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multiplot2_DBDnEC.R
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multiplot2_DBDnEC.R
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##############################################################################################################
# Plots for Naima's paper
# Baseline dataset with POSITIVE slope
# run on cluster
##############################################################################################################
#### cluster
#### load Naima's data
#----------------------
setwd("C:/Users/carme/Dropbox/Work_Files/shapiro_lab/Naima_project/cluster") #laptop
#setwd("/home/cmurall/naimaproject") #cluster
#EMP_data <-read.delim("EMP_table.from_biom_w_taxonomy.txt", header= TRUE) #takes ~3.6 mins
load(file="emp2.RData")
#or run Naima's code (see "randomGPO_data.R" file for this)
#### libraries
#-------------
library(tictoc)
library(gridExtra)
library(ggplot2)
library(lattice)
library(grid)
library(plyr)
library(dplyr)
library(magrittr)
library(gamlss)
#### used across code
#--------------------
nRows <-22014 # no. of ASVs
nCols <-2000 # no. of samples
num<-nRows*nCols #total no. of elements
p <- c(1, 5504, 11007, 16510, 22014) #c(1, 5504, 11007, 16510, 22014) #(0,25%,50%,75%, 100%) elements are tested
no<- length(p) #number of plots in a row
pl <-list() #for plots
perlist<-c(0, 25, 50, 75, 100) #for labelling top row of images and images
#### 1. add DBD ----------------------------------------------------------------------------------------------
#create Poisson data, change lambda per site (for groups of columns):
set.seed(3579)
simMat<-matrix(NA,nRows,nCols) #mat for filling
max<-0.01 #max lambda
sites<-read.csv(file="site_structure.csv", header = TRUE) #load Naima's site structure
colnames(sites) <- c("sites", "num_samples") #remove weird character
noSites<-as.vector(sites[,2])
cum<-cumsum(noSites)
for(i in 1:cum[1]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[1]+1):cum[2]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[2]+1):cum[3]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[3]+1):cum[4]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[4]+1):cum[5]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[5]+1):cum[6]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[6]+1):cum[7]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[7]+1):cum[8]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[8]+1):cum[9]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[9]+1):cum[10]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[10]+1):cum[11]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[11]+1):cum[12]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[12]+1):cum[13]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[13]+1):cum[14]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[14]+1):cum[15]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[15]+1):cum[16]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[16]+1):cum[17]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
write.csv(simMat, file="simMat2_DBD.csv")
# loop to add DBD
rm(i)
for (i in 1:no){
#ensure still using same base dataset:
simMat <-read.csv(file="simMat2_DBD.csv", header = TRUE) #or fromGPO
simMat<-simMat[,-1] #drop first column because it's not data
#make simData a P/A matrix
simData<-data.frame(simMat) #make into dataframe
fun<-function(x) replace(x, x>0, 1) #function, replace nonzero elements into 1
paData <-simData %>% mutate_all(fun) #make data presence/absence
#for filling mulitple elements:
for (k in 1:nCols){
for(j in 1:p[i]){ #check p elements per column
ind<-which(paData[,k]==0, arr.ind = TRUE) #indices of zero elements in column
if (any(ind!=0)) rind<-sample(ind,1) else next #randomly select a zero element to replace
el<-sample(paData[,k],1) #choose an element in a column at random
if (el>0) paData[,k][rind]<-1 #if el is nonzero, fill rind with 1
}}
#paData is now transformed
#save dataset
percent<-perlist[[i]] #percent label
name<-paste0("paData2_DBD", percent,".csv") #create label
write.csv(paData, file=name) #save dataset
#get number of ASVs per sample (by column)
vecSum <-c()
for(l in 1:nCols){
x<-sum(paData[,l])
vecSum<-append(vecSum,x)
}
# new dataframe for results
df <-data.frame()
df<-cbind(vecSum)
colnames(df)<-c("no.species")
#get number of genera per sample
x1<-c()
for(m in 1:nCols){
ind <-which(paData[,m]>0, arr.ind = TRUE) #indices for nonzero elements
vecGen <-emp2$genus[ind] #genera for those indices
count <-nrow(plyr::count(vecGen))#freq of genera in vecGen, count no. rows which gives no. of genera in vecGen
x1<-append(x1,count)#store
}
x1 <-as.data.frame(x1)
#fill results df
df <-as.data.frame(df)
df <- bind_cols(df, x1) #bind
colnames(df)<-c("no.species","no.gen")
ratio<- df$no.species/df$no.gen
df$ratio <-ratio
head(df)
#plot ASV:#genera vs. # of genera
pl[[i]]<-ggplot(df, aes(x = no.gen, y=ratio))+geom_point()+
#xlim(0,12)+#ylim(-5,50)+
geom_smooth(method = "lm") +
