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arthropodBorne_PN.R
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arthropodBorne_PN.R
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"""
Babayan, Orton & Streicker
Predicting Reservoir Hosts and Arthropod Vectors from Evolutionary Signatures in RNA Virus Genomes
-- Arthropod-bone transmission prediction from phylogenetic neighborhoods
"""
rm(list=ls())
setwd("") # Set local working directory where files are located
library(plyr)
library(h2o) # https://www.h2o.ai/products/h2o/
library(dplyr)
library(reshape2)
library(ape)
library(seqinr)
library(matrixStats)
`%not in%` <- function (x, table) is.na(match(x, table, nomatch=NA_integer_))
# Start h2o JVM
localh20<-h2o.init(nthreads = -1)
# Read data from file
f1<-read.csv("BabayanEtAl_VirusData.csv",header=T)
allP<-read.fasta(file ="BabayanEtAl_sequences.fasta", seqtype = "DNA", as.string = TRUE, seqonly = F, strip.desc = T)
# Remove viruses with unknown vector status
vecorphans<-subset(f1,f1$Vector.borne=="?")
vecorphans<-vecorphans[with(vecorphans,order(Viral.group,Genbank.accession,decreasing=T)),]
f_v<-subset(f1,f1$Vector.borne!="?")
f_v2<-f_v[,c("Virus.name","Vector.borne","Viral.group","Genbank.accession")]
f_v2$response<-factor(f_v2$Vector.borne)
ntax<-length(unique(f_v2$response))
bp<-c("X0","X1")
f_v2$Genbank.accession2<-do.call(rbind,strsplit(as.character(f_v2$Genbank.accession),"[.]"))[,1]
nfeatures<-length(bp)
# Write orphans
vecOrp<-allP[c(which(names(allP) %in% vecorphans$Genbank.accession))]
write.fasta(vecOrp,names(vecOrp),file.out="vecOrphanDB.fasta", open = "w", nbchar = 100, as.string = T)
# Clean up
rm(f1,f_v)
# Train many models
set.seed(78910) # ensure reproducible runs
s<-.7 # proportion in the training set
nloops<-600
accuracy.v<-c()
pc.accuracy<-matrix(nrow=nloops,ncol=2)
test.record<-matrix(nrow=80,ncol=nloops)
lr<-c()
md<-c()
sr<-c()
csr<-c()
nt<-c()
mr<-c()
vimps<-matrix(nrow=nfeatures,ncol=nloops)
for (i in 1:nloops){
# 70:15:15 stratified split
trains<-f_v2 %>% group_by(response) %>%
filter(Genbank.accession %in% sample(unique(Genbank.accession), floor(s*length(unique(Genbank.accession)))))
testval<-subset(f_v2,!(f_v2$Genbank.accession %in% trains$Genbank.accession)) # ref numbers absent from training set
vals<-testval %>% group_by(response) %>%
filter(Genbank.accession %in% sample(unique(Genbank.accession), floor(.5*length(unique(Genbank.accession)))))
tests<-subset(testval,!(testval$Genbank.accession %in% vals$Genbank.accession)) # ref numbers in testval set absent from test set
ntest<-dim(tests)[1]
trains<-droplevels(trains)
tests<-droplevels(tests)
vals<-droplevels(vals)
test.record[,i]<-as.character(tests$Genbank.accession)
# Select sequences and write to file
trainSeqs<-allP[c(which(names(allP) %in% trains$Genbank.accession))] # pick sequences in the training set
testSeqs<-allP[c(which(names(allP) %in% tests$Genbank.accession))] # pick sequences in the test set
valSeqs<-allP[c(which(names(allP) %in% vals$Genbank.accession))] # pick sequences in the optimization set
write.fasta(testSeqs, names(testSeqs), file.out="testDB.fasta", open = "w", nbchar = 100, as.string = T)
write.fasta(trainSeqs, names(trainSeqs), file.out="trainDB.fasta", open = "w", nbchar = 100, as.string = T)
write.fasta(valSeqs, names(valSeqs), file.out="valDB.fasta", open = "w", nbchar = 100, as.string = T)
# BLAST
system("makeblastdb -in trainDB.fasta -dbtype nucl -parse_seqids -out allTrainingDB",intern=F)
# Blast test against training
system("blastn -db allTrainingDB -query testDB.fasta -out testOut.out -num_threads 8 -outfmt 10 -max_target_seqs=5 -max_hsps 1 -reward 2 -task blastn -evalue 10 -word_size 8 -gapopen 2 -gapextend 2",inter=F,wait=FALSE)
# Blast validation against training
system("blastn -db allTrainingDB -query valDB.fasta -out valOut.out -num_threads 8 -outfmt 10 -max_target_seqs=5 -max_hsps 1 -reward 2 -task blastn -evalue 10 -word_size 8 -gapopen 2 -gapextend 2",inter=F,wait=FALSE)
# Blast orphan against training
system("blastn -db allTrainingDB -query vecOrphanDB.fasta -out vecOrphanOut.out -num_threads 8 -outfmt 10 -max_target_seqs=5 -max_hsps 1 -reward 2 -task blastn -evalue 10 -word_size 8 -gapopen 2 -gapextend 2",inter=F,wait=FALSE)
# Blast training against the training set (take top 5 hits)
system("blastn -db allTrainingDB -query trainDB.fasta -out trainOut.out -num_threads 8 -outfmt 10 -max_target_seqs=6 -max_hsps 1 -reward 2 -task blastn -evalue 10 -word_size 8 -gapopen 2 -gapextend 2",inter=F,wait=TRUE)
# Summarize blast hits from training set
allBlast<-read.csv(file="trainOut.out",col.names = c("query acc.", "subject acc.", "% identity", "alignment length", "mismatches", "gap opens", "q. start", "q. end"," s. start"," s. end"," evalue"," bit score"),header=F)
nvir<-length(unique(allBlast$query.acc.))
