## Otu01018_Ascomycota 0.9476 0.5152 0.699 0.0169 *
## Otu00116_GS10 0.9472 0.5152 0.699 0.0351 *
## Otu00376_Acremoniuminosum 1.0000 0.4848 0.696 0.0168 *
## Otu01295_Coniochaetaceae 1.0000 0.4848 0.696 0.0116 *
## Otu01559_Glomus_indicum 1.0000 0.4848 0.696 0.0070 **
## Otu00303_Penicillium 0.9857 0.4848 0.691 0.0212 *
## Otu00829_Sympoventuriaceae 0.9828 0.4848 0.690 0.0257 *
## Otu00717_Glomus_compressum 0.9801 0.4848 0.689 0.0168 *
## Otu01253_Hydropisphaera 0.9795 0.4848 0.689 0.0227 *
## Otu00231_Mortierella 0.9653 0.4848 0.684 0.0382 *
## Otu00189_Didymosphaeriaceae 1.0000 0.4545 0.674 0.0239 *
## Otu00577_Onygenales 1.0000 0.4545 0.674 0.0186 *
## Otu00967_Sordariomycetes 1.0000 0.4545 0.674 0.0135 *
## Otu00981_Helotiales 1.0000 0.4545 0.674 0.0184 *
## Otu01255_Ascomycota 1.0000 0.4545 0.674 0.0128 *
## Otu01257_Diatrypaceae 1.0000 0.4545 0.674 0.0108 *
## Otu01343_Rhinocladiella 1.0000 0.4545 0.674 0.0128 *
## Otu01454_Phaeosphaeriaceae 1.0000 0.4545 0.674 0.0099 **
## Otu01519_Scytalidium 1.0000 0.4545 0.674 0.0124 *
## Otu01826_Veronaea_compacta 1.0000 0.4545 0.674 0.0117 *
## Otu00166_Cunninghamellaceae 0.9979 0.4545 0.673 0.0200 *
## Otu00684_Ascomycota 0.9942 0.4545 0.672 0.0287 *
## Otu00808_Chaetomiaceae 0.9220 0.4848 0.669 0.0476 *
## Otu01081_Clavicipitaceae 0.9204 0.4848 0.668 0.0381 *
## Otu00349_Gongronella_butleri 1.0000 0.4242 0.651 0.0275 *
## Otu00593_Subulicystidium 1.0000 0.4242 0.651 0.0397 *
## Otu00718_GS10 1.0000 0.4242 0.651 0.0328 *
## Otu01197_Xylariaceae 1.0000 0.4242 0.651 0.0198 *
## Otu01212_Delfinachytrium_mesopotamicum 1.0000 0.4242 0.651 0.0244 *
## Otu01463_Pleosporales 1.0000 0.4242 0.651 0.0184 *
## Otu01469_Phacidiella_eucalypti 1.0000 0.4242 0.651 0.0247 *
## Otu01498_Herpotrichiellaceae 1.0000 0.4242 0.651 0.0236 *
## Otu01804_Glomeromycota 1.0000 0.4242 0.651 0.0117 *
## Otu01920_Sordariomycetes 1.0000 0.4242 0.651 0.0123 *
## Otu01347_Pleosporales 0.9609 0.4242 0.638 0.0359 *
## Otu00995_Setophaeosphaeria_badalingensis 1.0000 0.3939 0.628 0.0440 *
## Otu01229_Stachybotrys_sansevieriae 1.0000 0.3939 0.628 0.0395 *
## Otu01233_Glomeraceae 1.0000 0.3939 0.628 0.0278 *
## Otu01302_Cucurbitariaceae 1.0000 0.3939 0.628 0.0298 *
## Otu01473_Glomeraceae 1.0000 0.3939 0.628 0.0242 *
## Otu01511_Hypocreales 1.0000 0.3939 0.628 0.0374 *
## Otu01686_Glomeromycota 1.0000 0.3939 0.628 0.0214 *
## Otu01815_Ganoderma 1.0000 0.3939 0.628 0.0201 *
## Otu01459_Hannaella_luteola 0.9725 0.3939 0.619 0.0418 *
## Otu01285_Helicoma_fumosum 1.0000 0.3636 0.603 0.0448 *
## Otu01366_Devriesia_pseudoamericana 