From 7bca4a77af39557d1b4d5bb48baa0da95590811b Mon Sep 17 00:00:00 2001 From: "katharina.schmid" Date: Fri, 25 Jun 2021 15:39:00 +0200 Subject: [PATCH] Added explanation for expression cutoff selection to the vignette --- vignettes/introduction-scPower.Rmd | 38 +++++++++++++++++++++++++++++ vignettes/introduction-scPower.pdf | Bin 316858 -> 319729 bytes 2 files changed, 38 insertions(+) diff --git a/vignettes/introduction-scPower.Rmd b/vignettes/introduction-scPower.Rmd index d7c3764..4b6cead 100644 --- a/vignettes/introduction-scPower.Rmd +++ b/vignettes/introduction-scPower.Rmd @@ -357,6 +357,44 @@ print(opt.design[which.max(opt.design$powerDetect),]) Similar to the power calculation, variants of the function exist to model a restricted number of cells per lane (optimize.constant.budget.restrictedDoublets) and to model Smart-seq data (optimize.constant.budget.smartseq). Furthermore, there is the option to calculate the library preparation cost per cell instead of per kit (optimize.constant.budget.libPrepCell). +### Selecting expression thresholds + +Both the individual level expression threshold "min.UMI.counts"/"min.counts" and +the population level expression threshold "perc.indiv.expr" can be fexibly chosen +by the user. As a rule of thumb, lower more lenient thresholds increase the number of expressed +genes and so in many cases also the power, while higher more conservative thresholds +select more likely only accuratly quantified genes and reduce so the number of false positives. + +If the users are interested in optimizing the threshold to reach the maximal +possible power in their setting, scPower provides a function for that called +"select.cutoffs". As a higher number of expressed genes is +increasing also the multiple testing burden, thresholds +of zero are here necessarily always the optimal choice. The range of thresholds that should be +considered can be specified both for the individual level threshold (umi_range) +and the population level threshold (pop_range). The optimal thresholds in the example below +are an UMI threshold of 3 and population threshold of 0. + +```{r} +optimal_cutoffs<-select.cutoffs(umi_range=c(0,3,5), + pop_range=c(0,0.2,0.5), + nSamples=100,nCells=1500,readDepth=25000, + ct.freq=0.2,type="eqtl", + ref.study=scPower::eqtl.ref.study, + ref.study.name="Blueprint (Monocytes)", + cellsPerLane=20000, + read.umi.fit = scPower::read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits = scPower::gamma.mixed.fits, + ct="CD14+ Monocytes", + disp.fun.param=scPower::disp.fun.param, + mappingEfficiency = 0.8, + sign.threshold = 0.05, + MTmethod="Bonferroni") + +optimal_cutoffs + +``` + ## Part 2: Generation of new priors from a data set Additionally to the priors given by the package, scPower offers the option to generate new priors from your own data set. 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