virnames<-unique(allBlast$query.acc.)
ecutoff<-1E-3
j=1
d<-subset(allBlast,allBlast$query.acc.==virnames[j])
d2<-subset(d,d$X..identity<100)
d2<-subset(d2,d2$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no acceptable blast hit
for (z in 1:1){
if (nrow(d2)==0){
blast.uc<-rep(1/ntax,ntax)
blast.uc<-data.frame(t(blast.uc))
colnames(blast.uc)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc<-cbind(id,blast.uc)
colnames(blast.uc)<-c("id","X0","X1")}
else {
dhost<-merge(d2,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=F)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(virnames[j])
blast.uc<-cbind(id,hosts)
colnames(blast.uc)<-c("id","X0","X1")}}
for (j in 2:nvir){
d<-subset(allBlast,allBlast$query.acc.==virnames[j])
d2<-subset(d,d$X..identity<100)
d2<-subset(d2,d2$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
if (nrow(d2)==0){
blast.uc.s<-rep(1/ntax,ntax)
blast.uc.s<-data.frame(t(blast.uc.s))
colnames(blast.uc.s)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc.s<-cbind(id,blast.uc.s)
colnames(blast.uc.s)<-c("id","X0","X1")}
else {
dhost<-merge(d2,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=T)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(virnames[j])
blast.uc.s<-cbind(id,hosts)
colnames(blast.uc.s)<-c("id","X0","X1")}
blast.uc<-rbind(blast.uc,blast.uc.s)}
f1_train<-merge(trains,blast.uc,by.x="Genbank.accession",by.y="id",all.x=F,all.y=T)
set<-c("response",bp)
f1_train<-f1_train[,c(set)]
# Summarize blast hits from test set
testBlast<-read.csv(file="testOut.out",col.names = c("query acc.", "subject acc.", "% identity", "alignment length", "mismatches", "gap opens", "q. start", "q. end"," s. start"," s. end"," evalue"," bit score"),header=F)
nvir<-length(unique(testBlast$query.acc.))
virnames<-unique(testBlast$query.acc.)
ecutoff<-1E-3
j=1
d<-subset(testBlast,testBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
for (z in 1:1){
if (nrow(d2)==0){
blast.uc<-rep(1/ntax,ntax)
blast.uc<-data.frame(t(blast.uc))
colnames(blast.uc)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc<-cbind(id,blast.uc)
colnames(blast.uc)<-c("id","X0","X1")}
else {
dhost<-merge(d2,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=F)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(virnames[j])
blast.uc<-cbind(id,hosts)
colnames(blast.uc)<-c("id","X0","X1")}}
for (j in 2:nvir){
d<-subset(testBlast,testBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
if (nrow(d2)==0){
blast.uc.s<-rep(1/ntax,ntax)
blast.uc.s<-data.frame(t(blast.uc.s))
colnames(blast.uc.s)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc.s<-cbind(id,blast.uc.s)
colnames(blast.uc.s)<-c("id","X0","X1") }
else {
dhost<-merge(d,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=T)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(d$query.acc.[1])
blast.uc.s<-cbind(id,hosts)
colnames(blast.uc.s)<-c("id","X0","X1")}
blast.uc<-rbind(blast.uc,blast.uc.s)}
f1_test<-merge(tests,blast.uc,by.x="Genbank.accession",by.y="id",all.x=F,all.y=T)
testID<-f1_test$Genbank.accession
set<-c("response",bp)
f1_test<-f1_test[,c(set)]
# Summarize blast hits from optimization set
valBlast<-read.csv(file="valOut.out",col.names = c("query acc.", "subject acc.", "% identity", "alignment length", "mismatches", "gap opens", "q. start", "q. end"," s. start"," s. end"," evalue"," bit score"),header=F)
nvir<-length(unique(valBlast$query.acc.))
