Effects of Correction of Detection Rate
Last updated: 2018-07-17
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| 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))
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 2203793 117.7 3972484 212.2 NA 3972484 212.2
Vcells 5419519 41.4 138305840 1055.2 16384 172882188 1319.0Yan
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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Yan-2.png:
| Version | Author | Date |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$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 2236314 119.5 3972484 212.2 NA 3972484 212.2
Vcells 5358557 40.9 110644672 844.2 16384 172882188 1319.0Treutlein
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$Y)), true.label = as.factor(rep(truth, 2)))
ggplot(dat, aes(x=v1, y=v2, col=true.label)) + facet_grid(~label) + geom_point() + ggtitle("tSNE")
Expand here to see past versions of Treutlein-2.png:
| Version | Author | Date |
|---|---|---|
| 36ebfc2 | tk382 | 2018-07-16 |
| b7e4475 | tk382 | 2018-07-16 |
set.seed(1); res1 = SLSL(R, log=F, filter=F, numClust = numClust)
adj.rand.index(res1$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 2225549 118.9 3972484 212.2 NA 3972484 212.2
Vcells 5350667 40.9 88515737 675.4 16384 172882188 1319.0Chu (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] psych_1.8.3.3 evaluate_0.10.1 labeling_0.3
[25] knitr_1.20 parallel_3.5.1 Rcpp_0.12.16
[28] scales_0.5.0 backports_1.1.2 mnormt_1.5-5
[31] digest_0.6.15 stringi_1.1.7 grid_3.5.1
[34] rprojroot_1.3-2 tools_3.5.1 magrittr_1.5
[37] lazyeval_0.2.1 tibble_1.4.2 tidyr_0.8.0
[40] whisker_0.3-2 pkgconfig_2.0.1 assertthat_0.2.0
[43] rmarkdown_1.9 R6_2.2.2 mclust_5.4
[46] nlme_3.1-137 git2r_0.21.0 compiler_3.5.1 This reproducible R Markdown analysis was created with workflowr 1.1.1