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genes <- c("Q09428", "P15056", "O00555", "P21802",
"Q14654", "P07949", "Q99250", "Q14524.clean", "P04637")
gene.names <- c("ABCC8", "BRAF", "CACNA1A", "FGFR2",
"KCNJ11", "RET", "SCN2A", "SCN5A", "TP53")
py.path = '/share/descartes/Users/gz2294/miniconda3/envs/RESCVE/bin/python'
source('./AUROC.R')
summary.df <- data.frame()
plots <- list()
source('./prepare.biochem.R')
ALL <- read.csv('figs/ALL.csv', row.names = 1, na.strings = c('.', 'NA'))
ALL <- prepare.unique.id(ALL)
pick.cond <- 'auc'
for (i in 1:length(genes)) {
gene <- genes[i]
for (subset in c(1,2,4,6,8)) {
for (fold in 0:4) {
aucs <- c()
if (subset == 8) {
gene.test.res <- read.csv(paste0('PreMode/', gene, '/testing.fold.' ,fold, '.4fold.csv'))
log.yaml <- yaml::read_yaml(paste0('../scripts/PreMode/',
gene, '.5fold/', gene, '.fold.', fold, '.yaml'))
# compare with large window
gene.test.res.lw <- read.csv(paste0('PreMode/', gene, '.large.window/testing.fold.' ,fold, '.4fold.csv'))
gene.train.res <- read.csv(paste0('PreMode/', gene, '/training.fold.' ,fold, '.4fold.csv'))
gene.train.res.lw <- read.csv(paste0('PreMode/', gene, '.large.window/training.fold.' ,fold, '.4fold.csv'))
} else {
gene.test.res <- read.csv(paste0('PreMode/', gene, '/testing.subset.', subset, '.fold.' ,fold, '.4fold.csv'))
log.yaml <- yaml::read_yaml(paste0('../scripts/PreMode/',
gene, '.subset.', subset, '.5fold/', gene, '.subset.', subset, '.fold.', fold, '.yaml'))
# compare with large window
gene.test.res.lw <- read.csv(paste0('PreMode/', gene, '.large.window/testing.subset.', subset, '.fold.' ,fold, '.4fold.csv'))
gene.train.res <- read.csv(paste0('PreMode/', gene, '/training.subset.', subset, '.fold.' ,fold, '.4fold.csv'))
gene.train.res.lw <- read.csv(paste0('PreMode/', gene, '.large.window/training.subset.', subset, '.fold.' ,fold, '.4fold.csv'))
}
tr.auc <- plot.AUC(gene.train.res$score, rowMeans(gene.train.res[,paste0('logits.FOLD.', 0:3)]))$auc
tr.auc.lw <- plot.AUC(gene.train.res.lw$score, rowMeans(gene.train.res.lw[,paste0('logits.FOLD.', 0:3)]))$auc
tr.loss <- rowMeans(gene.train.res[,paste0('min_loss.FOLD.', 0:3)])[1]
tr.loss.lw <- rowMeans(gene.train.res.lw[,paste0('min_loss.FOLD.', 0:3)])[1]
if (pick.cond == 'auc') {
cond <- tr.auc.lw > tr.auc
} else if (pick.cond == 'loss') {
cond <- tr.loss > tr.loss.lw
} else if (pick.cond == 'auc+loss') {
cond <- tr.auc.lw/tr.loss.lw > tr.auc/tr.loss
} else {
cond <- F
}
# do 4 fold auc
if (cond) {
auc <- plot.AUC(gene.test.res.lw$score, rowMeans(gene.test.res.lw[,paste0('logits.FOLD.', 0:3)]))
} else {
auc <- plot.AUC(gene.test.res$score, rowMeans(gene.test.res[,paste0('logits.FOLD.', 0:3)]))
}
aucs <- c(aucs, auc$auc)
# do random forest
gene.train <- read.csv(paste0('../', log.yaml$data_file_train))
gene.test <- read.csv(paste0('../', log.yaml$data_file_test))
# get the same training/val split
fold.splits <- reticulate::py_load_object(paste0('../', log.yaml$log_dir, '/fold_split.pkl'))
# prepare unique id
gene.train <- prepare.unique.id(gene.train)
gene.test <- prepare.unique.id(gene.test)
train.biochem <- prepare.biochemical(ALL[match(gene.train$unique.id, ALL$unique.id),])
