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figure-dp-peaks-interactive.R
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figure-dp-peaks-interactive.R
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works_with_R("3.1.1", reshape2="1.2.2", dplyr="0.2",
"tdhock/ggplot2@aac38b6c48c016c88123208d497d896864e74bd7",
"tdhock/animint@4b6a459b4e7425831ca4f3bb2b81f7305835252b")
load("dp.peaks.interactive.RData")
load("dp.peaks.regression.RData")
load("dp.peaks.baseline.RData")
roc <- do.call(rbind, dp.peaks.roc) %.%
mutate(algorithm="PeakSegDP")
roc.chosen <- do.call(rbind, dp.peaks.roc.chosen) %.%
mutate(algorithm="PeakSegDP")
broc <- do.call(rbind, dp.peaks.baseline.roc) %.%
mutate(thresh=param.name,
algorithm=sub(".trained", "", algorithm))
broc.chosen <- do.call(rbind, dp.peaks.baseline.chosen) %.%
mutate(thresh=param.name,
algorithm=sub(".trained", "", algorithm))
default.params <-
c(macs.trained="1.30103", hmcan.broad.trained="2.30258509299405")
default.list <- list()
for(curve.name in names(dp.peaks.baseline.roc)){
one.curve <- dp.peaks.baseline.roc[[curve.name]]
default.param <- default.params[[as.character(one.curve$algorithm[1])]]
one.param <- subset(one.curve, param.name == default.param) %.%
mutate(algorithm=sub(".trained", "", algorithm),
thresh=param.name)
default.list[[curve.name]] <- one.param
}
default.df <- do.call(rbind, default.list)
str(roc)
str(roc.chosen)
str(broc)
str(broc.chosen)
roc.cols <- c("set.name", "set.i", "FPR", "TPR", "algorithm", "thresh")
all.roc <- rbind(roc[, roc.cols], broc[, roc.cols]) %.%
mutate(set.name=as.character(set.name),
testSet=paste(set.name, "split", set.i))
all.dots <-
rbind(data.frame(roc.chosen[, roc.cols], model.type="trained"),
data.frame(broc.chosen[, roc.cols], model.type="trained"),
data.frame(default.df[, roc.cols], model.type="default")) %.%
mutate(set.name=as.character(set.name),
testSet=paste(set.name, "split", set.i))
longErrors <- dp.peaks.interactive$regionErrors %.%
group_by(testSet, test.chunk, algorithm) %.%
summarise(fp=sum(fp),
fn=sum(fn)) %.%
group_by(testSet) %.%
mutate(chunk.x=as.numeric(factor(as.character(test.chunk))),
errors=fp+fn)
longErrors
fp.fn <- melt(longErrors, measure.vars=c("fp", "fn"),
variable.name="error.type",
value.name="errors")
wideErrors <-
dcast(longErrors,
testSet + test.chunk ~ algorithm,
value.var="errors")
comp <- function(algorithm){
wideErrors$competitor <- algorithm
wideErrors$competitor.errors <- wideErrors[,algorithm]
wideErrors
}
competitors <- rbind(comp("macs.trained"), comp("hmcan.broad.trained"))
chunkErrors <- dp.peaks.interactive$regionErrors %.%
group_by(testSet, test.chunk, algorithm, sample.id) %.%
summarise(fp=sum(fp),
fn=sum(fn)) %.%
mutate(errors=fp+fn)
others <- chunkErrors %.%
group_by(testSet, test.chunk, sample.id) %.%
filter(algorithm != "PeakSeg") %.%
summarise(min.other=min(errors),
max.other=max(errors))
chunk.others <- inner_join(chunkErrors, others)
chunk.diffs <- chunk.others %.%
filter(algorithm=="PeakSeg") %.%
mutate(diff1=errors-min.other,
diff2=errors-max.other) %.%
arrange(diff1, errors, diff2) %.%
group_by(testSet, test.chunk) %.%
mutate(rank=seq_along(diff1))
algo.order <- c("hmcan.broad.trained", "macs.trained", "PeakSeg")
afactor <- function(x)factor(x, algo.order)
algo.y <- -0.1*(3:1)
names(algo.y) <- algo.order
chunk.ordered <-
inner_join(chunk.diffs, chunkErrors,
c("testSet", "test.chunk", "sample.id")) %.%
mutate(algorithm=afactor(algorithm.y),
errors=errors.y)
dp.peaks.interactive$regionErrors %.%
group_by(algorithm) %.%
summarise(regions=n())
regions <- dp.peaks.interactive$regionErrors %.%
filter(algorithm == algorithm[1])
regionErrors <- dp.peaks.interactive$regionErrors %.%
mutate(algo.y=algo.y[as.character(algorithm)])
peaks <- dp.peaks.interactive$peaks %.%
mutate(algo.y=algo.y[as.character(algorithm)])