1.0000 0.3636 0.603 0.0401 *
## Otu01433_Claroideoglomus_claroideum 1.0000 0.3636 0.603 0.0376 *
## Otu01528_Talaromyces_stipitatus 1.0000 0.3636 0.603 0.0430 *
## Otu01554_Eutypella 1.0000 0.3636 0.603 0.0474 *
## Otu01780_Paraconiothyrium_brasiliense 1.0000 0.3636 0.603 0.0372 *
## Otu01436_Coprinellus 1.0000 0.3333 0.577 0.0498 *
##
## Group IN+WA #sps. 116
## A B stat p.value
## Otu00045_Plectosphaerella_cucumerina 0.9602 1.0000 0.980 1e-04 ***
## Otu00008_Solicoccozyma_aeria 0.9836 0.9630 0.973 0.0005 ***
## Otu00037_Mortierella_elongata 0.9657 0.9630 0.964 1e-04 ***
## Otu00124_Ascomycota 0.9493 0.9630 0.956 1e-04 ***
## Otu00019_Pseudogymnoascus_roseus 0.9670 0.9259 0.946 1e-04 ***
## Otu00020_Tetracladium 0.9649 0.9259 0.945 1e-04 ***
## Otu00093_Ascomycota 0.9599 0.9259 0.943 1e-04 ***
## Otu00066_Nectria_ramulariae 0.9576 0.9259 0.942 1e-04 ***
## Otu00102_Onygenales 0.9936 0.8889 0.940 1e-04 ***
## Otu00014_Penicillium 0.9043 0.9630 0.933 0.0049 **
## Otu00046_Mortierella_exigua 0.9393 0.9259 0.933 1e-04 ***
## Otu00027_Pyrenochaetopsis_leptospora 0.9783 0.8889 0.933 0.0037 **
## Otu00148_Cystofilobasidiales 0.9752 0.8889 0.931 1e-04 ***
## Otu00439_Ochroconis 0.9510 0.8889 0.919 1e-04 ***
## Otu00083_Helotiales 0.9895 0.8519 0.918 1e-04 ***
## Otu00176_Lecythophora 0.9049 0.9259 0.915 0.0027 **
## Otu00077_Tetracladium 0.8656 0.9630 0.913 1e-04 ***
## Otu00373_Eucasphaeria 0.9622 0.8519 0.905 0.0002 ***
## Otu00065_Pyronemataceae 0.9531 0.8519 0.901 1e-04 ***
## Otu00355_Vishniacozyma_victoriae 0.9036 0.8889 0.896 0.0007 ***
## Otu00080_Operculomyces_laminatus 0.8617 0.9259 0.893 0.0006 ***
## Otu00005_Mortierella_minutissima 0.8592 0.9259 0.892 0.0017 **
## Otu00461_Leucosporidium_intermedium 0.9224 0.8519 0.886 1e-04 ***
## Otu00358_Lycoperdaceae 0.9603 0.8148 0.885 0.0023 **
## Otu00078_Sordariomycetes 0.9940 0.7778 0.879 0.0005 ***
## Otu00127_Metarhizium_marquandii 0.9074 0.8519 0.879 1e-04 ***
## Otu00617_Fusicolla 0.9070 0.8519 0.879 0.0002 ***
## Otu00468_Vishniacozyma_tephrensis 0.9022 0.8519 0.877 0.0019 **
## Otu00382_Clavatospora_longibrachiata 0.8592 0.8889 0.874 0.0005 ***
## Otu00274_Ascomycota 0.9799 0.7778 0.873 1e-04 ***
## Otu00153_Sordariomycetes 0.8935 0.8519 0.872 0.0016 **
## Otu00230_Ascomycota 0.9748 0.7778 0.871 0.0002 ***
## Otu00610_Sagenomella_oligospora 0.9285 0.8148 0.870 0.0003 ***
## Otu00209_Branch06 0.9574 0.7778 0.863 1e-04 ***
## Otu00494_Hypocreales 0.8733 0.8519 0.863 0.0002 ***
## Otu00106_Mortierella_antarctica 1.0000 0.7407 0.861 1e-04 ***
## Otu00360_Agaricomycetes 