test.biochem <- prepare.biochemical(ALL[match(gene.test$unique.id, ALL$unique.id),])
rownames(train.biochem) <- gene.train[,1]
rownames(test.biochem) <- gene.test[,1]
rf.aucs <- c()
for (f in 1:4) {
# get split info
val.gof.idx <- fold.splits[[1]][[f]]
val.lof.idx <- fold.splits[[2]][[f]]
train.idx <- !gene.train[,1] %in% c(val.gof.idx, val.lof.idx)
# call python on elastic net
train.biochem.file <- tempfile()
test.biochem.file <- tempfile()
train.label.file <- tempfile()
test.label.file <- tempfile()
output.file <- tempfile()
write.csv(train.biochem[train.idx,],
file = train.biochem.file)
write.csv(test.biochem,
file = test.biochem.file)
write.csv(gene.train[train.idx,], file = train.label.file)
write.csv(gene.test, file = test.label.file)
# call python on random forest
res <- system(paste0(py.path, ' ',
'random.forest.glof.py ',
train.biochem.file, ' ',
train.label.file, ' ',
test.biochem.file, ' ',
test.label.file), intern = T)
rf.aucs <- c(rf.aucs, as.numeric(strsplit(res, split = '=')[[1]][2]))
}
aucs <- c(aucs, mean(el.aucs), mean(rf.aucs))
summary.df <- rbind(summary.df,
data.frame(auc=aucs,
use.lw=c(cond, NA),
model=c('PreMode.transfer', 'random.forest'),
seed=fold,
gene=gene.names[i],
subset=subset,
ngof.train=sum(gene.train$score==1),
nlof.train=sum(gene.train$score==-1),
ngof.test=sum(gene.test$score==1),
nlof.test=sum(gene.test$score==-1)))
}
}
}
write.csv(summary.df, file = 'figs/fig.5e.prepare.csv')
library(ggplot2)
summary.df <- read.csv('figs/fig.5e.prepare.csv', row.names = 1)
plots <- list()
library(patchwork)
for (i in 1:length(genes)) {
task <- gene.names[i]
task.res <- summary.df[startsWith(summary.df$gene, task),]
task.res <- task.res[,!is.na(task.res[1,])]
task.plots <- list()
data.points <- paste0(task.res$ngof.train[task.res$seed==0 & task.res$model=="PreMode.transfer"],
" | ",
task.res$nlof.train[task.res$seed==0 & task.res$model=="PreMode.transfer"])
num.models <- length(unique(summary.df$model))
p <- ggplot(task.res, aes(x=subset, y=auc, col=model)) +
geom_point(alpha=0.2) +
# geom_line(aes(y=zero.shot), linetype="dotted") +
stat_smooth(geom='line', span=0.3, se = FALSE, alpha=0.5) + scale_y_continuous(breaks=seq(0.4, 1, 0.2), limits = c(0.4, 1.0)) +
scale_x_continuous(breaks=c(1, 2, 4, 6, 8),
labels=paste0(data.points,
c(" (10%)", " (20%)", " (40%)", " (60%)", " (80%)"))) +
stat_summary(data = task.res,
aes(x=as.numeric((subset))+0.4*(as.numeric((model)))/num.models-0.2*(num.models+1)/num.models,
y = auc, col=model),
fun.data = mean_se, geom = "errorbar", width = 0.2) +
stat_summary(data = task.res,
aes(x=as.numeric((subset))+0.4*(as.numeric((model)))/num.models-0.2*(num.models+1)/num.models,
y = auc, col=model),
fun.data = mean_se, geom = "point") +
theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle(paste0(task)) + ggeasy::easy_center_title() + xlab("training data size, format: GoF | LoF (%)")
plots[[i]] <- p
}
library(patchwork)
p <- plots[[1]] + plots[[2]] + plots[[3]] + plots[[4]] + plots[[5]] + plots[[6]] + plots[[7]] + plots[[8]] + plots[[9]] + plot_layout(ncol=3)
summary.df <- read.csv('figs/fig.5e.prepare.csv', row.names = 1)
summary.df <- summary.df[summary.df$model %in% c('PreMode.transfer', 'random.forest'),]