## TODO: label parameter values as well.
peakLabels <- data.frame(algo.y, algorithm=names(algo.y))
ann.colors <-
c(noPeaks="#f6f4bf",
peakStart="#ffafaf",
peakEnd="#ff4c4c",
peaks="#a445ee")
baselines <- dp.peaks.interactive$baselineParams %.%
mutate(competitor=algorithm)
test.pred <- dp.peaks.prediction %.%
filter(set=="test")
train.pred <- dp.peaks.prediction %.%
filter(set=="train")
hgTracks <- "http://ucscbrowser.genap.ca/cgi-bin/hgTracks"
psplits <- dp.peaks.interactive$splitErrors %.%
filter(algorithm=="PeakSeg")
cat(deparse(RColorBrewer::brewer.pal(3, "Dark2")))
algo.colors <-
c(hmcan.broad="#1B9E77",
macs="#D95F02",
PeakSegDP="#7570B3",
hmcan.broad.trained="#1B9E77",
macs.trained="#D95F02",
PeakSeg="#7570B3")
viz <-
list(splits=ggplot()+
scale_color_manual(values=algo.colors, guide="none")+
geom_point(aes(percent, algorithm, clickSelects=testSet,
color=algorithm),
data=dp.peaks.interactive$splitErrors, size=5, alpha=0.6)+
facet_grid(.~set.name)+
ggtitle("select data set and train/test split")+
xlab("percent test error")+
theme_animint(width=1200, height=150),
samples=ggplot()+
scale_color_manual(values=algo.colors, guide="none")+
theme(axis.line.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme_animint(height=150)+
xlab("<- PeakSeg better ----- samples ----- PeakSeg worse ->")+
ylab("algorithm")+
ggtitle("error counts, select sample")+
geom_text(aes(rank, algorithm, label=errors,
color=algorithm,
clickSelects=sample.id,
showSelected=testSet,
showSelected2=test.chunk),
data=chunk.ordered),
competitors=ggplot()+
geom_point(aes(competitor.errors, PeakSeg,
clickSelects=test.chunk,
showSelected=testSet),
data=competitors, size=5, alpha=0.6)+
geom_text(aes(80, 100, color=algorithm,
label=paste0("selected parameter=", param.name),
showSelected=testSet),
data=baselines)+
scale_color_manual(values=algo.colors, guide="none")+
scale_x_continuous(paste("incorrectly predicted annotated regions",
"(competitor)"),
breaks=seq(0, 200, by=25))+
scale_y_continuous("incorrectly predicted annotated regions (PeakSeg)",
breaks=seq(0, 200, by=25))+
ggtitle("Test error comparison, select test chunk")+
theme_animint(width=500, height=300)+
facet_grid(.~competitor, labeller=label_both)+
coord_equal()+
geom_segment(aes(x, y, xend=xend, yend=yend),
data=data.frame(x=0, y=0, xend=100, yend=100)),
roc=ggplot()+
theme_animint(width=300, height=300)+
geom_text(aes(0.5, 1.01, label=testSet,
showSelected=testSet),
data=psplits)+
geom_path(aes(FPR, TPR, color=algorithm, group=algorithm,
showSelected=testSet),
data=all.roc)+
geom_point(aes(FPR, TPR, color=algorithm, fill=model.type,
showSelected=testSet),
pch=21, data=all.dots)+
scale_fill_manual(values=c(trained="white", default="black"))+
coord_equal()+
scale_color_manual(values=algo.colors)+
scale_x_continuous("False positive rate",
breaks=seq(0, 1, by=1/2))+
ggtitle("Test ROC curves")+
scale_y_continuous("True positive rate",
breaks=seq(0, 1, by=1/2)),
extrapolation=ggplot()+
theme_animint(width=300, height=300)+
geom_text(aes(5, 16.5, label=testSet,
showSelected=testSet),
data=psplits)+
ggtitle("PeakSeg model feature space")+
geom_contour(aes(log.max.coverage, log.total.weight, z=log.lambda,
color=..level..,
showSelected=testSet),
data=dp.peaks.grid)+
geom_point(aes(log.max.coverage, log.total.weight,
fill=set,
showSelected=testSet),
data=train.pred, pch=21, alpha=1/10)+
