Effects of Correction of Detection Rate
Last updated: 2018-07-16
workflowr checks: (Click a bullet for more information)-
✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
-
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
-
✔ Seed:
set.seed(20180618)The command
set.seed(20180618)was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible. -
✔ Session information: recorded
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
-
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.✔ Repository version: 1d42e31
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can usewflow_publishorwflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.Ignored files: Ignored: .Rhistory Ignored: .Rproj.user/ Ignored: R/.Rhistory Ignored: analysis/.Rhistory Ignored: analysis/correcting_detection_rate/.Rhistory Ignored: analysis/pipeline/.Rhistory Untracked files: Untracked: ..gif Untracked: .DS_Store Untracked: R/.DS_Store Untracked: analysis/.DS_Store Untracked: analysis/bibliography.bib Untracked: analysis/clean_data/ Untracked: analysis/downsampling/ Untracked: analysis/dropouts/ Untracked: analysis/filteringtest.R Untracked: analysis/old/ Untracked: analysis/pipeline/large_sets.pdf Untracked: analysis/pipeline/temp_ari.txt Untracked: analysis/pipeline/temp_time.txt Untracked: analysis/sysdata_reference_test/ Untracked: analysis/tutorial_cache/ Untracked: analysis/writeup/cite.bib Untracked: analysis/writeup/cite.log Untracked: analysis/writeup/paper.aux Untracked: analysis/writeup/paper.bbl Untracked: analysis/writeup/paper.blg Untracked: analysis/writeup/paper.log Untracked: analysis/writeup/paper.out Untracked: analysis/writeup/paper.synctex.gz Untracked: analysis/writeup/paper.tex Untracked: analysis/writeup/writeup.aux Untracked: analysis/writeup/writeup.bbl Untracked: analysis/writeup/writeup.blg Untracked: analysis/writeup/writeup.dvi Untracked: analysis/writeup/writeup.log Untracked: analysis/writeup/writeup.out Untracked: analysis/writeup/writeup.synctex.gz Untracked: analysis/writeup/writeup.tex Untracked: analysis/writeup/writeup2.aux Untracked: analysis/writeup/writeup2.bbl Untracked: analysis/writeup/writeup2.blg Untracked: analysis/writeup/writeup2.log Untracked: analysis/writeup/writeup2.out Untracked: analysis/writeup/writeup2.pdf Untracked: analysis/writeup/writeup2.synctex.gz Untracked: analysis/writeup/writeup2.tex Untracked: analysis/writeup/writeup3.aux Untracked: analysis/writeup/writeup3.log Untracked: analysis/writeup/writeup3.out Untracked: analysis/writeup/writeup3.synctex.gz Untracked: analysis/writeup/writeup3.tex Untracked: data/unnecessary_in_building/ Unstaged changes: Deleted: SCNoisyClustering_0.1.0.tar.gz Modified: analysis/pipeline/large_sets.Rmd Modified: analysis/pipeline/small_good_sets.Rmd Modified: analysis/pipeline/small_good_sets_result.txt Modified: analysis/pipeline/small_good_sets_time.txt Modified: analysis/writeup/.DS_Store
Expand here to see past versions:
Pollen
i = 2
load('../data/unnecessary_in_building/2_Pollen.RData')
X = as.matrix(Pollen$x)
truth = as.numeric(as.factor(Pollen$label))
numClust = length(unique(truth))
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(det)
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Pollen-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R))
tsne2 = Rtsne(t(logX))
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Pollen-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.7755788set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.8325414set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.7665684Usoskin
i = 3
load('../data/unnecessary_in_building/3_Usoskin.RData')
X = as.matrix(Usoskin$X)
truth = as.numeric(as.factor(as.character(Usoskin$lab1)))
numClust = 4
rm(Usoskin)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
plot(irlba(logX,1)$v[,1]~log(det))
Expand here to see past versions of Usoskin-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
det2 = qr(cbind(rep(1, length(det)), log(det)))
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Usoskin-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R), perplexity=20)
tsne2 = Rtsne(t(logX))
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Usoskin-3.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.6188269set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.8746858set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.6348444rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2221109 118.7 3972565 212.2 NA 3972565 212.2
Vcells 5410323 41.3 166030754 1266.8 16384 207538345 1583.4Buettner
i = 4
#read data
load('../data/unnecessary_in_building/4_Buettner.RData')
X = as.matrix(Buettner$X)
truth = as.numeric(as.factor(Buettner$label))
numClust = 3
rm(Buettner)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(det)
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Buettner-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R), perplexity=20)
tsne2 = Rtsne(t(logX), perplexity=20)
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Buettner-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.4329975set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.428236set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.4145447rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2214780 118.3 3972565 212.2 NA 3972565 212.2