model.dic <- c("PreMode.transfer"="Supervised: PreMode",
"random.forest"="Supervised: Random Forest")
summary.df$model <- model.dic[summary.df$model]
summary.df$model <- factor(summary.df$model, levels = c("Supervised: PreMode",
"Supervised: Random Forest"))
gene.names <- unique(summary.df$gene)
plots <- list()
library(patchwork)
for (i in 1:length(genes)) {
task <- gene.names[i]
task.res <- summary.df[startsWith(summary.df$gene, task),]
task.res <- task.res[,!is.na(task.res[1,])]
task.plots <- list()
data.points <- paste0(task.res$ngof.train[task.res$seed==0 & task.res$model=="Supervised: PreMode"],
" | ",
task.res$nlof.train[task.res$seed==0 & task.res$model=="Supervised: PreMode"])
num.models <- length(unique(summary.df$model))
p <- ggplot(task.res, aes(x=subset, y=auc, col=model)) +
geom_point(alpha=0) +
# geom_line(aes(y=zero.shot), linetype="dotted") +
stat_smooth(geom='line', span=0.3, se = FALSE, alpha=0.5) +
scale_y_continuous(breaks=seq(0.4, 1, 0.2), limits = c(0.4, 1.0)) +
scale_x_continuous(breaks=c(1, 2, 4, 6, 8),
labels=paste0(data.points,
c(" (10%)", " (20%)", " (40%)", " (60%)", " (80%)"))) +
stat_summary(data = task.res,
aes(x=as.numeric((subset))+0.4*(as.numeric((model)))/num.models-0.2*(num.models+1)/num.models,
y = auc, col=model),
fun.data = mean_se, geom = "errorbar", width = 0.2) +
stat_summary(data = task.res,
aes(x=as.numeric((subset))+0.4*(as.numeric((model)))/num.models-0.2*(num.models+1)/num.models,
y = auc, col=model),
fun.data = mean_se, geom = "point") +
theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="bottom",
legend.direction="horizontal") +
ggtitle(paste0(task)) + ggeasy::easy_center_title() + xlab("training data size, format: GoF | LoF (%)")
if (i != 5) {
p <- p + guides(color=FALSE)
}
plots[[i]] <- p
}
library(ggpubr)
p <- ggarrange(plots[[6]], plots[[5]], plots[[3]],
plots[[2]], plots[[8]], plots[[7]],
plots[[9]], plots[[1]], plots[[4]],
ncol=3, nrow=3, common.legend = TRUE, legend="bottom")
# plot weighted average
summary.df <- read.csv('figs/fig.5e.prepare.csv', row.names = 1)
summary.df <- summary.df[summary.df$model %in% c('PreMode.transfer', 'random.forest'),]
model.dic <- c("PreMode.transfer"="PreMode",
"random.forest"="Random Forest")
summary.df$model <- model.dic[summary.df$model]
summary.df$model <- factor(summary.df$model, levels = c("PreMode", "Random Forest"))
# plot the task weighted averages as well as task size weighted error bars
uniq.result.plot <- summary.df[summary.df$seed==0,]
for (i in 1:dim(uniq.result.plot)[1]) {
aucs <- summary.df$auc[summary.df$model==uniq.result.plot$model[i] &
summary.df$gene==uniq.result.plot$gene[i] &
summary.df$subset==uniq.result.plot$subset[i]]
# aucs <- aucs[aucs > 0]
uniq.result.plot$auc[i] = mean(aucs, na.rm=T)
uniq.result.plot$auc.se[i] = sd(aucs, na.rm=T) / sqrt(length(aucs))
}
task.dic <- unique(uniq.result.plot$gene)
plots <- list()
num.models <- unique(uniq.result.plot$model)
library(patchwork)
for (i in 1:length(task.dic)) {
task <- (genes)[i]
task.res <- uniq.result.plot[uniq.result.plot$gene == gene.names[i],]
task.res <- task.res[,!is.na(task.res[1,])]
data.points <- paste0(task.res$ngof.train[task.res$seed==0 & task.res$model=="PreMode"],
