diff --git a/r.scripts/sc_PC.Score.NE.R b/r.scripts/sc_PC.Score.NE.R index fa09ed98dfd031e2b7ba301ad18ffce371fecada..9acccfa62f63f32c0e4a303616c1c685fe5a37de 100755 --- a/r.scripts/sc_PC.Score.NE.R +++ b/r.scripts/sc_PC.Score.NE.R @@ -96,7 +96,7 @@ ts_1 <- ts(d1$y) #ts_2 <- ts(d2$y) tp1 <- turnpoints(ts_1) #tp2 <- turnpoints(ts_2) -pit1 <- min(d1$x[tp1$pits][d1$y[tp1$pits]==min(d1$y[tp1$pits])]) +pit1 <- min(d1$x[tp1$pits][d1$x[tp1$pits]>d1$x[tp1$peaks][d1$y[tp1$peaks]==max(d1$y[tp1$peaks])]]) #pit2 <- d2$x[tp2$pits][length(d2$x[tp2$pits])] #abline(v=pit1) diff --git a/r.scripts/sc_PC.Score.Stress.R b/r.scripts/sc_PC.Score.Stress.R index 7ee2ac5ba41af2e9386287e8b388f2bb737f5bad..176d89b583061195862719425c8acc6492f748fc 100755 --- a/r.scripts/sc_PC.Score.Stress.R +++ b/r.scripts/sc_PC.Score.Stress.R @@ -75,13 +75,14 @@ plot(plot) dev.off() #find first turning point on a KDE of Stress1 +histo <- hist(GetCellEmbeddings(object=sc10x.Group,reduction.type="Stress",dims.use=1),breaks=10,prob=TRUE,plot=TRUE) d1 <- density(GetCellEmbeddings(object=sc10x.Group,reduction.type="Stress",dims.use=1),n=1000) #d2 <- density(GetCellEmbeddings(object=sc10x.Group,reduction.type="Stress",dims.use=2),n=1000) ts_1 <- ts(d1$y) #ts_2 <- ts(d2$y) tp1 <- turnpoints(ts_1) #tp2 <- turnpoints(ts_2) -pit1 <- min(d1$x[tp1$pits]) +pit1 <- min(d1$x[tp1$pits][d1$x[tp1$pits]>d1$x[tp1$peaks][d1$y[tp1$peaks]==max(d1$y[tp1$peaks])]]) #pit2 <- min(d2$x[tp2$pits]) #plot clusters @@ -98,6 +99,8 @@ plot <- plot+geom_vline(xintercept=pit1,color="red",lwd=2.5) plot(plot) dev.off() + + #subsample all cells (+Stress) to better visualize their clustering if (!is.na(opt$s)){ rnd <- sample(1:ncol(sc10x.Group@data),opt$sv)