virnames<-unique(valBlast$query.acc.)
ecutoff<-1E-3
j=1
d<-subset(valBlast,valBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
for (z in 1:1){
if (nrow(d2)==0){
blast.uc<-rep(1/ntax,ntax)
blast.uc<-data.frame(t(blast.uc))
colnames(blast.uc)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc<-cbind(id,blast.uc)
colnames(blast.uc)<-c("id","X0","X1")}
else {
dhost<-merge(d2,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=F)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(virnames[j])
blast.uc<-cbind(id,hosts)
colnames(blast.uc)<-c("id","X0","X1")}}
for (j in 2:nvir){
d<-subset(valBlast,valBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
if (nrow(d2)==0){
blast.uc.s<-rep(1/ntax,ntax)
blast.uc.s<-data.frame(t(blast.uc.s))
colnames(blast.uc.s)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc.s<-cbind(id,blast.uc.s)
colnames(blast.uc.s)<-c("id","X0","X1") }
else {
dhost<-merge(d,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=T)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(d$query.acc.[1])
blast.uc.s<-cbind(id,hosts)
colnames(blast.uc.s)<-c("id","X0","X1")}
blast.uc<-rbind(blast.uc,blast.uc.s)}
f1_val<-merge(vals,blast.uc,by.x="Genbank.accession",by.y="id",all.x=F,all.y=T)
valID<-f1_val$Genbank.accession
set<-c("response",bp)
f1_val<-f1_val[,c(set)]
# Summarize blast hits from orphan set
oBlast<-read.csv(file="vecOrphanOut.out",col.names = c("query acc.", "subject acc.", "% identity", "alignment length", "mismatches", "gap opens", "q. start", "q. end"," s. start"," s. end"," evalue"," bit score"),header=F)
nvir<-length(unique(oBlast$query.acc.))
virnames<-unique(oBlast$query.acc.)
j=1
d<-subset(oBlast,oBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
for (z in 1:1){
if (nrow(d2)==0){
blast.uc<-rep(1/ntax,ntax)
blast.uc<-data.frame(t(blast.uc))
colnames(blast.uc)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc<-cbind(id,blast.uc)
colnames(blast.uc)<-c("id","X0","X1")}
else {
dhost<-merge(d2,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=F)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(virnames[j])
blast.uc<-cbind(id,hosts)
colnames(blast.uc)<-c("id","X0","X1")}}
for (j in 2:nvir){
d<-subset(oBlast,oBlast$query.acc.==virnames[j])
d2<-subset(d,d$X.evalue<ecutoff)
# Assign equal probability across all hosts if there is no good blast hit
if (nrow(d2)==0){
blast.uc.s<-rep(1/ntax,ntax)
blast.uc.s<-data.frame(t(blast.uc.s))
colnames(blast.uc.s)<-sort(unique(trains$response))
id<-as.character(virnames[j])
blast.uc.s<-cbind(id,blast.uc.s)
colnames(blast.uc.s)<-c("id","X0","X1") }
else {
dhost<-merge(d,trains,by.x="subject.acc.",by.y="Genbank.accession",all.x = T,all.y = F)
dhost$rel.support<-dhost$X..identity/sum(dhost$X..identity)
hosts<-tapply(dhost$rel.support,dhost$response,sum,na.rm=T)
hosts[is.na(hosts)]<-0
hosts<-t(data.frame(hosts))
hosts<-data.frame(hosts)
id<-as.character(d$query.acc.[1])
blast.uc.s<-cbind(id,hosts)
colnames(blast.uc.s)<-c("id","X0","X1")}
blast.uc<-rbind(blast.uc,blast.uc.s)}
f1_orphan<-merge(vecorphans,blast.uc,by.x="Genbank.accession",by.y="id",all.x=T,all.y=T,sort=F)
set<-c("X0","X1")
f1_orphan<-f1_orphan[,c(set)]
# Convert to h2o data frames
train<-as.h2o(f1_train)
test<-as.h2o(f1_test)
