From e456e5104232f7440014baeb3ee7d465901c5ee0 Mon Sep 17 00:00:00 2001 From: "katharina.schmid" Date: Fri, 24 Sep 2021 15:55:55 +0200 Subject: [PATCH] Updated reproduce-paper-plots vignette --- DESCRIPTION | 7 +- NEWS | 5 + R/datasets.R | 16 + README.md | 2 +- data/simulatedDEValues.RData | Bin 1485 -> 4041 bytes data/simulatedEQTLValues.RData | Bin 0 -> 587 bytes man/simulated.eQTL.values.Rd | 20 + vignettes/reproduce-paper-plots.Rmd | 2628 +++++++++++++++------------ vignettes/reproduce-paper-plots.pdf | Bin 650302 -> 693361 bytes 9 files changed, 1511 insertions(+), 1167 deletions(-) create mode 100644 data/simulatedEQTLValues.RData create mode 100644 man/simulated.eQTL.values.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 0287a0b..9db7361 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: scPower Title: Experimental design framework for scRNAseq population studies (eQTL and DE) -Version: 0.1.3 +Version: 1.0.0 Authors@R: c( person('Katharina', 'Schmid', NULL, 'katharina.schmid@helmholtz-muenchen.de', 'aut'), person('Barbara', 'Hoellbacher', NULL, 'barbara.hoellbacher@helmholtz-muenchen.de', 'aut'), @@ -30,5 +30,8 @@ Suggests: rmarkdown, ggpubr, data.table, - viridis + viridis, + RColorBrewer, + gridExtra, + dplyr VignetteBuilder: knitr diff --git a/NEWS b/NEWS index 7968ace..71cd879 100644 --- a/NEWS +++ b/NEWS @@ -1,4 +1,9 @@ +CHANGES IN VERSION 1.0.0 +------------------------- +o Updated vignette reproduce-paper-plots to match with the updated plots of the publication. + + CHANGES IN VERSION 0.1.3 ------------------------- o Added the option to calculate the power for unbalanced DE sets (using the variable diff --git a/R/datasets.R b/R/datasets.R index 142581c..3c9507d 100644 --- a/R/datasets.R +++ b/R/datasets.R @@ -271,3 +271,19 @@ #' data(simulatedDEValues) #' head(simulated.DE.values) "simulated.DE.values" + +#' Results of eQTL power generated by self-implemented simulations +#' (see vignette reproduce-paper-plots) +#' +#' @docType data +#' +#' @usage data(simulatedEQTLValues) +#' +#' @format Data frame with simulated eQTL power +#' +#' @keywords datasets +#' +#' @examples +#' data(simulatedEQTLValues) +#' head(simulated.eQTL.values) +"simulated.eQTL.values" diff --git a/README.md b/README.md index 60f57b1..2ee7579 100644 --- a/README.md +++ b/README.md @@ -63,5 +63,5 @@ Be aware that additional libraries are required for building the vignettes: ```R #CRAN packages -install.packages(c("knitr","rmarkdown","ggplot2","ggpubr","data.table", "viridis")) +install.packages(c("knitr","rmarkdown","ggpubr","data.table", "viridis", "RColorBrewer","gridExtra","dplyr")) ``` diff --git a/data/simulatedDEValues.RData b/data/simulatedDEValues.RData index 1d9969b2ebd371389af9dabfbc60cd5978625ce2..e3fd8f4c75ebf8f8d371b66c3be78c0028f1404e 100644 GIT binary patch literal 4041 zcmV;)4>s^0iwFP!000001MOM|R8-g69l(kOBT5Vgv3C$=V1QV*AfgC}QWODcGK$ih z1|z6QFTzlyNEd=)R16j%F&0D=krEXYLD~#6Ll>+>$r}jVYhdwS^6Pr*z4vq1I_y2) 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reproduce-paper-plots)} +\format{Data frame with simulated eQTL power} +\usage{ +data(simulatedEQTLValues) +} +\description{ +Results of eQTL power generated by self-implemented simulations +(see vignette reproduce-paper-plots) +} +\examples{ +data(simulatedEQTLValues) +head(simulated.eQTL.values) +} +\keyword{datasets} diff --git a/vignettes/reproduce-paper-plots.Rmd b/vignettes/reproduce-paper-plots.Rmd index 675f42f..ca94995 100644 --- a/vignettes/reproduce-paper-plots.Rmd +++ b/vignettes/reproduce-paper-plots.Rmd @@ -31,18 +31,31 @@ library(data.table) library(viridis) library(ggpubr) library(RColorBrewer) +library(gridExtra) +library(dplyr) ``` ```{r} #Color