0.8315 0.8889 0.860 0.0032 **
## Otu00470_Leptosphaeria_sclerotioides 0.9881 0.7407 0.856 0.0004 ***
## Otu00935_Mucor_circinelloides 0.9878 0.7407 0.855 1e-04 ***
## Otu00088_Tetracladium 0.9119 0.7778 0.842 0.0017 **
## Otu00205_Penicillium 0.7299 0.9630 0.838 0.0264 *
## Otu00653_Articulospora 0.9390 0.7407 0.834 0.0002 ***
## Otu00154_Acremonium_furcatum 0.8532 0.8148 0.834 0.0081 **
## Otu00232_Lycoperdon_pratense 0.9358 0.7407 0.833 0.0415 *
## Otu00220_Hyphodontia 0.9237 0.7407 0.827 0.0004 ***
## Otu00169_Chaetomium 0.9204 0.7407 0.826 0.0089 **
## Otu00377_Nectriaceae 0.9190 0.7407 0.825 0.0070 **
## Otu00486_Pleosporales 0.9570 0.7037 0.821 0.0074 **
## Otu00608_Pleosporales 1.0000 0.6667 0.816 1e-04 ***
## Otu00112_GS10 0.9971 0.6667 0.815 0.0008 ***
## Otu00748_Gibberella_zeae 0.8969 0.7407 0.815 0.0089 **
## Otu00478_Acremonium_pilosum 0.9911 0.6667 0.813 0.0007 ***
## Otu00828_Myxospora_crassiseta 0.9893 0.6667 0.812 1e-04 ***
## Otu00492_Setophoma_terrestris 0.9886 0.6667 0.812 0.0012 **
## Otu01133_Cadophora_malorum 0.9721 0.6667 0.805 1e-04 ***
## Otu01535_Sarocladium_strictum 0.9721 0.6667 0.805 1e-04 ***
## Otu00580_Clavatospora_longibrachiata 0.9119 0.7037 0.801 0.0004 ***
## Otu00279_Sordariales 0.7827 0.8148 0.799 0.0227 *
## Otu00251_Cylindrocarpon 0.8599 0.7407 0.798 0.0131 *
## Otu00876_Leucosporidium 0.9879 0.6296 0.789 0.0002 ***
## Otu00134_Metarhizium_marquandii 0.7200 0.8519 0.783 0.0295 *
## Otu01162_Hypocreales 0.9630 0.6296 0.779 1e-04 ***
## Otu00028_Chaetomiaceae 0.9080 0.6667 0.778 0.0059 **
## Otu01840_Malassezia_restricta 0.9029 0.6667 0.776 0.0011 **
## Otu00263_Pleosporales 0.9877 0.5926 0.765 0.0053 **
## Otu00370_Cistella_albidolutea 0.9850 0.5926 0.764 0.0030 **
## Otu00553_Pleosporales 0.7830 0.7407 0.762 0.0107 *
## Otu00366_Microascaceae 0.9762 0.5926 0.761 0.0064 **
## Otu00499_Onygenales 0.9976 0.5556 0.744 0.0016 **
## Otu00128_Ascobolaceae 0.9345 0.5926 0.744 0.0037 **
## Otu00671_Sordariomycetes 0.9966 0.5556 0.744 0.0036 **
## Otu00766_Psathyrella 0.9727 0.5556 0.735 0.0023 **
## Otu00407_Mortierella 0.9514 0.5556 0.727 0.0035 **
## Otu00359_Cylindrocarpon 0.9505 0.5556 0.727 0.0100 **
## Otu00179_Mortierella 0.8864 0.5926 0.725 0.0310 *
## Otu00757_GS05 0.9906 0.5185 0.717 0.0040 **
## Otu00411_Basidiomycota 0.9903 0.5185 0.717 0.0017 **
## Otu00843_Knufia_tsunedae 0.9237 0.5556 0.716 0.0149 *
## Otu00547_Pleosporales 0.9873 0.5185 0.715 0.0063 **
## Otu01327_Dichotomopilus_indicus 0.9209 0.5556 0.715 0.0048 **
## Otu00599_Pycnopeziza_sympodialis 0.9855 0.5185 0.715 0.0186 *