theme(axis.title.x=element_blank(),axis.title.y=element_blank(), plot.title = element_text(hjust = 0.5, face = "plain")) +
ggtitle(as.character(percent))
}
#### 2. add EC ----------------------------------------------------------------------------------------------
set.seed(3579)
simMat<-matrix(NA,nRows,nCols) #mat for filling
max<-0.01 #max lambda
#sites<-read.csv(file="site_structure.csv", header = TRUE) #load Naima's site structure
#colnames(sites) <- c("sites", "num_samples")
#noSites<-as.vector(sites[,2])
#cum<-cumsum(noSites)
rm(i) #clear i
for(i in 1:cum[1]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[1]+1):cum[2]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[2]+1):cum[3]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[3]+1):cum[4]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[4]+1):cum[5]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[5]+1):cum[6]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[6]+1):cum[7]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[7]+1):cum[8]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[8]+1):cum[9]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[9]+1):cum[10]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[10]+1):cum[11]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[11]+1):cum[12]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[12]+1):cum[13]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[13]+1):cum[14]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[14]+1):cum[15]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[15]+1):cum[16]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
for(i in (cum[16]+1):cum[17]){
lambda<-runif(1, 0, max) #choose a lambda per site (a group of columns)
simMat[,i]<-rpois(nRows, lambda) #fill elements, each column has its unique distribution
}
write.csv(simMat, file="simMat2_EC.csv")
# loop to add EC
rm(i)
for (i in 1:no){
#ensure still using same base dataset:
simMat <-read.csv(file="simMat2_EC.csv", header = TRUE) #or fromGPO
simMat<-simMat[,-1] #drop first column because it's not data
#make simData a P/A matrix
simData<-data.frame(simMat) #make into dataframe
fun<-function(x) replace(x, x>0, 1) #function, replace nonzero elements into 1
paData <-simData %>% mutate_all(fun) #make data presence/absence
#for removing mulitple elements:
for (k in 1:nCols){
for(j in 1:p[i]){ #check p elements per column
ind<-which(paData[,k]!=0, arr.ind = TRUE) #indices of nonzero elements in column
if (any(ind!=0)) rind<-sample(ind,1) else next #randomly select a nonzero element to replace #if (any(ind!=0)) makes sure that ind isn't empty
el<-sample(paData[,k],1) #choose an element in the column at random
if (el>0) paData[,k][rind]<-0 else next #if el is nonzero, replace element at position rind with 0
}}
#paData is now transformed
#save dataset
percent<-perlist[[i]] #percent label
name<-paste0("paData2_EC", percent,".csv") #create label
write.csv(paData, file=name) #save dataset
#get number of ASVs per sample (by column)
vecSum <-c()
for(l in 1:nCols){
x<-sum(paData[,l])
vecSum<-append(vecSum,x)
}
# new dataframe for results
df <-data.frame()
df<-cbind(vecSum)
colnames(df)<-c("no.species")
#get number of genera per sample
x1<-c()
for(m in 1:nCols){
ind <-which(paData[,m]>0, arr.ind = TRUE) #indices for nonzero elements
vecGen <-emp2$genus[ind] #genera for those indices
count <-nrow(plyr::count(vecGen))#freq of genera in vecGen, count no. rows which gives no. of genera in vecGen
x1<-append(x1,count)#store
}
x1 <-as.data.frame(x1)
#fill results df
df <-as.data.frame(df)
df <- bind_cols(df, x1) #bind
colnames(df)<-c("no.species","no.gen")
ratio<- df$no.species/df$no.gen
df$ratio <-ratio
head(df)
#plot ASV:#genera vs. # of genera
pl[[(i+no)]]<-ggplot(df, aes(x = no.gen, y=ratio))+geom_point()+
#xlim(0,12)+#ylim(-5,50)+
geom_smooth(method = "lm") + theme(axis.title.x=element_blank(),axis.title.y=element_blank()) #+ ggtitle("rpois, 75% ")
}
#### 3. final plot ------------------------------------------------------------------------------------------
# plot all runs in one plot
fullpl<-grid.arrange(grobs=pl, nrow=2, top="Poisson dataset, by site lambda (0, 0.01)",
left = textGrob("ASV:Genus", rot = 90, vjust = 0.5),
bottom = textGrob("number of genera"))#,
# missing label of rows, DBD top, EC bottom
#save plot
ggsave(fullpl,filename="multiplot2.pdf")#, width = 14.0, height = 5.0, units = "cm")
ggsave(fullpl,filename="multiplot2png.png")
#save data of plot (so it can be reformated)
save(pl, file="multiplot2_data.RData")
#load using: load("multiplot2_data.RData")
#then, change like this:
# pl + ggtitle("better title")
# #reconfiguring the multi-plot figure:
# pl[[1]]<-pl[[1]]+ylim(1,2.5)+xlim(0,320)
# pl[[2]]<-pl[[2]]+ylim(1,2.5)+xlim(0,320)
# pl[[3]]<-pl[[3]]+ylim(1,2.5)+xlim(0,320)
# pl[[4]]<-pl[[4]]+ylim(1,2.5)+xlim(0,320)
# pl[[5]]<-pl[[5]]+ylim(1,2.5)+xlim(0,320)
# pl[[6]]<-pl[[6]]+ylim(1,2.)+xlim(0,320)
# pl[[7]]<-pl[[7]]+ylim(1,2.)+xlim(0,320)
# pl[[8]]<-pl[[8]]+ylim(1,2.)+xlim(0,320)
# pl[[9]]<-pl[[9]]+ylim(1,2.)+xlim(0,320)
# pl[[10]]<-pl[[10]]+ylim(1,2.)+xlim(0,320)