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result.plot <- readRDS('figs/fig.5.prepare.RDS')
result.plot <- result.plot[result.plot$task.type %in% c("Gene.Domain", "Gene.Gene"),]
result.plot$use.lw <- F
pick.cond <- 'auc'
# get unique models
uniq.models <- unique(gsub('.lw', '', result.plot$model))
# only keep the original models
uniq.models <- uniq.models[grepl('/$', uniq.models)]
# get unique genes, remove Q14524
uniq.genes <- unique(result.plot$task.id)
# for each gene and each fold, decide weather to use large window
for (g in uniq.genes) {
for (m in uniq.models) {
for (f in 0:4) {
lw.loss <- result.plot$val.loss[result.plot$model == paste0(m, '.lw') & result.plot$task.id == g & result.plot$fold==f]
loss <- result.plot$val.loss[result.plot$model == m & result.plot$task.id == g & result.plot$fold==f]
lw.tr.auc <- result.plot$tr.auc[result.plot$model == paste0(m, '.lw') & result.plot$task.id == g & result.plot$fold==f]
tr.auc <- result.plot$tr.auc[result.plot$model == m & result.plot$task.id == g & result.plot$fold==f]
if (pick.cond == 'auc') {
cond <- !is.na(mean(lw.tr.auc)) & lw.tr.auc > tr.auc
} else if (pick.cond == 'loss') {
cond <- !is.na(mean(lw.loss)) & loss > lw.loss
} else if (pick.cond == 'auc+loss') {
cond <- !is.na(lw.loss) & !is.na(lw.tr.auc) & (lw.tr.auc/lw.loss > tr.auc/loss)
} else if (pick.cond == 'lw') {
cond <- T
} else {
cond <- F
}
if (cond) {
# use lw
to.remove <- which(result.plot$model == m & result.plot$task.id == g & result.plot$fold==f)
to.anno <- which(result.plot$model == paste0(m, '.lw') & result.plot$task.id == g & result.plot$fold==f)
result.plot$model[to.anno] <- m
result.plot$use.lw[to.anno] <- T
result.plot <- result.plot[-to.remove,]
} else {
to.remove <- which(result.plot$model == paste0(m, '.lw') & result.plot$task.id == g & result.plot$fold==f)
result.plot <- result.plot[-to.remove,]
}
}
}
}
result.plot <- result.plot[result.plot$model %in% c("PreMode/"),]
model.dic <- c("PreMode/"="1: PreMode")
result.plot$HGNC <- NA
for (i in 1:dim(result.plot)[1]) {
result.plot$HGNC[i] <- gsub('Gene: ', '', strsplit(result.plot$task.name[i], "\\.")[[1]][1])
}
result.plot$model <- model.dic[result.plot$model]
# rename result plot model
result.plot$model <- "1: PreMode (Gene Only)"
result.plot$model[grepl('\\(Family\\)', result.plot$task.name)] <- "2: PreMode (Protein Family)"
# rename result task name
result.plot$task.name <- gsub('Gene: ', '', result.plot$task.name)
result.plot$task.name <- gsub('\\.\\(Gene Only\\)', '', result.plot$task.name)
result.plot$task.name <- gsub('\\.\\(Family\\)', '', result.plot$task.name)
result.plot$task.name <- gsub('\\.', ': ', result.plot$task.name)
rename.dict <- c('KCNJ11: Potassium Channel Inwardly Rectifying Kir Cytoplasmic Domain'='KCNJ11: Potassium Channel Inwardly\nRectifying Kir Cytoplasmic Domain',
'FGFR2: Fibroblast Growth Factor Receptor Family'='FGFR2: Fibroblast Growth Factor\nReceptor Family',
'RET: Tyrosine Protein Kinase Catalytic Domain'='RET: Tyrosine Protein Kinase\nCatalytic Domain')
result.plot$task.name[result.plot$task.name %in% names(rename.dict)] <- rename.dict[result.plot$task.name[result.plot$task.name %in% names(rename.dict)]]
num.models <- length(unique(result.plot$model))
p <- ggplot(result.plot, aes(y=auc, x=task.name, col=model)) +
geom_point(alpha=0) +
stat_summary(data = result.plot,
aes(x=as.numeric(factor(task.name))+0.4*(as.numeric(factor(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 = result.plot,
aes(x=as.numeric(factor(task.name))+0.4*(as.numeric(factor(model)))/num.models-0.2*(num.models+1)/num.models,
y = auc, col=model),
fun.data = mean_se, geom = "point") +
labs(x = "task", y = "AUC") +
ggtitle('PreMode trained on Gene/Protein Family data') +
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.title = element_blank(),
legend.text = element_text(size=10),
legend.position="bottom",
legend.direction="horizontal") +
coord_flip() + guides(col=guide_legend(ncol=3)) +
ggeasy::easy_center_title() +
ylim(0.4, 1) + xlab('task: Genetics Level Mode of Action')
ggsave(paste0('figs/fig.5f.pdf'), p, width = 8, height = 5)
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