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BarPlots_Family_Inoculum_16S_ESV.R
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#March 5, 2018
#Reset R's Brain
rm(list=ls())
## Set working directory
#setwd("")
#set working directory
setwd("~/Dropbox/StatsandProgramming/16SElevationGradient/")
#source in functions
source('~/Dropbox/StatsandProgramming/source/gettaxondd.R', chdir = TRUE)
source('~/Dropbox/StatsandProgramming/source/getrowsums.R', chdir = TRUE)
################################################################################
############################## 1. DATA PROCESSING ##############################
################################################################################
#Bacteria
# 1.1. Input the file of rarefied OTU-abundance-taxon table
#read in dataframes
zotu_abundance_taxa <- read.csv("data/16S_usearch10/zotutab_inoculum_tax_filtered.csv", row.names=1)
head(zotu_abundance_taxa)
# 1.2. Extract otu-abundance table from OTU-abundance-taxon table
zotu_abundance_bac <- zotu_abundance_taxa[, colnames(zotu_abundance_taxa) != "Consensus.Lineage"]
head(zotu_abundance_bac)
#transform
zotu_abundance_bac_trans <- t(zotu_abundance_bac)
#rarefy
set.seed(10)
getrowsums(zotu_abundance_bac_trans)
zotu_abundance_bac_trans<- rrarefy(zotu_abundance_bac_trans,9212)
rowSums(zotu_abundance_bac_trans)
#rarefied
zotu_abundance_bac <- t(zotu_abundance_bac_trans)
colSums(zotu_abundance_bac)
#head(otu_taxa)
dd_zotu <- as.data.frame(zotu_abundance_taxa$Consensus.Lineage)
dd_zotu$ZOTU <- row.names(zotu_abundance_taxa)
names(dd_zotu) <- c("taxon","id")
names(dd_zotu)
library("stringr")
library("plyr")
zotu_taxa1 <- ldply(str_split(string = dd_zotu$taxon, pattern=";"), rbind) # Divide a column using ";"and convert list to data frame
names(zotu_taxa1) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
zotu_taxa2 <- as.data.frame(lapply(zotu_taxa1, gsub, pattern=" ", replacement=""))
zotu_taxa3<- cbind(dd_zotu[,1:2 ],zotu_taxa2)
#check they match
row.names(zotu_abundance_bac)==(zotu_taxa3$id)
zotu_abundance_taxa2 <- cbind(zotu_abundance_bac, zotu_taxa3) #Combine otu ID and taxon table
head(zotu_abundance_taxa2)
#figure out how many phyla are in the inoculum
phyla_inoculum_zotu <- as.vector(unique(zotu_abundance_taxa2$Phylum))
length(phyla_inoculum_zotu) #20 phyla
Family_inoculum_zotu <- as.vector(unique(zotu_abundance_taxa2$Family ))
length(Family_inoculum_zotu) #123 families
percentidentified <- function(otuabund){
#calculate percentage with no Phyla ID - NA or "g__" for bacteria or "g__unidentified" for fungi
totalphylanoID <- length(which(is.na(otuabund$Phylum))) + length(which(otuabund$Phylum == "p__")) + length(which(otuabund$Phylum == "p__unidentified"))
percentwithphyla <- (nrow(otuabund) - totalphylanoID )/nrow(otuabund)*100
#calculate percentage with no Class ID - NA or "g__" for bacteria or "g__unidentified" for fungi
totalclassnoID <- length(which(is.na(otuabund$Class))) + length(which(otuabund$Class == "c__")) + length(which(otuabund$Class == "c__unidentified"))
#calculation based on total number of rows minus rows with no ids/ total number of rows * 100
percentwithclass <- (nrow(otuabund) - totalclassnoID )/nrow(otuabund)*100
#calculate percentage with no Order ID - NA or "o__" for bacteria or "o__unidentified" for fungi
totalordernoID <- length(which(is.na(otuabund$Order))) + length(which(otuabund$Order == "o__" )) + length(which(otuabund$Order == "o__unidentified"))
#calculation based on total number of rows minus rows with no ids/ total number of rows * 100
percentwithorder <- (nrow(otuabund) - totalordernoID )/nrow(otuabund)*100
#calculate percentage with no Family ID - NA or "g__" for bacteria or "g__unidentified" for fungi
totalfamnoID <- length(which(is.na(otuabund$Family))) + length(which(otuabund$Family == "f__")) + length(which(otuabund$Family == "f__unidentified"))
#calculation based on total number of rows minus rows with no ids/ total number of rows * 100
