Last updated: 2018-07-14
Code version: eed7f05
results_filt_eval_top10 <- readRDS("../data/results/results_filt_eval_top10.rds")
results_filt_eval_top101 <- readRDS("../data/results/results_filt_eval_top101.rds")
source("../peco/R/utility.R")
source("../peco/R/fit.trendfilter.generic.R")
source("../peco/R/run_seurat.R")
mean(results_filt_eval_top10$fit.supervised$diff_time)/2/pi
mean(results_filt_eval_top10$fit.trend2.unsup$diff_time)/2/pi
mean(results_filt_eval_top10$fit.seurat$diff_time)/2/pi
pve.wrapper <- function(results_list, methods_list) {
res <- lapply(1:length(results_list),
function(i) {
obj <- results_list[[i]]
out <- data.frame(
dapi=with(obj, get.pve(dapi[order(pred_time_shift)])$pve),
gfp=with(obj, get.pve(gfp[order(pred_time_shift)])$pve),
rfp=with(obj, get.pve(rfp[order(pred_time_shift)])$pve) )
})
names(res) <- methods_list
return(res)
}
results_list <- results_filt_eval_top10
methods_list <- sapply(names(results_list), function(x) strsplit(x, split=".", fixed=TRUE)[[1]][2])
pve_eval_top10 <- do.call(rbind, pve.wrapper(results_list=results_filt_eval_top10,
methods_list=methods_list))
pve_eval_top10$genes_used <- "top10"
pve_eval_top10$methods <- methods_list
saveRDS(pve_eval_top10,
"../output/method-eval-withheld-explore-removenoisy.Rmd/pve_eval_top10.rds")
pve_eval_top10 <- readRDS("../output/method-eval-withheld-explore-removenoisy.Rmd/pve_eval_top10.rds")
print(pve_eval_top10)
dapi gfp rfp genes_used methods
supervised 0.07345013 0.0489577790 0.3138331 top10 supervised
trend2 0.14252546 0.0006007315 0.2895497 top10 trend2
bspline 0.06255537 0.0062581397 0.3511136 top10 bspline
loess 0.12301345 0.0166849294 0.2876617 top10 loess
seurat 0.09360356 0.0006058249 0.3565288 top10 seurat
get.aov(yy=results_filt_eval_top10$fit.seurat$dapi,
xx=results_filt_eval_top10$fit.seurat$assignments)
get.aov(yy=results_filt_eval_top10$fit.seurat$gfp,
xx=results_filt_eval_top10$fit.seurat$assignments)
get.aov(yy=results_filt_eval_top10$fit.seurat$rfp,
xx=results_filt_eval_top10$fit.seurat$assignments)
seurat.S.sup <- with(results_filt_eval_top10,
get.pve(fit.seurat$S[order(fit.supervised$pred_time_shift)]))
seurat.S.unsup <- with(results_filt_eval_top10,
get.pve(fit.seurat$S[order(fit.trend2.unsup$pred_time_shift)]))
seurat.G2M.sup <- with(results_filt_eval_top10,
get.pve(fit.seurat$G2M[order(fit.supervised$pred_time_shift)]))
seurat.G2M.unsup <- with(results_filt_eval_top10,
get.pve(fit.seurat$G2M[order(fit.trend2.unsup$pred_time_shift)]))
seurat.S.owntime <- with(results_filt_eval_top10,
get.pve(fit.seurat$S[order(fit.seurat$cell_times_est)]))
seurat.G2M.owntime <- with(results_filt_eval_top10,
get.pve(fit.seurat$G2M[order(fit.seurat$cell_times_est)]))
save(seurat.S.sup, seurat.S.unsup,
seurat.G2M.sup, seurat.G2M.unsup,
seurat.S.owntime, seurat.G2M.owntime,
file = "../output/method-eval-withheld-explore-removenoisy.Rmd/seurat.time.top10.rda")
load(file="../output/method-eval-withheld-explore-removenoisy.Rmd/seurat.time.top10.rda")
c(seurat.S.sup$pve, seurat.G2M.sup$pve)
[1] 0.4109253 0.5839934
c(seurat.S.unsup$pve, seurat.G2M.unsup$pve)
[1] 0.3196591 0.6121693
c(seurat.S.owntime$pve, seurat.G2M.owntime$pve)
[1] 0.8383764 0.8031149
# with(results_filt_eval_top10,
# get.aov(yy=fit.seurat$G2M,xx=fit.seurat$assignments))
# with(results_filt_eval_top10,
# get.aov(yy=fit.seurat$S,xx=fit.seurat$assignments))
cols <- c("orange", "red", "brown")
par(mfrow=c(1,3))
with(results_filt_eval_top10,
hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="G1"],
nclass=5, col=cols[1], main = "G1, 9 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
with(results_filt_eval_top10, hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="S"],
nclass=10, col=cols[2],
main = "S, 53 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
with(results_filt_eval_top10, hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="G2M"],
nclass=10, col=cols[3],
main = "G2M, 55 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
title("Supervised cell time and Seurat classes", outer=TRUE, line=-1)
source("../peco/R/utility.R")
source("../peco/R/fit.trendfilter.generic.R")