Vcells 5441158 41.6 132917256 1014.1 16384 207538345 1583.4Yan
i = 5
load('../data/unnecessary_in_building/5_Yan.rda')
X = as.matrix(yan)
truth = as.character(ann$cell_type1)
truth = as.numeric(as.factor(truth))
numClust = 6
rm(ann, yan)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(det)
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Yan-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R), perplexity=20)
tsne2 = Rtsne(t(logX), perplexity=20)
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Yan-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.8954618set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.8954618set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.675345rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2247297 120.1 3972565 212.2 NA 3972565 212.2
Vcells 5380249 41.1 106333804 811.3 16384 207538345 1583.4Treutlein
i = 6
load('../data/unnecessary_in_building/6_Treutlein.rda')
X = as.matrix(treutlein)
truth = as.numeric(colnames(treutlein))
ind = sort(truth, index.return=TRUE)$ix
X = X[,ind]
truth = truth[ind]
numClust = length(unique(truth))
rm(treutlein)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(cbind(log(det), rep(1, length(det))))
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Treutlein-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R), perplexity=10)
tsne2 = Rtsne(t(logX), perplexity=10)
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Treutlein-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.3672819set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.4136064set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.3488583rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2236513 119.5 3972565 212.2 NA 3972565 212.2
Vcells 5372257 41.0 85067043 649.1 16384 207538345 1583.4Chu (cell type)
i = 7
load('../data/unnecessary_in_building/7_Chu_celltype.Rdata')
X = as.matrix(Chu_celltype$X)
truth = as.numeric(as.factor(Chu_celltype$label))
numClust = 7
rm(Chu_celltype)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(det)
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Chu_celltype-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R))
tsne2 = Rtsne(t(logX))
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Chu_celltype-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.9900038set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.9956408set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.7612027rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2259138 120.7 3972565 212.2 NA 3972565 212.2
Vcells 5473999 41.8 207597943 1583.9 16384 259497187 1979.9Chu (timecourse)
i = 8
load('../data/unnecessary_in_building/8_Chu_timecourse.Rdata')
X = as.matrix(Chu_timecourse$X)
truth = as.numeric(as.factor(Chu_timecourse$label))
numClust = length(unique(truth))
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(det)
R = t(qr.resid(det2, t(logX)))
pca1 = irlba(R,2); pca2 = irlba(logX,2)
dat = data.frame(pc1=c(pca1$v[,1], pca2$v[,1]), detection.rate=rep(det, 2), label=rep(c("After correction", "Before correction"), each=nrow(pca1$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Chu_timecourse-1.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
tsne1 = Rtsne(t(R))
tsne2 = Rtsne(t(logX))
dat = data.frame(v1 = c(tsne1$Y[,1], tsne2$Y[,1]), v2 = c(tsne1$Y[,2], tsne2$Y[,2]), label=rep(c("After correction", "Before correction"), each=nrow(tsne1$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Chu_timecourse-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.7321747set.seed(1); res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.7276994set.seed(1); res3 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res3$result, truth)[1] 0.7145637rm(R,X,logX,res1,res2,res3); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 2260642 120.8 3972565 212.2 NA 3972565 212.2
Vcells 20014295 152.7 199358024 1521.0 16384 259497187 1979.9Session information
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] stargazer_5.2.2 abind_1.4-5
[3] broom_0.4.5 diceR_0.5.1
[5] Rtsne_0.13 fossil_0.3.7
[7] shapefiles_0.7 foreign_0.8-70
[9] maps_3.3.0 sp_1.2-7
[11] reshape_0.8.7 dplyr_0.7.6
[13] ggplot2_3.0.0 irlba_2.3.2
[15] Matrix_1.2-14 quadprog_1.5-5
[17] inline_0.3.15 matrixStats_0.53.1
[19] SCNoisyClustering_0.1.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 reshape2_1.4.3 purrr_0.2.4
[4] lattice_0.20-35 colorspace_1.3-2 htmltools_0.3.6
[7] yaml_2.1.19 rlang_0.2.0 R.oo_1.22.0
[10] pillar_1.2.2 glue_1.2.0 withr_2.1.2
[13] R.utils_2.6.0 bindrcpp_0.2.2 plyr_1.8.4
[16] bindr_0.1.1 stringr_1.3.0 munsell_0.4.3
[19] gtable_0.2.0 workflowr_1.1.1 R.methodsS3_1.7.1
[22] codetools_0.2-15 psych_1.8.3.3 evaluate_0.10.1
[25] labeling_0.3 knitr_1.20 parallel_3.5.1
[28] Rcpp_0.12.16 scales_0.5.0 backports_1.1.2
[31] mnormt_1.5-5 digest_0.6.15 stringi_1.1.7
[34] grid_3.5.1 rprojroot_1.3-2 tools_3.5.1
[37] magrittr_1.5 lazyeval_0.2.1 tibble_1.4.2
[40] tidyr_0.8.0 whisker_0.3-2 pkgconfig_2.0.1
[43] assertthat_0.2.0 rmarkdown_1.9 R6_2.2.2
[46] mclust_5.4 nlme_3.1-137 git2r_0.21.0
[49] compiler_3.5.1 This reproducible R Markdown analysis was created with workflowr 1.1.1