percentwithfamily <- (nrow(otuabund) - totalfamnoID)/nrow(otuabund)*100
#calculate percentage with no Genus ID - NA or "g__" for bacteria or "g__unidentified" for fungi
totalgenusnoID <- length(which(is.na(otuabund$Genus))) + length(which(otuabund$Genus == "g__")) + length(which(otuabund$Genus == "g__unidentified"))
#calculation based on total number of rows minus rows with no ids/ total number of rows * 100
percentwithgenus <- (nrow(otuabund) - totalgenusnoID)/nrow(otuabund)*100
#calculate percentage with no species ID - NA or "s__" for bacteria or "s__unidentified" for fungi
totalspeciesnoID <- length(which(is.na(otuabund$Species))) + length(which(otuabund$Species == "s__")) + length(which(otuabund$Species == "s__unidentified"))
#calculation based on total number of rows minus rows with no ids/ total number of rows * 100
percentwithspecies <- (nrow(otuabund) - totalspeciesnoID)/nrow(otuabund)*100
#make names column
names <- c("Phyla","Class","Order","Family","Genus","Species")
#make percentages column
percentages <- c(percentwithphyla,percentwithclass, percentwithorder,percentwithfamily, percentwithgenus,percentwithspecies)
as.data.frame(cbind(names,percentages))
}
#run function
bacpercentID_zotu <- percentidentified(zotu_abundance_taxa2)
bacpercentID_zotu
write.csv(bacpercentID_zotu, "Figures/otuvzotu_taxonomy/bacpercent_ID_zotu_rarefied.csv")
##########################################################################################
########################## 4. Community composition at phylum level ######################
##########################################################################################
# 4.1. Make dataframe for phylum composition
zotu_abundance_taxa3 <- cbind(OTU_ID = rownames(zotu_abundance_taxa2), zotu_abundance_taxa2) #convert the rownames to a proper column of the data.frame
rownames(zotu_abundance_taxa3) <- NULL
zotu_abundance_taxa3$Phylum <- as.character(zotu_abundance_taxa3$Phylum) #convert empty column to unidentified
zotu_abundance_taxa3$Phylum[is.na(zotu_abundance_taxa3$Phylum)]<-"p__unidentified"
zotu_abundance_taxa3$Family <- as.character(zotu_abundance_taxa3$Family) #convert empty column to unidentified
zotu_abundance_taxa3$Family[is.na(zotu_abundance_taxa3$Family)]<-"f__unidentified"
zotu_abundance_taxa3$Family[zotu_abundance_taxa3$Family=="f__"]<-"f__unidentified"
zotu_abundance_taxa3$Kingdom_Phylum <- str_c(zotu_abundance_taxa3$Kingdom, zotu_abundance_taxa3$Phylum,zotu_abundance_taxa3$Family, sep=";") #combine kingdom and phylun
zotu_abundance_taxa4 <- zotu_abundance_taxa3[, !(colnames(zotu_abundance_taxa3) %in% c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"))]
zotu_abundance_taxa4 <- zotu_abundance_taxa4[, -c(22,23)]
head(zotu_abundance_taxa4)
# 4.2. Caluculate mean value for each phylum on each date
library("reshape2")
zotu_abundance_taxa5 <- melt(zotu_abundance_taxa4, id.vars= c("OTU_ID","Kingdom_Phylum"), variable.name="Sample", value.name="Abundance")
zotu_abundance_taxa5 <- subset(zotu_abundance_taxa5, Abundance>0)
#get total number of sequences per sample
totalseqs <- colSums(zotu_abundance_taxa4[ ,2:21])
avgnumseqs <- sum(totalseqs)/20
zotu_abundance_taxa5$Abundance <- zotu_abundance_taxa5$Abundance/avgnumseqs*100 # Check your sequence number and convert to relative abundance (%)
head(zotu_abundance_taxa5)
phylum_abundance_zotu <- ddply(zotu_abundance_taxa5, c("Kingdom_Phylum","Sample"), summarise,
sum = sum(Abundance, na.rm=TRUE)
)
head(phylum_abundance_zotu)
library("stringr")
library("plyr")
phylum_abundance_zotu$Site <- str_sub(phylum_abundance_zotu$Sample,2,2) #get a column of site names
head(phylum_abundance_zotu)
phylum_abundance2_zotu <- ddply(phylum_abundance_zotu, c("Kingdom_Phylum","Site"), summarise, # Caluculate mean value on each date
mean = mean(sum, na.rm=TRUE),
sd = sd(sum, na.rm=TRUE),
n = sum(!is.na(sum)),
se = sd/sqrt(n),
max = max(sum, na.rm=TRUE)
)
head(phylum_abundance2_zotu)