source("../peco/R/run_seurat.R")
mean(results_filt_eval_top101$fit.supervised$diff_time)/2/pi
mean(results_filt_eval_top101$fit.trend2.unsup$diff_time)/2/pi
mean(results_filt_eval_top101$fit.seurat$diff_time)/2/pi
pve.wrapper <- function(results_list, methods_list) {
res <- lapply(1:length(results_list),
function(i) {
obj <- results_list[[i]]
out <- data.frame(
dapi=with(obj, get.pve(dapi[order(pred_time_shift)])$pve),
gfp=with(obj, get.pve(gfp[order(pred_time_shift)])$pve),
rfp=with(obj, get.pve(rfp[order(pred_time_shift)])$pve) )
})
names(res) <- methods_list
return(res)
}
results_list <- results_filt_eval_top101
methods_list <- sapply(names(results_list), function(x) strsplit(x, split=".", fixed=TRUE)[[1]][2])
pve_eval_top101 <- do.call(rbind, pve.wrapper(results_list=results_filt_eval_top101,
methods_list=methods_list))
pve_eval_top101$genes_used <- "top101"
pve_eval_top101$methods <- methods_list
saveRDS(pve_eval_top101,
"../output/method-eval-withheld-explore-removenoisy.Rmd/pve_eval_top101.rds")
pve_eval_top101 <- readRDS("../output/method-eval-withheld-explore-removenoisy.Rmd/pve_eval_top101.rds")
print(pve_eval_top101)
dapi gfp rfp genes_used methods
supervised 0.012566735 0.0538205897 0.4034993 top101 supervised
trend2 -0.003921578 0.0045257798 0.3864573 top101 trend2
bspline 0.070241404 0.0015846912 0.4212742 top101 bspline
loess 0.025346711 0.0091878022 0.1472481 top101 loess
seurat 0.093603559 0.0006058249 0.3565288 top101 seurat
get.aov(yy=results_filt_eval_top101$fit.seurat$dapi,
xx=results_filt_eval_top101$fit.seurat$assignments)
get.aov(yy=results_filt_eval_top101$fit.seurat$gfp,
xx=results_filt_eval_top101$fit.seurat$assignments)
get.aov(yy=results_filt_eval_top101$fit.seurat$rfp,
xx=results_filt_eval_top101$fit.seurat$assignments)
seurat.S.sup <- with(results_filt_eval_top101,
get.pve(fit.seurat$S[order(fit.supervised$pred_time_shift)]))
seurat.S.unsup <- with(results_filt_eval_top101,
get.pve(fit.seurat$S[order(fit.trend2.unsup$pred_time_shift)]))
seurat.G2M.sup <- with(results_filt_eval_top101,
get.pve(fit.seurat$G2M[order(fit.supervised$pred_time_shift)]))
seurat.G2M.unsup <- with(results_filt_eval_top101,
get.pve(fit.seurat$G2M[order(fit.trend2.unsup$pred_time_shift)]))
seurat.S.owntime <- with(results_filt_eval_top101,
get.pve(fit.seurat$S[order(fit.seurat$cell_times_est)]))
seurat.G2M.owntime <- with(results_eval_top101,
get.pve(fit.seurat$G2M[order(fit.seurat$cell_times_est)]))
save(seurat.S.sup, seurat.S.unsup,
seurat.G2M.sup, seurat.G2M.unsup,
seurat.S.owntime, seurat.G2M.owntime,
file = "../output/method-eval-withheld-explore-removenoisy.Rmd/seurat.time.top101.rda")
load(file="../output/method-eval-withheld-explore.Rmd/seurat.time.top101.rda")
c(seurat.S.sup$pve, seurat.G2M.sup$pve)
[1] 0.4832478 0.6548333
c(seurat.S.unsup$pve, seurat.G2M.unsup$pve)
[1] 0.5051351 0.5050090
c(seurat.S.owntime$pve, seurat.G2M.owntime$pve)
[1] 0.8272036 0.8565118
# with(results_filt_eval_top101,
# get.aov(yy=fit.seurat$G2M,xx=fit.seurat$assignments))
# with(results_filt_eval_top101,
# get.aov(yy=fit.seurat$S,xx=fit.seurat$assignments))
cols <- c("orange", "red", "brown")
par(mfrow=c(1,3))
with(results_eval_top101,
hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="G1"],
nclass=5, col=cols[1], main = "G1, 9 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
with(results_eval_top101, hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="S"],
nclass=10, col=cols[2],
main = "S, 53 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
with(results_eval_top101, hist(fit.supervised$pred_time_shift[fit.seurat$assignments=="G2M"],
nclass=10, col=cols[3],
main = "G2M, 55 cells", xlim=c(0,2*pi), ylim=c(0,30),
xlab="Predicted cell time"))
title("Supervised cell time and Seurat classes", outer=TRUE, line=-1)
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.3 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
[5] tools_3.4.3 htmltools_0.3.6 yaml_2.1.16 Rcpp_0.12.17
[9] stringi_1.1.6 rmarkdown_1.10 knitr_1.20 git2r_0.21.0
[13] stringr_1.2.0 digest_0.6.15 evaluate_0.10.1
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