specifications col.paired <- brewer.pal(n = 12, "Paired") col.set <- col.paired[c(2,8,9)] -col.set2 <- col.paired[c(9,5,7,3,8,2)] +col.set2<- RColorBrewer::brewer.pal(n = 5, name = "Set1")[-1] col.set3 <- brewer.pal(n = 11, "PRGn")[c(4,6,8)] col.set3 <- c(col.set[3], "#F7F7F7", col.set[1]) col.set4 <- c(col.set[1], col.set[3]) col.set5<-col.paired[seq(2,12,2)] +col.dark2<-brewer.pal(n=7,"Dark2") +col.power<-c("#FF9900", "#9900CC","#FF5050") +#Large set with 10 colors for optimization plots +gg_color_hue <- function(n) { + hues = seq(15, 375, length = n + 1) + hcl(h = hues, l = 65, c = 100)[1:n] +} +col.set.new<- gg_color_hue(10) +#Order them so that similar colors are not close +col.set.new<-col.set.new[c(1,6,3,8,4,9,2,7,5,10)] +method.colors<-col.paired[c(1:4,7:9,6)] ``` # Paper Plot @@ -53,243 +66,245 @@ Figure 1 is a schematic overview figure showing the dependence of experimental d ## Figure 2: Expression probability model parameterized by UMI counts per cell -Figure 2 shows how the fit of our expression probabilty model is applied on two example data sets, our own data set and one of Kang et al, 2018. The model is fitted using our own PBMC data set. The fits are provided in the scPower package in the data frames "disp.fun.param" and "gamma.mixed.fits". Also the used UMI-read fit was generated using our own PBMC data set, saved in the data frame "read.umi.fit" as "10X_PBMC_1". The fitting itself is not shown here due to runtime limitations, but the fits were exactly produced as described in the introduction-scPower" vignette in section "Expression probability curves" (using the same functions in the same order). The only difference is that in the introduction vignette it is only shown for one cell type, we performed the fit separately for all celltypes with at least 50 cells. +Figure 2a is a schematic overview figure visualizing the expression model + +Figure 2b-c shows how the fit of our expression probabilty model is applied on two example data sets, our own data set and one of Kang et al, 2018. The model is fitted using our own PBMC data set. The fits are provided in the scPower package in the data frames "disp.fun.param" and "gamma.mixed.fits". Also the used UMI-read fit was generated using our own PBMC data set, saved in the data frame "read.umi.fit" as "10X_PBMC_1". The fitting itself is not shown here due to runtime limitations, but the fits were exactly produced as described in the introduction-scPower" vignette in section "Expression probability curves" (using the same functions in the same order). The only difference is that in the introduction vignette it is only shown for one cell type, we performed the fit separately for all celltypes with at least 50 cells. The observed expression curves were calculated as described in section "Counting observed expressed genes", the percalculated values are saved due to time reasons in the data frame "observed.gene.counts". -```{r, fig.height=8, fig.width=8} +```{r, fig.height=4, fig.width=11} #Load observed gene counts (precalculated) data(precalculatedObservedGeneCounts) #observed.gene.counts #Selected run run<-"Run 5" +estimates.allSamples<-NULL + ############### -#Fit for own data and count > 10 -estimates.allSamples<-observed.gene.counts[ +# Fit for own data and count > 10 +estimates<-observed.gene.counts[ observed.gene.counts$evaluation=="own_count10" & observed.gene.counts$run==run,] -estimates.allSamples$estimated.counts<-NA -for(i in 1:nrow(estimates.allSamples)){ +estimates$estimated.counts<-NA +for(i in 1:nrow(estimates)){ exp.probs<-scPower::estimate.exp.prob.count.param( nSamples=14, - nCellsCt=estimates.allSamples$num.cells[i]/14, - meanCellCounts=estimates.allSamples$meanUMI[i], + nCellsCt=estimates$num.cells[i]/14, + meanCellCounts=estimates$meanUMI[i], gamma.mixed.fits = scPower::gamma.mixed.fits, - ct=estimates.allSamples$cell.type[i], + ct=estimates$cell.type[i], disp.fun.param = scPower::disp.fun.param, min.counts=10, perc.indiv=0.5) - estimates.allSamples$estimated.counts[i]<-round(sum(exp.probs)) + estimates$estimated.counts[i]<-round(sum(exp.probs)) } - -plot.estimates<-reshape2::melt(estimates.allSamples, - id.vars=c("run","sample","cell.type","num.cells", - "meanUMI","evaluation")) -#Set the ordering of expressed vs estimated correctly -plot.estimates$variable<-factor(plot.estimates$variable, - levels=c("expressed.genes","estimated.counts")) - -#Replace subsampling -subsampled.names<-setNames(c("50000","37500","25000","12500"), - c("complete","subsampled75","subsampled50", - "subsampled25")) -plot.estimates$sample<-as.character(plot.estimates$sample) -plot.estimates$sample<-subsampled.names[plot.estimates$sample] - -g.run.10<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=sample,linetype=variable))+ - geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ - xlab("Number of cells per cell type")+ - ylab("Expressed genes")+ - scale_shape_manual("Cell type",values=c(21,22,23,24,25,8))+ - scale_fill_manual(values=col.set2)+ - scale_color_manual("Read depth",values=col.set2)+ - theme_bw() + - theme(axis.title=element_text(size=12), - axis.text=element_text(size=8), - legend.position="none", - aspect.ratio = 0.8, - legend.direction = "vertical", legend.box = "horizontal") - +estimates$dataset<-"Training data set" +estimates$threshold<-"Training data set - Count > 10" +estimates.allSamples<-rbind(estimates.allSamples,estimates) ############### -#Fit for own data and count > 0 -estimates.allSamples<-observed.gene.counts[ +# Fit for own data and count > 0 +estimates<-observed.gene.counts[ observed.gene.counts$evaluation=="own_count0" & observed.gene.counts$run==run,] -estimates.allSamples$estimated.counts<-NA -for(i in 1:nrow(estimates.allSamples)){ +estimates$estimated.counts<-NA +for(i in 1:nrow(estimates)){ exp.probs<-scPower::estimate.exp.prob.count.param( nSamples=14, - nCellsCt=estimates.allSamples$num.cells[i]/14, - meanCellCounts=estimates.allSamples$meanUMI[i], + nCellsCt=estimates$num.cells[i]/14, + meanCellCounts=estimates$meanUMI[i], gamma.mixed.fits = scPower::gamma.mixed.fits, - ct=estimates.allSamples$cell.type[i], + ct=estimates$cell.type[i], disp.fun.param = scPower::disp.fun.param, min.counts=0, perc.indiv=0.5) - estimates.allSamples$estimated.counts[i]<-round(sum(exp.probs)) + estimates$estimated.counts[i]<-round(sum(exp.probs)) } +estimates$dataset<-"Training data set" +estimates$threshold<-"Training data set - Count > 0" +estimates.allSamples<-rbind(estimates.allSamples,estimates) -plot.estimates<-reshape2::melt(estimates.allSamples, - id.vars=c("run","sample","cell.type","num.cells", - "meanUMI","evaluation")) - -#Set the ordering of expressed vs estimated correctly -plot.estimates$variable<-factor(plot.estimates$variable, - levels=c("expressed.genes","estimated.counts")) - -#Replace subsampling +#Set batch variable +estimates.allSamples$run<-NULL subsampled.names<-setNames(c("50000","37500","25000","12500"), - c("complete","subsampled75","subsampled50", - "subsampled25")) -plot.estimates$sample<-as.character(plot.estimates$sample) -plot.estimates$sample<-subsampled.names[plot.estimates$sample] - -g.run.0<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=sample,linetype=variable))+ - geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ - xlab("Number