" | ",
task.res$nlof.train[task.res$seed==0 & task.res$model=="PreMode"])
task.plots <- list()
p <- ggplot(task.res, aes(x=subset, y=auc, col=model)) +
geom_point() +
geom_errorbar(aes(ymin=auc-auc.se, ymax=auc+auc.se), width=.4) +
# geom_line(aes(y=zero.shot), linetype="dotted") +
geom_line() +
scale_y_continuous(breaks=seq(0.4, 1, 0.2), limits = c(0.4, 1.0)) +
scale_x_continuous(breaks=c(1, 2, 4, 6, 8),
labels=paste0(data.points,
c(" (10%)", " (20%)", " (40%)", " (60%)", " (80%)"))) +
ylab('Spearman rho') +
theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle(paste0(task)) + ggeasy::easy_center_title() + xlab("training data size (%)")
plots[[i]] <- p
}
library(patchwork)
p <- ggarrange(plots[[6]], plots[[5]], plots[[3]],
plots[[2]], plots[[8]], plots[[7]],
plots[[9]], plots[[1]], plots[[4]],
ncol=3, nrow=3, common.legend = TRUE, legend="bottom")
# aggregate across models
uniq.model.result.plot <- uniq.result.plot[!duplicated(uniq.result.plot[,c('model', "subset")]),]
for (i in 1:dim(uniq.model.result.plot)[1]) {
aucs <- uniq.result.plot$auc[uniq.result.plot$model == uniq.model.result.plot$model[i] &
uniq.result.plot$subset == uniq.model.result.plot$subset[i]]
auc.ses <- uniq.result.plot$auc.se[uniq.result.plot$model == uniq.model.result.plot$model[i] &
uniq.result.plot$subset == uniq.model.result.plot$subset[i]]
model.gene.names <- gsub(":.*", "", uniq.result.plot$gene[uniq.result.plot$model == uniq.model.result.plot$model[i] &
uniq.result.plot$subset == uniq.model.result.plot$subset[i]])
subsets <- uniq.result.plot$subset[uniq.result.plot$model == uniq.model.result.plot$model[i] &
uniq.result.plot$subset == uniq.model.result.plot$subset[i]]
# get data set sizes
ngof <- summary.df$ngof.train[summary.df$seed==0 &
summary.df$model=="PreMode" &
summary.df$subset == uniq.model.result.plot$subset[i]]
nlof <- summary.df$nlof.train[summary.df$seed==0 &
summary.df$model=="PreMode" &
summary.df$subset == uniq.model.result.plot$subset[i]]
data.points <- 1 / (1/ngof + 1/nlof)
gene.ids <- genes[match(model.gene.names, gene.names)]
# use harmonic prior of data points
uniq.model.result.plot$auc[i] <- sum(aucs * data.points, na.rm = T) / sum(data.points)
uniq.model.result.plot$auc.se[i] <- sum(auc.ses * data.points, na.rm = T) / sum(data.points)
}
p <- ggplot(uniq.model.result.plot, aes(x=subset, y=auc, col=model)) +
geom_point() +
geom_errorbar(aes(ymin=auc-auc.se, ymax=auc+auc.se), width=.2) +
geom_line() +
scale_y_continuous(breaks=seq(0.4, 1, 0.2), limits = c(0.4, 1.0)) +
scale_x_continuous(breaks=c(1, 2, 4, 6, 8),
labels=paste0(c(" (10%)", " (20%)", " (40%)", " (60%)", " (80%)"))) +
ylab('AUC') +
theme_bw() +
theme(axis.text.x = element_text(angle=60, vjust = 1, hjust = 1),
text = element_text(size = 16),
plot.title = element_text(size=15),
legend.text = element_text(size=10),
axis.title.x = element_text(size=12),
legend.position="bottom",
legend.direction="horizontal") +
ggtitle("Weighted Average of Model AUC\non subsample of training") +
ggeasy::easy_center_title() + xlab("training data size (% of full G/LoF dataset)")
ggsave('figs/fig.5e.pdf', p, width = 4, height = 5)
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