## Otu00118_Cylindrocarpon 0.9853 0.5185 0.715 0.0073 **
## Otu00429_Natantispora_retorquens 0.8614 0.5926 0.714 0.0257 *
## Otu00260_Cephaliophora 0.9841 0.5185 0.714 0.0034 **
## Otu00750_Ascomycota 0.9390 0.5185 0.698 0.0034 **
## Otu00459_Mortierella_alpina 1.0000 0.4815 0.694 0.0017 **
## Otu00253_Plectosphaerellaceae 0.9978 0.4815 0.693 0.0129 *
## Otu00084_Sordariales 0.9966 0.4815 0.693 0.0167 *
## Otu00586_Lasiosphaeriaceae 0.9903 0.4815 0.691 0.0057 **
## Otu00734_Pleosporales 0.9176 0.5185 0.690 0.0214 *
## Otu00959_Hypocreaceae 0.9866 0.4815 0.689 0.0130 *
## Otu00551_Ascomycota 0.9782 0.4815 0.686 0.0156 *
## Otu01047_Ramicandelaber 0.9754 0.4815 0.685 0.0141 *
## Otu00418_Leohumicola 0.9469 0.4815 0.675 0.0131 *
## Otu00155_Microascaceae 1.0000 0.4444 0.667 0.0068 **
## Otu01296_Pleosporales 1.0000 0.4444 0.667 0.0025 **
## Otu01288_Glomus_macrocarpum 0.9873 0.4444 0.662 0.0044 **
## Otu00490_Aspergillus 0.9820 0.4444 0.661 0.0450 *
## Otu00379_Ascomycota 0.9784 0.4444 0.659 0.0474 *
## Otu01254_Cystobasidiomycetes 0.8921 0.4815 0.655 0.0215 *
## Otu00306_Trechispora 0.9214 0.4444 0.640 0.0325 *
## Otu01566_Acaulium 0.9855 0.4074 0.634 0.0091 **
## Otu01105_Plectosphaerellaceae 1.0000 0.3704 0.609 0.0149 *
## Otu01626_Hymenoscyphus 1.0000 0.3704 0.609 0.0187 *
## Otu01676_Penicillium_megasporum 1.0000 0.3704 0.609 0.0157 *
## Otu02986_Clavatospora_longibrachiata 1.0000 0.3704 0.609 0.0105 *
## Otu00790_Spizellomyces 0.9901 0.3704 0.606 0.0396 *
## Otu01313_Xylariales 0.9859 0.3704 0.604 0.0239 *
## Otu01496_Sporidiobolaceae 0.9830 0.3704 0.603 0.0259 *
## Otu00428_Sebacinales 1.0000 0.3333 0.577 0.0284 *
## Otu02165_Basidiomycota 0.9716 0.3333 0.569 0.0370 *
## Otu00850_Pleosporales 1.0000 0.2963 0.544 0.0405 *
## Otu01226_Entoloma 1.0000 0.2963 0.544 0.0380 *
## Otu01907_Lecanicillium_subprimulinum 1.0000 0.2963 0.544 0.0265 *
## Otu02084_Tetracladium 1.0000 0.2963 0.544 0.0304 *
## Otu02087_Rhodotorula_graminis 1.0000 0.2593 0.509 0.0492 *
##
## Group TN+WA #sps. 12
## A B stat p.value
## Otu00186_Aureobasidium_pullulans 0.9400 0.9667 0.953 1e-04 ***
## Otu00353_Pleosporales 0.9613 0.9000 0.930 1e-04 ***
## Otu00241_GS07 0.9537 0.7667 0.855 0.0013 **
## Otu00730_Endoconidioma_populi 0.8957 0.7333 0.810 0.0349 *
## Otu00229_Paraphaeosphaeria 0.9301 0.7000 0.807 0.0430 *
## Otu00728_Eucasphaeria 0.9707 0.6667 0.804 0.0027 **
## Otu00193_Aspergillus_pseudodeflectus 0.9766 0.6333 0.786 0.0370 *
## Otu01211_Rhizophagus_irregularis 0.9351 0.6333 0.770 0.0073 **
## Otu00151_Penicillium_pimiteouiense 0.9861 0.5667 0.748 