of cells per cell type")+ - ylab("Expressed genes")+ - scale_shape_manual("Cell type",values=c(21,22,23,24,25,8))+ - scale_fill_manual(values=col.set2)+ - scale_color_manual("Read depth",values=col.set2)+ - theme_bw() + - theme(axis.title=element_text(size=12), - axis.text=element_text(size=8), - legend.position="none", - aspect.ratio = 0.8, - legend.direction = "vertical", legend.box = "horizontal") + c("complete","subsampled75", + "subsampled50","subsampled25")) +estimates.allSamples$sample<-subsampled.names[estimates.allSamples$sample] +estimates.allSamples$sample<-paste0(estimates.allSamples$sample," - PBMC1") ############### -#Fit for Kang data and count > 10 -estimates.allSamples<-observed.gene.counts[ +# Fit for Kang data and count > 10 +estimates<-observed.gene.counts[ observed.gene.counts$evaluation=="Kang_count10",] personsPerRunKang<-list("A"=4,"B"=4,"C"=8) -estimates.allSamples$estimated.counts<-NA -for(i in 1:nrow(estimates.allSamples)){ - sampleSize<-personsPerRunKang[[estimates.allSamples$run[i]]] +estimates$estimated.counts<-NA +for(i in 1:nrow(estimates)){ + sampleSize<-personsPerRunKang[[estimates$run[i]]] exp.probs<-scPower::estimate.exp.prob.count.param( nSamples=sampleSize, - nCellsCt=estimates.allSamples$num.cells[i]/sampleSize, - meanCellCounts=estimates.allSamples$meanUMI[i], + nCellsCt=estimates$num.cells[i]/sampleSize, + meanCellCounts=estimates$meanUMI[i], gamma.mixed.fits = scPower::gamma.mixed.fits, - ct=estimates.allSamples$cell.type[i], + ct=estimates$cell.type[i], disp.fun.param = scPower::disp.fun.param, min.counts=10, perc.indiv=0.5) - estimates.allSamples$estimated.counts[i]<-round(sum(exp.probs)) + estimates$estimated.counts[i]<-round(sum(exp.probs)) } -plot.estimates<-reshape2::melt(estimates.allSamples, - id.vars=c("run","sample","cell.type","num.cells", - "meanUMI","evaluation")) - -#Remove dendritic cell type (only fitted with data from the overloaded run) -plot.estimates<-plot.estimates[plot.estimates$cell.type!="Dendritic cells",] - -g.ye.10<-ggplot(plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=run,linetype=variable))+ - geom_point(aes(shape=cell.type,color=run,fill=run),size=2)+ - xlab("Number of cells per cell type")+ - ylab("Expressed genes")+ - scale_fill_manual(values=col.set2)+ - scale_color_manual("Batch",values=col.set2)+ - scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ - theme_bw() + - theme(axis.title=element_text(size=12), - axis.text.x=element_text(size=8), - legend.position="none", - aspect.ratio = 0.8) +estimates$dataset<-"Test data set" +estimates$threshold<-"Test data set - Count > 10" +estimates$sample<-paste0(estimates$run," - PBMC2") +estimates$run<-NULL +estimates.allSamples<-rbind(estimates.allSamples,estimates) ############### -#Fit for Kang data and count > 10 -estimates.allSamples<-observed.gene.counts[ +# Fit for Kang data and count > 10 +estimates<-observed.gene.counts[ observed.gene.counts$evaluation=="Kang_count0",] personsPerRunKang<-list("A"=4,"B"=4,"C"=8) -estimates.allSamples$estimated.counts<-NA -for(i in 1:nrow(estimates.allSamples)){ - sampleSize<-personsPerRunKang[[estimates.allSamples$run[i]]] +estimates$estimated.counts<-NA +for(i in 1:nrow(estimates)){ + sampleSize<-personsPerRunKang[[estimates$run[i]]] exp.probs<-scPower::estimate.exp.prob.count.param( nSamples=sampleSize, - nCellsCt=estimates.allSamples$num.cells[i]/sampleSize, - meanCellCounts=estimates.allSamples$meanUMI[i], + nCellsCt=estimates$num.cells[i]/sampleSize, + meanCellCounts=estimates$meanUMI[i], gamma.mixed.fits = scPower::gamma.mixed.fits, - ct=estimates.allSamples$cell.type[i], + ct=estimates$cell.type[i], disp.fun.param = scPower::disp.fun.param, min.counts=0, perc.indiv=0.5) - estimates.allSamples$estimated.counts[i]<-round(sum(exp.probs)) + estimates$estimated.counts[i]<-round(sum(exp.probs)) } +estimates$dataset<-"Test data set" +estimates$threshold<-"Test data set - Count > 0" +estimates$sample<-paste0(estimates$run," - PBMC2") +estimates$run<-NULL +estimates.allSamples<-rbind(estimates.allSamples,estimates) + +############### +# Get the correlation values +text_vals<-estimates.allSamples%>% + group_by(threshold)%>% + summarize(corr=cor(expressed.genes,estimated.counts)^2) +text_vals$corr<-paste0("r^2==",round(text_vals$corr,3)) + +############### +# Reformat data for plotting +estimates.allSamples$meanUMI<-NULL +estimates.allSamples$evaluation<-NULL + plot.estimates<-reshape2::melt(estimates.allSamples, - id.vars=c("run","sample","cell.type","num.cells", - "meanUMI","evaluation")) + id.vars=c("sample","cell.type","num.cells", + "threshold","dataset")) -#Remove dendritic cell type (only fitted with data from the overloaded run) -plot.estimates<-plot.estimates[plot.estimates$cell.type!="Dendritic cells",] +named_colvector<-c("12500 - PBMC1"=col.dark2[1],"25000 - PBMC1"=col.dark2[2], + "37500 - PBMC1"=col.dark2[3],"50000 - PBMC1"=col.dark2[4], + "A - PBMC2"=col.dark2[5],"B - PBMC2"=col.dark2[6], + "C - PBMC2"=col.dark2[7]) -g.ye.0<-ggplot(plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=run,linetype=variable))+ - geom_point(aes(shape=cell.type,color=run,fill=run),size=2)+ +plot.estimates_train <- plot.estimates %>% + dplyr::filter(dataset=="Training data set") +plot.estimates_test <- plot.estimates %>% + dplyr::filter(dataset=="Test data set") + +# New plot +gg_train <- ggplot(plot.estimates_train,aes(x=num.cells,y=value))+ + geom_line(aes(color=sample,linetype=variable))+ + geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ xlab("Number of cells per cell type")+ ylab("Expressed genes")+ - scale_fill_manual(values=col.set2)+ - scale_color_manual("Batch",values=col.set2)+ + scale_fill_manual(values=named_colvector)+ + scale_color_manual("Read depth",values=named_colvector)+ + scale_linetype_discrete(name="Curve",labels=c("Expressed","Estimated")) + scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ + facet_wrap(~threshold,ncol=2,scales="free")+ + geom_text(data=text_vals[3:4,],aes(label=corr,x=Inf,y=-Inf, + hjust=1.2, vjust=-0.8), parse = TRUE)+ theme_bw() + - theme(axis.title=element_text(size=12), - axis.text.x=element_text(size=8), - legend.position="none", - aspect.ratio = 0.8) - -#Create a plot only for the legend and save that -g.legend<-ggplot(plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=run,linetype=variable))+ - geom_point(aes(shape=cell.type,color=run,fill=run),size=2)+ + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position = "bottom", + legend.title=element_text(size=8), + legend.text=element_text(size=8), + aspect.ratio = 0.8)+ + guides(color = guide_legend(override.aes = list(size = 0.5), order=1, nrow=4), + linetype=guide_legend(nrow=2,byrow=TRUE), + shape=guide_legend(nrow=3,byrow=TRUE), + fill=FALSE) + +gg_test <- ggplot(plot.estimates_test,aes(x=num.cells,y=value))+ + geom_line(aes(color=sample,linetype=variable))+ + geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ + xlab("Number of cells per cell type")+ + ylab("Expressed genes")+ + scale_fill_manual(values=named_colvector)+ + scale_color_manual("Batch",values=named_colvector)+ + scale_linetype_discrete(name="Curve",labels=c("Expressed","Estimated")) + scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ - theme_bw()+ -theme(legend.position = "bottom", - legend.title=element_text(size=8), - legend.text=element_text(size=8))+ - guides(linetype=FALSE,color=FALSE,fill=FALSE) - -leg.ct<-get_legend(g.legend) -leg.ct<-as_ggplot(leg.ct) + facet_wrap(~threshold,ncol=2,scales="free")+ + geom_text(data=text_vals[1:2,],aes(label=corr,x=Inf,y=-Inf, + hjust=1.2, vjust=-0.8), parse=TRUE)+ + theme_bw() + + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position = "bottom", + legend.title=element_text(size=8), + legend.text=element_text(size=8), + aspect.ratio = 0.8)+ + guides(color = guide_legend(override.aes = list(size = 0.5), order=1, nrow=3), + linetype=FALSE, + shape=FALSE, + fill=FALSE) -g.plots<-ggarrange(g.run.10,g.run.0,g.ye.10,g.ye.0, ncol=2, nrow=2, - labels=c("A","B","C","D")) +jointlegend<-gtable_cbind(get_legend(gg_train),get_legend(gg_test)) -g<-ggarrange(g.plots,leg.ct,nrow=2,heights=c(4,0.3)) +g<-ggarrange(gg_train,gg_test,ncol=2, + labels=c("b","c"), + common.legend = TRUE, legend="bottom", legend.grob = jointlegend) print(g) - ``` -Figure 2: Expression probability model parameterized by UMI counts per cell. Plot A and B show the observed (solid) number of expressed genes and the number of expressed genes expected under our model (dashed) on the y-axis and the number of cells per cell type (cell type indicated by the point symbol) on the x-axis for Run 5 of the PBMC data set (Additional file 1: Table S1). The data is subsampled to different read depths (indicated by the color). Similarly, plot C and D show expressed genes for the three batches of the Ye data set. The definition for an expressed gene is based on a flexible user defined cutoff. Here it was parameterized: in Figure A and C, a gene is called expressed with count > 10 in more than 50% of the individuals, in Figure B and D, count > 0 in more than 50% of the individuals. +Figure 2: Expression probability model parameterized by UMI counts per cell. +a The expression probabilities for genes in pseudobulk of a newly planned experiment are estimated based on the expression prior and the planned experimental parameters. For this, the expression prior is derived from the mean and dispersion parameters of gene-wise negative binomial distributions fitted from a matching pilot data set. -## Figure 3: Expression probability, DE/eQTL power and overall detection power +b Using this approach, the number of expressed genes expected under our model (dashed line) closely matches the observed number of expressed genes (solid line) dependent on the number of cells per cell type (cell type indicated by point symbol) for one batch of the training PBMC data set. The data is subsampled to different read depths (indicated by color). The r2 values between estimated and expressed genes were highly significant for both expression thresholds. -The figure shows the influence of the experimental parameters on the detection power. The same expression probability model as in Figure 2 is used, the priors for the power, i.e. the effect sizes and expression ranks, are taken from different reference studies (see publication for citation and more explanation). The experimental parameters are set to realistic values. FDR adjustment is set as multiple testing correction for DE and FWER adjustment for eQTLs. +c The model performed similarly well for the three batches of an independent validation PBMC data set. + +Used expression threshold: count > 10 (right panels of b,c) or count > 0 (left panels of b,c) in more than 50% of the individuals. + + +## Figure 3: Expression probability, DE/eQTL power and overall detection power and their validation in simulation studies. + +The Figure 3a,b shows the influence of the experimental parameters on the detection