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library(ggplot2)
source('./AUROC.R')
source('./bind_rows.R')
# next test whole genome-split and within in Gene AUC
icc <- read.csv('figs/ALL.csv', row.names = 1)
icc$score[icc$score == 0] <- -1
icc$unique.id <- paste(icc$uniprotID, icc$ref, icc$pos.orig, icc$alt, sep = ":")
pfam.list <- read.csv('../scripts/gene.txt', header = F)
pfam.even.split <- pfam.list$V1[grep('even.uniprotID', pfam.list$V1, invert = T)]
pfam.even.split <- pfam.even.split[grep('Heyne', pfam.even.split, invert = T)]
result.df <- data.frame()
for (seed in 0:4) {
icc.test <- data.frame()
icc.train <- icc[!icc$uniprotID %in% pfam.even.split,]
for (uid in pfam.even.split) {
icc.test <- my.bind.rows(icc.test, read.csv(paste0('../data.files/ICC.seed.',
seed, '/', uid, '/testing.csv')))
icc.train <- my.bind.rows(icc.train, read.csv(paste0('../data.files/ICC.seed.',
seed, '/', uid, '/training.csv')))
}
train.tmp <- tempfile()
test.tmp <- tempfile()
write.csv(icc.train[,c('uniprotID', 'score')], train.tmp)
write.csv(icc.test[,c('uniprotID', 'score')], test.tmp)
rf.command <- paste0('/share/descartes/Users/gz2294/miniconda3/envs/r4-base/bin/python ', 'random.forest.process.classifier.py ',
train.tmp, ' ',
test.tmp)
rf.result <- system(rf.command, intern = T)
result.df <- rbind(result.df, data.frame(uid = 'ALL',
rf.auc = as.numeric(gsub('Testing AUC: ', '', rf.result)),
seed = seed,
gof = sum(icc.train$score==1) + sum(icc.test$score==1),
lof = sum(icc.train$score==-1) + sum(icc.test$score==-1)))
}
for (seed in 1:4) {
for (uid in pfam.even.split) {
icc.train <- icc[!icc$uniprotID %in% pfam.even.split,]
icc.test <- read.csv(paste0('../data.files/ICC.seed.', seed, '/', uid, '/testing.csv'))
icc.test$unique.id <- paste(icc.test$uniprotID, icc.test$ref, icc.test$pos.orig, icc.test$alt, sep = ":")
icc.train <- icc[!icc$unique.id %in% icc.test,]
train.tmp <- tempfile()
test.tmp <- tempfile()
write.csv(icc.train[,c('uniprotID', 'score')], train.tmp)
write.csv(icc.test[,c('uniprotID', 'score')], test.tmp)
rf.command <- paste0('/share/descartes/Users/gz2294/miniconda3/envs/r4-base/bin/python ', 'random.forest.process.classifier.py ',
train.tmp, ' ',
test.tmp)
rf.result <- system(rf.command, intern = T)
testing.points <- read.csv(paste0('../data.files/ICC.seed.', seed, '/', uid, '/testing.csv'))
training.points <- read.csv(paste0('../data.files/ICC.seed.', seed, '/', uid, '/training.csv'))
result.df <- rbind(result.df, data.frame(uid = uid,
rf.auc = 0.5,
seed = seed,
gof = sum(icc.test$score==1),
lof = sum(icc.test$score==-1)))
}
}
uniprotID.dic <- c("P21802"="FGFR2", "P15056"="BRAF", "P07949"="RET", "P04637"="TP53",
"Q09428"="ABCC8",
"O00555"="CACNA1A", "Q14654"="KCNJ11",
"Q99250"="SCN2A", "Q14524"="SCN5A",
"IonChannel.chps"="Na+/Ca2+ Channel",
"IonChannel"="Na+/Ca2+ Channel",
"IPR000719"="Protein Kinase Domain",
"IPR001806"="Small GTPase",
"IPR001245"="Protein Kinase Catalytic Domain",
"IPR016248"="Fibroblast Growth Factor Receptor Family",
"IPR005821"="Ion Transport Domain",
"IPR027359"="Voltage-dependent Channel Domain"
)
result.df$uid[result.df$uid == 'ALL'] <- '0: ALL genes'
result.df$uid[result.df$uid %in% names(uniprotID.dic)] <- uniprotID.dic[result.df$uid[result.df$uid %in% names(uniprotID.dic)]]
num.models <- 1
p <- ggplot(result.df, aes(x=uid, y=rf.auc)) +
geom_point(alpha=0.2) +
stat_summary(data = result.df,
aes(x=as.numeric(factor(uid))+0.4/num.models-0.2*(num.models+1)/num.models,
y = rf.auc),
fun.data = mean_se, geom = "errorbar", width = 0.2) +
stat_summary(data = result.df,
aes(x=as.numeric(factor(uid))+0.4/num.models-0.2*(num.models+1)/num.models,
y = rf.auc),
fun.data = mean_se, geom = "point") +
xlab("Task Name") + ylab("random forest classifier AUC") + theme_bw() +
ggtitle('Random Forest Classifier trained on all genes with gene names') + ggeasy::easy_center_title() +
theme(axis.text.x = element_text(angle=70, vjust = 1, hjust = 1),
legend.position="bottom",
legend.direction="horizontal")
ggsave(p, filename = "figs/fig.sup.7.pdf", height = 4, width = 6)
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