0.0134 *
## Otu01134_Ramicandelaber 0.9082 0.5667 0.717 0.0113 *
## Otu00945_Paraconiothyrium 0.9644 0.4667 0.671 0.0444 *
## Otu02107_Pleosporales 1.0000 0.3333 0.577 0.0282 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## State Clone Group
## 1 IN 130 IN_130_MCB_26_S_ITS_S70
## 2 IN 130 IN_130_MCB_2_S_ITS_S69
## 3 IN 130 IN_130_MCB_9_S_ITS_S22
## 4 IN 132 IN_132_MCB_16_S_ITS_S23
## 5 IN 132 IN_132_MCB_17_S_ITS_S71
## 6 IN 132 IN_132_MCB_27_S_ITS_S24
## 7 IN 272 IN_272_MCB_10_S_ITS_S31
## 8 IN 272 IN_272_MCB_11_S_ITS_S79
## 9 IN 272 IN_272_MCB_24_S_ITS_S72
## 10 IN 55 IN_55_MCB_6_S_ITS_S20
## 11 IN 55 IN_55_MCB_7_S_ITS_S68
## 12 IN 55 IN_55_MCB_8_S_ITS_S21
## 13 IN WT IN_WT_MCB_28_S_ITS_S32
## 14 IN WT IN_WT_MCB_29_S_ITS_S80
## 15 IN WT IN_WT_MCB_33_S_ITS_S33
## 16 TN 130 TN_130_A_S_ITS_S35
## 17 TN 130 TN_130_B_S_ITS_S83
## 18 TN 130 TN_130_C_S_ITS_S36
## 19 TN 132 TN_132_A_S_ITS_S84
## 20 TN 132 TN_132_B_S_ITS_S43
## 21 TN 132 TN_132_C_S_ITS_S91
## 22 TN 272 TN_272_A_S_ITS_S44
## 23 TN 272 TN_272_B_S_ITS_S92
## 24 TN 272 TN_272_C_S_ITS_S45
## 25 TN 55 TN_55_A_S_ITS_S81
## 26 TN 55 TN_55_B_S_ITS_S34
## 27 TN 55 TN_55_C_S_ITS_S82
## 28 TN WT TN_LS_1_S_ITS_S93
## 29 TN WT TN_LS_2_S_ITS_S46
## 30 TN WT TN_LS_3_S_ITS_S94
## 31 TN WT TN_WT_MB_19_S_ITS_S47
## 32 TN WT TN_WT_MB_20_S_ITS_S95
## 33 TN WT TN_WT_MB_21_S_ITS_S48
## 34 WA 130 WA130_BNL_20_S_ITS_S9
## 35 WA 132 WA132_RN_7_S_ITS_S57
## 36 WA 132 WA132_RN_8_S_ITS_S10
## 37 WA 272 WA272_BNL_17_S_ITS_S59
## 38 WA 272 WA272_BNL_18_S_ITS_S12
## 39 WA 272 WA272_RN_10_S_ITS_S11
## 40 WA 55 WA_55_BNL_19_S_ITS_S8
## 41 WA 55 WA_55_RN_1_S_ITS_S7
## 42 WA 55 WA_55_RN_2_S_ITS_S55
## 43 WA WT WA_WT_BNL_21_S_ITS_S60
## 44 WA WT WA_WT_BNL_22_S_ITS_S19
## 45 WA WT WA_WT_BNL_23_S_ITS_S67tcd.status.soil<-c("Negative","Positive","Positive","Negative","Negative","Positive","Negative","Positive","Negative","Negative","Positive","Positive")
set.seed(577)
indicspecies.its2.disease.soil<-multipatt(its2.soil.rareotutabtax.rename[34:45,], tcd.status.soil,func="IndVal.g",control=how(nperm=10000))
sink(file="its2.soil.tcdindicators.txt")
summary(indicspecies.its2.disease.soil,indvalcomp=TRUE)##
## Multilevel pattern analysis
## ---------------------------
##
## Association function: IndVal.g
## Significance level (alpha): 0.05
##
## Total number of species: 6738
## Selected number of species: 9
## Number of species associated to 1 group: 9
##
## List of species associated to each combination:
##
## Group Negative #sps. 6
## A B stat p.value
## Otu00818_Clonostachys 0.9185 1.0000 0.958 0.0144 *
## Otu00209_Branch06 0.8783 1.0000 0.937 0.0225 *
## Otu00553_Pleosporales 0.8448 1.0000 0.919 0.0229 *
## Otu01412_Herpotrichiellaceae 0.9667 0.8333 0.898 0.0220 *
## Otu00150_GS10 0.9408 0.8333 0.885 0.0435 *
## Otu00266_Ascobolus 0.9159 0.8333 0.874 0.0404 *
##
## Group Positive #sps. 3
## A B stat p.value
## Otu00748_Gibberella_zeae 0.9176 1.0000 0.958 0.0425 *
## Otu00498_Papiliotrema_laurentii 0.8596 1.0000 0.927 0.0372 *
## Otu02470_Chionosphaeraceae 0.9474 0.8333 0.889 0.0331 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1its2.soil.indicators<-read.table("IndicatorAnalysis/soilits_indicatoranalysis_sp20_ajo.csv",header=TRUE,sep=",")
library(tidyr)
its2.soil.indicators.long<-as.data.frame(pivot_longer(its2.soil.indicators, cols=c("Specificity","Sensitivity","Indicator.Value"),"Statistic"))
in.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="IN")
tn.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="TN")
tn.in.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="IN+TN")
wa.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="WA")
tcd.positive.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="Positive")
tcd.negative.its2.soil.indicators<-subset(its2.soil.indicators.long,its2.soil.indicators.long[,3]=="Negative")
its2.soil.indicator.mat<-its2.soil.indicators[c(1:10,21:30,41:50,61:70,81:89),2:4]
rownames(its2.soil.indicator.mat)<-its2.soil.indicators$OTU[c(1:10,21:30,41:50,61:70,81:89)]
colnames(its2.soil.indicator.mat)<-colnames(its2.soil.indicators)[2:4]
its2.soil.indicator.row.dat<-data.frame(State=its2.soil.indicators$Group,Mycoparasite=as.factor(its2.soil.indicators$Mycoparasite),Plant.Pathogen=as.factor(its2.soil.indicators$Plant.Pathogen), Arbuscular.Mycorrhizal=as.factor(its2.soil.indicators$Arbuscular.Mycorrhizal))
rownames(its2.soil.indicator.row.dat)<-make.names(its2.soil.indicators$OTU)
annotation_colors<-list(
Plant.Pathogen = c(Yes="maroon", No="white"),
Mycoparasite = c(Yes="khaki4", No="white"),
Arbuscular.Mycorrhizal=c(Yes="dodgerblue4", No="white"),
State=c(IN="springgreen3", IN.TN="slateblue2", TN="skyblue1", WA="violet",Negative="olivedrab1", Positive="darkslategrey")
)
my_colors<-colorRampPalette(colors=c("darkblue","lightblue"))
library(ClassDiscovery)## Loading required package: cluster## Loading required package: oompaBaselibrary(pheatmap)
its.soil.heatmap<-pheatmap(its2.soil.indicator.mat,
annotation_row=its2.soil.indicator.row.dat,
color=gray.colors(15),
cluster_rows=FALSE,
cluster_cols=FALSE,
gaps_row=c(10,20,30,40,46),
annotation_colors=annotation_colors,
cellwidth=10)