Effects of Correction of Detection Rate
Last updated: 2018-07-23
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: 87fe8d5
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/pipeline/.Rhistory Untracked files: Untracked: ..gif Untracked: .DS_Store Untracked: R/.DS_Store Untracked: R/LSLSL.R Untracked: R/makesparsekernel.R Untracked: R/savesparsekernel.R Untracked: analysis/.DS_Store Untracked: analysis/bibliography.bib Untracked: analysis/pipeline/large_sets.pdf Untracked: analysis/pipeline/temp_ari.txt Untracked: analysis/pipeline/temp_time.txt 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: Modified: analysis/pipeline/large_sets.Rmd Modified: analysis/pipeline/pbmc_pipeline.R 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 Modified: analysis/writeup/paper.pdf Modified: data/.DS_Store Modified: docs/figure/.DS_Store
Expand here to see past versions:
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3c6fbdb | tk382 | 2018-07-22 | update large version to store matrices in hard drive |
| html | 5787a2e | tk382 | 2018-07-17 | Build site. |
| html | 410c051 | tk382 | 2018-07-17 | Build site. |
| html | 36ebfc2 | tk382 | 2018-07-16 | Build site. |
| Rmd | 1d42e31 | tk382 | 2018-07-16 | wflow_publish(“analysis/detection_rate_correction.Rmd”) |
| html | b7e4475 | tk382 | 2018-07-16 | Build site. |
| Rmd | 884f74e | tk382 | 2018-07-16 | wflow_publish(“analysis/detection_rate_correction.Rmd”) |
| Rmd | 66600ac | tk382 | 2018-07-16 | add analysis files |
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))
rm(Pollen)
logX = log(X+1)
det = colSums(X!=0) / nrow(X)
det2 = qr(cbind(rep(1,length(det)), 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$v)), true.label=as.factor(rep(truth,2)))
ggplot(dat, aes(x=pc1, y=detection.rate, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("PCA")
Expand here to see past versions of Pollen-1.png:
| Version | Author | Date |
|---|---|---|
| 5787a2e | tk382 | 2018-07-17 |
| b7e4475 | tk382 | 2018-07-16 |
# tsne1 = Rtsne(t(R))
# tsne2 = Rtsne(t(logX))
# dat = data.frame(v1 = c(tsne1$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")
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, 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] 0.7755788plot(res1$tsne, col=rainbow(numClust)[res1$result])
Expand here to see past versions of Pollen-2.png:
| Version | Author | Date |
|---|---|---|
| 5787a2e | tk382 | 2018-07-17 |
| b7e4475 | tk382 | 2018-07-16 |
plot(res2$tsne, col=rainbow(numClust)[res2$result])
Usoskin
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")
#
# 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$Y)), true.label = as.factor(rep(truth, 2)))
# ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.5574876set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.5215175Buettner
i = 4
#read data
load('../data/unnecessary_in_building/4_Buettner.RData')
X = as.matrix(Buettner$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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Buettner-2.png:
| Version | Author | Date |
|---|---|---|
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.4665139set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.5680807Yan
i = 5
load('../data/unnecessary_in_building/5_Yan.rda')
X = as.matrix(yan)
truth = as.character(ann$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 |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.6517271set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.8954618Treutlein
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 |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| 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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Treutlein-2.png:
| Version | Author | Date |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.326254set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.4426981Chu (cell type)
i = 7
load('../data/unnecessary_in_building/7_Chu_celltype.Rdata')
X = as.matrix(Chu_celltype$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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Chu_celltype-2.png:
| Version | Author | Date |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.6712136set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.9556889Chu (timecourse)
i = 8
load('../data/unnecessary_in_building/8_Chu_timecourse.Rdata')
X = as.matrix(Chu_timecourse$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 |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| 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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Chu_timecourse-2.png:
| Version | Author | Date |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1)
res1 = SLSL(X, log=T, filter=F, correct_detection_rate = T, numClust = numClust)
adj.rand.index(res1$result, truth)[1] 0.6992235set.seed(1)
res2 = SLSL(X, log=T, filter=F, correct_detection_rate = F, numClust = numClust)
adj.rand.index(res2$result, truth)[1] 0.6997664Session 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