power. The same expression probability model as in Figure 2 is used, the priors for the power, i.e. the effect sizes and expression ranks, are taken from different reference studies (see publication for citation and more explanation). The experimental parameters are set to realistic values. FDR adjustment is set as multiple testing correction for DE and FWER adjustment for eQTLs. +In Figure 3c-f, the performance is validated against different simulation-based methods. Due to runtime reasons, all simulations (for powsimR, muscat and our own eQTL study) were already precalculated for the vignette here (for the DE studies for a smaller range shown than in the publication). + +```{r} + +################################################################################ +# Part a,b (DE and eQTL power for different parameter combis) -```{r, fig.width=8,fig.height=6} #General parameters transcriptome.mapped.reads<-20000 nGenes<-21000 @@ -324,10 +339,10 @@ res<-lapply(1:nrow(param.combis),function(i) sign.threshold = 0.05, MTmethod = "FDR")) -power.DE.study<-rbindlist(res) +power.DE<-rbindlist(res) #Plot DE results -power.DE.study<-reshape2::melt(power.DE.study, +power.DE.study<-reshape2::melt(power.DE, id.vars=c("name","sampleSize","totalCells", "usableCells","multipletFraction", "ctCells","readDepth", @@ -381,10 +396,10 @@ res<-lapply(1:nrow(param.combis),function(i) sign.threshold = 0.05, MTmethod = "Bonferroni")) -power.eqtl.study<-rbindlist(res) +power.eqtl<-rbindlist(res) #Plot eQTL results -power.eqtl.study<-reshape2::melt(power.eqtl.study, +power.eqtl.study<-reshape2::melt(power.eqtl, id.vars=c("name","sampleSize","totalCells", "usableCells","multipletFraction", "ctCells","readDepth", @@ -411,361 +426,542 @@ g.eqtl<-ggplot(power.eqtl.study,aes(x=ctCells,y=sampleSize,fill=value))+ legend.title=element_text(size=8), legend.text=element_text(size=8)) -g<-ggarrange(g.DE,g.eqtl,ncol=1,nrow=2,labels=c("A","B"), +g.ab<-ggarrange(g.DE,g.eqtl,ncol=1,nrow=2,labels=c("a","b"), align="hv",common.legend=TRUE,legend="right") -print(g) - -``` - -Figure 3: Expression probability, DE/eQTL power and overall detection power. Power estimation using data driven priors for A. DE genes and B. eQTL genes dependent on the total sample size and the number of measured cells per cell type. The detection power is the product of the expression probability and the power to detect the genes as DE or eQTL genes, respectively. The fold change for DEGs and the R2 for eQTL genes were taken from published studies, together with the expression rank of the genes. For A, the Blueprint CLL study with comparison iCLL vs mCLL was used, for B, the Blueprint T cell study. The expression profile and expression probabilities in a single cell experiment with a specific number of samples and measured cells was estimated using our gamma mixed models, setting the definition for expressed to > 10 counts in more than 50% of the individuals. +################################################################################ +# Part c-e (Comparison with powsimR and edgeR) -## Figure 4: Optimal parameters for varying budgets and 10X data +#Rerun power calculation as different cutoffs and additional filtering is necessary! +cellsPerCelltype<-c(200,1000,3000) +sampleSize<-c(4,8,16) +ref.study.name<-"Blueprint (CLL) iCLL-mCLL" -How the optimal parameters develop for an increasing experimental budget, is shown in Figures 4 and 5. As this means running the optimization function many times, this takes too long to run it in the vignette. Instead, the code is given, but the evaluation set to FALSE. Interested users can run the calculation, by setting it to