# 4.3. Classify phylum with mean abundance < XX% (1% here) into "other phylum".
# Twelve phylum and others are recommended to use palette="Paired".
# "maximum abundance" rather than "mean abundance" will be better when compositional change is large.
phylum_over1_zotu <- subset(phylum_abundance2_zotu, mean>6) # Subset the phylum with >1%
#if one site has more than 1% of that phylum then it stays
phylum_over1_zotu <- as.vector(unique(phylum_over1_zotu$Kingdom_Phylum)) # Subset the phylum with >1%
length(phylum_over1_zotu) #11
#stick with over 6% bc its 12 families which is the max that can fit
phylum_abundance_over1_zotu <- subset(phylum_abundance2_zotu, Kingdom_Phylum==phylum_over1_zotu[1]) # Subset the phylum with >1%
for(i in 2:length(phylum_over1_zotu)){
phylum_abundance_over1_zotu <-merge(phylum_abundance_over1_zotu, subset(phylum_abundance2_zotu, Kingdom_Phylum==phylum_over1_zotu[i]), all=T)
}
phylum_abundance_over1_zotu
#figure out how many phyla are in the inoculum
famininoculum_zotu <- as.vector(unique(phylum_abundance2_zotu$Kingdom_Phylum))
length(famininoculum_zotu)
#110 families in the inoculum
#ok so this script adds up the mean abundance of every phyla in Site D, then 100-that number, is the other phyla
phylum_abundance_below1_zotu <- ddply(phylum_abundance_over1_zotu, c("Site"), summarise, # Subset the phylum with <1% and classify into other phylum
Kingdom_Phylum = "k__Bacteria;p__;f__other families",
mean = 100-sum(mean),
sd = 0,
n = mean(n),
se= 0
)
#ok so what this says is that only ~1% of the reads in desert belong to other phyla
phylum_abundance_below1_zotu
phylum_abundance3_zotu <- merge(phylum_abundance_over1_zotu, phylum_abundance_below1_zotu, all=T)
head(phylum_abundance3_zotu)
# 4.4. Reformat the date description
phylum_abundance3_zotu$Phylum <- ldply(str_split(string=phylum_abundance3_zotu$Kingdom_Phylum, pattern=";p__"), rbind)[,2] # Divide a column using "_".
#order by most abundant phyla
phyla_sorted_zotu <- phylum_abundance3_zotu[order(-phylum_abundance3_zotu[,3]), ]
phyla_sorted_zotu
# 4.4. Reorder the phylum from the largest abundance to the lowest
length(levels(phylum_abundance3_zotu$Phylum))
#re order the Sites - give site full names
phyla_sorted_zotu <- phylum_abundance3_zotu[order(phylum_abundance3_zotu[,2]), ]
phyla_sorted_zotu
table(phyla_sorted_zotu$Site)
phyla_sorted_zotu$Sitefullnames <- c(rep("Desert",12),rep("Grassland",12),rep("Pine-Oak",12),rep("Subalpine",12),rep("Scrubland",12))
phyla_sorted_zotu
phyla_sorted_zotu$Sitefullnames <- factor(phyla_sorted_zotu$Sitefullnames,
levels = c("Desert","Scrubland","Grassland","Pine-Oak","Subalpine"))
# 4.5 Graph making
library("ggplot2")
library("scales")
library("gridExtra")
fam_barplot1_zotu<-
ggplot(phyla_sorted_zotu, aes(x=Sitefullnames, y=mean, fill=Phylum)) +
geom_bar(stat="identity",color="black", width=0.4) +
scale_fill_brewer(palette = "Paired")+
#When you use scale_fill_brewer(palette = "Paired"), the maximum is 12, or 13 ("other phylum" is not colored)
labs(x="Site", y="Relative abundance (%) ESV", fill="Family") + theme_bw()+
theme(axis.text.x=element_text(size=12,angle=30, hjust=1),
axis.text.y=element_text(size=12),
legend.text = element_text(size=12)
)
fam_barplot1_zotu
phyla_sorted_zotu$otuvesv <- rep("ESV", nrow(phyla_sorted_zotu))
write.csv(phyla_sorted_zotu, "Figures/otuvzotu_taxonomy/16S_Inoculum_phylasorted_zotu_rarefied.csv")
fam_barplot1_zotu
ggsave(fam_barplot1_zotu,filename=paste("Figures/otuvzotu_taxonomy/fambarplot_inoculum_16S_zotu_rarefied.pdf"), width=7, height=4.5)