val<-as.h2o(f1_val)
orp<-as.h2o(f1_orphan)
# Clean up
rm(f1_orphan,f1_val,f1_test,f1_train,trainSeqs,testSeqs,valSeqs)
# Identity the response column
y <- "response"
# Identify the predictor columns
x <- setdiff(names(train), y)
# Convert response to factor
train[,y] <- as.factor(train[,y])
test[,y] <- as.factor(test[,y])
val[,y] <- as.factor(val[,y])
# Train and validate a grid of GBMs
gbm_params <- list(learn_rate = c(.001,seq(0.01, 0.2, .02)),
max_depth = seq(6, 15, 1),
sample_rate = seq(0.6, 1.0, 0.1),
col_sample_rate = seq(0.5, 1.0, 0.1),
ntrees=c(100,150,200),
min_rows=c(5,8,10))
search_criteria <- list(strategy = "RandomDiscrete",
max_models = 500,
stopping_rounds=10,
stopping_metric="misclassification",
stopping_tolerance=1e-3)
gbm_grid4 <- h2o.grid("gbm", x = x, y = y,
grid_id = "gbm_grid4",
training_frame = train,
validation_frame = val,
seed = 1,
hyper_params = gbm_params,
search_criteria = search_criteria)
gbm_gridperf <- h2o.getGrid(grid_id = "gbm_grid4",
sort_by = "accuracy",
decreasing = TRUE)
# Grab the model_id for the top GBM model
best_gbm_model_id <- gbm_gridperf@model_ids[[1]]
best_gbm <- h2o.getModel(best_gbm_model_id)
perf <- h2o.performance(best_gbm, test)
# Write model (if required)
#h2o.saveModel(best_gbm,path =paste("h2o_ab_PN_model",i))
# record with best settings
lr[i]<-as.numeric(gbm_gridperf@summary_table[1,2]) # learn_rate
sr[i]<-as.numeric(gbm_gridperf@summary_table[1,6]) # sample_rate
md[i]<-as.numeric(gbm_gridperf@summary_table[1,3]) # maxdepth
csr[i]<-as.numeric(gbm_gridperf@summary_table[1,1]) # col_sample_rate
mr[i]<-as.numeric(gbm_gridperf@summary_table[1,4]) # col_sample_rate
nt[i]<-as.numeric(gbm_gridperf@summary_table[1,5]) # col_sample_rate
# Print confusion matrix
cm1<-h2o.confusionMatrix(perf)
nclass<-length(unique(trains$response))
cm2<-cm1[1:nclass,1:nclass]
cm<-as.matrix(cm2)
norm_cm<-cm/rowSums(cm)
accuracy.v[i]=sum(diag(cm))/sum(cm)
pc.accuracy[i,]<-t(diag(cm)/rowSums(cm))
write.csv(norm_cm,file=paste("h2o_ab_PN_CM",i,".csv"))
# Retreive feature importance
vi <- h2o.varimp(best_gbm)
data2 <- vi[order(vi[,1],decreasing=FALSE),] # order alphabetically
vimps[,i]<-data2[,4]
# Orphan predictions
orp.pred <- h2o.predict(best_gbm, orp)
df<-orp.pred[,c(2:(ntax+1))]
df2<-as.data.frame(df)
row.names(df2)<-vecorphans$Virus.name
write.csv(df2,file=paste("AB_Orphans",i,".csv",sep="_"))
# Test set
test.pred<-h2o.predict(best_gbm,test[,2:length(names(test))]) # REMOVE host COLUMN
df2<-as.data.frame(test.pred)
row.names(df2)<-testID
write.csv(df2,file=paste("ABTestPred",i,".csv",sep="_"))
h2o.rm(c("gbm_grid4",best_gbm))
rm(vals,oBlast,valBlast,testBlast,allBlast)
}
accs<-data.frame(accuracy.v,pc.accuracy,lr,sr,md,csr,mr,nt)
colnames(accs)[2:(ntax+1)]<-row.names(cm)
row.names(vimps)<-data2$variable
# Write results summaries
write.csv(vimps,file="ArthropodBorne_PN_FI.csv",row.names = T)
write.csv(accs,file="ArthropodBorne_PN_out.csv",row.names=F)
write.csv(test.record,file="ArthropodBorne_PN_TestSets.csv",row.names=F)
# Null model accuracy
prob<-table(trains$response)/sum(table(trains$response))
res<-table(tests$response)
chanceAccurate<-round(sum(prob*res),digits=0)
tot<-sum(res)
nullAcc<-chanceAccurate/tot
print(nullAcc)