geom_polygon(aes(log.max.coverage, log.total.weight, group=test.chunk,
showSelected=testSet,
clickSelects=test.chunk),
data=dp.peaks.polygon, alpha=6/10, size=3)+
## geom_segment(aes(log.max.coverage, log.total.weight,
## xend=log.max.coverage.end, yend=log.total.weight.end,
## showSelected=testSet,
## clickSelects=test.chunk),
## data=dp.peaks.segment, alpha=6/10, size=3)+
xlab("log.max.coverage feature")+
ylab("log.total.weight feature")+
geom_text(aes(log.max.coverage, log.total.weight,
label=test.errors,
showSelected=testSet,
showSelected2=test.chunk,
clickSelects=sample.id),
data=test.pred)+
geom_blank(aes(log.max.coverage, log.total.weight,
fill=set,
showSelected=testSet,
showSelected2=test.chunk),
data=test.pred)+
guides(fill=guide_legend(
override.aes=list(alpha=1/2)))+
##scale_color_gradient("log.penalty", low="white", high="blue")+
scale_color_gradient2("log.penalty")+
scale_fill_manual(values=c(train="red", test="black")),
profile=ggplot()+
geom_text(aes(0, 1, label=sprintf("%s max=%d", sample.id, max.coverage),
showSelected=test.chunk,
showSelected2=sample.id),
data=dp.peaks.interactive$profile.info, hjust=0)+
geom_text(aes(0.5, 1,
label=sprintf("%.1fkb on %s", bases/1e3, chunkChrom),
href=sprintf("%s?db=hg19&position=%s:%d-%d",
hgTracks, chunkChrom, expandStart, expandEnd),
showSelected=test.chunk),
data=dp.peaks.interactive$chunks)+
theme(axis.line=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank())+
theme_animint(width=1300)+
geom_rect(aes(xmin=chromStartNorm, xmax=chromEndNorm,
ymin=-0.4, ymax=1,
fill=annotation,
showSelected=testSet,
showSelected2=test.chunk,
showSelected3=sample.id),
data=regions, linetype="none")+
geom_rect(aes(xmin=chromStartNorm, xmax=chromEndNorm,
ymin=algo.y-0.025, ymax=algo.y+0.025,
fill=annotation,
linetype=status,
showSelected=testSet,
showSelected2=test.chunk,
showSelected3=sample.id),
color="black", size=2,
data=regionErrors)+
geom_segment(aes(ifelse(chromStartNorm < 0, 0, chromStartNorm),
algo.y,
xend=ifelse(chromEndNorm > 1, 1, chromEndNorm),
yend=algo.y,
showSelected=testSet,
showSelected2=test.chunk,
showSelected3=sample.id),
data=peaks,
color="deepskyblue")+
geom_point(aes(ifelse(chromEndNorm > 1, 1, chromEndNorm),
algo.y,
showSelected=testSet,
showSelected2=test.chunk,
showSelected3=sample.id),
data=peaks,
color="deepskyblue")+
geom_text(aes(0, algo.y, label=sub(".trained", "", algorithm),
color=algorithm),
data=peakLabels,
hjust=1)+
scale_color_manual(values=algo.colors, guide="none")+
guides(linetype=guide_legend(order=2,
override.aes=list(fill="white")))+
scale_linetype_manual("error type",
values=c(correct="none",
"false negative"="dashed",
"false positive"="solid"))+
ylab("aligned read counts")+
xlab("position on chromosome")+
scale_fill_manual(values=ann.colors)+
geom_line(aes(baseNorm, coverageNorm,
showSelected=test.chunk,
showSelected2=sample.id),
data=dp.peaks.interactive$profiles,
color="grey"),
first=list(testSet="H3K36me3_TDH_immune split 5",
test.chunk="H3K36me3_TDH_immune/3",
sample.id="McGill0101"),
title="Peak detection model comparison")
stopifnot(length(dp.peaks.segment) == 0)
with.facets <- viz$extrapolation+
theme_bw()+
theme(panel.margin=grid::unit(0, "cm"))+
facet_grid(set.name ~ set.i, labeller=function(var, val){
gsub("_", "\n", val)
})
animint2dir(viz, "figure-dp-peaks-interactive")
##animint2gist(viz)