TRUE. Calculations are done for DE and eQTL studies, with observed and simulated priors. +#Filter ref study for the ones that are kept after gene filtering +ref.study<-de.ref.study[de.ref.study$name==ref.study.name,] +fitted.genes<-16197 #Hard-coded here, found in powsimR object -```{r} +#Build a frame of all possible combinations +param.combis<-expand.grid(cellsPerCelltype,sampleSize) +colnames(param.combis)<-c("cellsPerCelltype","sampleSize") -##### -#General parameters +power.DE<-NULL +for(i in 1:nrow(param.combis)){ + detailed<-power.general.restrictedDoublets(param.combis$sampleSize[i], + param.combis$cellsPerCelltype[i], + transcriptome.mapped.reads, + ct.freq, "de",de.ref.study,ref.study.name, + cellsPerLane,read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits,ct, + disp.fun.param,mappingEfficiency = 1, + multipletRate=0,multipletFactor=1, + min.UMI.counts = 0, + perc.indiv.expr = 0, + nGenes=21000, + returnResultsDetailed = TRUE, + MTmethod="FDR") + + temp.overview<-detailed$overview.df -ct<-"CD14+ Monocytes" -ct.freq<-0.2 + #Filter power for the expressed genes (for comparison with simulation methods) + ref.study$not.filtered<-ref.study$rank<=min(fitted.genes, + detailed$overview.df$expressedGenes) + temp.overview$power.filtered<-mean(detailed$probs.df$power[ref.study$not.filtered]) + + #Filter overall power for simulated genes (for comparison with simulation methods) + ref.study$not.filtered<-ref.study$rank<=fitted.genes + temp.overview$overall.power.filtered<-mean(detailed$probs.df$combined.prob[ + ref.study$not.filtered]) + + power.DE<-rbind(power.DE,temp.overview) +} -cellsPerChanel<-20000 -costKit<-5600 -costFlowCell<-14032 -readsPerFlowcell<-4100*10^6 +#FDR corrected thresold (get only edgeR values) +power.fdr<-scPower::simulated.DE.values[simulated.DE.values$MT=="FDR",] +power.fdr<-power.fdr[startsWith(power.fdr$method,"edgeR"),] + +power.fdr<-merge(power.fdr,power.DE[,c("sampleSize","totalCells","expressedGenes", + "power.filtered","overall.power.filtered")], + by=c("sampleSize","totalCells")) +power.fdr$variable<-ifelse(endsWith(power.fdr$method,"powsimR"), + "powsimR","muscat") +#Change variable ordering in plots +power.fdr$variable<-factor(power.fdr$variable,levels=c("powsimR","muscat")) + +g.c<-ggplot(power.fdr,aes(x=expressed.genes,y=expressedGenes, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+geom_hline(yintercept = 16171)+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("Expressed genes - simulations")+ + ylab("Expressed genes - scPower")+ + theme_bw() -mapping.efficiency<-0.8 -minUMI<-3 -perc.indiv<-0.5 +g.d<-ggplot(power.fdr,aes(x=power,y=power.filtered, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("DE power - simulations")+ + ylab("DE power - scPower")+ + theme_bw() -budget<-seq(5000,100000,by=5000) - -######## -#Parameters for DE studies -readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) -cellsPerPerson<-c(100,200,500,700,seq(1000,9000,500)) +g.e<-ggplot(power.fdr,aes(x=overall.power,y=overall.power.filtered, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("Overall power - simulations")+ + ylab("Overall power - scPower")+ + theme_bw() ``` ```{r, eval=FALSE} +#Example code how the eQTL simulation is done (not run here due to runtime reasons) -##### -#Simulated DE studies - PBMC (10X) - -simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), - rank.max=c(20000,20000,10000,10000), - name=c("highES_unifRank","lowES_unifRank", - "highES_highRank","lowES_highRank")) - -print(simulated.parameters) +power.eqtl.sim<-NULL -#Number simulated DE genes -numGenes<-250 +#Set specifications +cellsPerCelltype<-c(100,200,500,1000,1500,2000,2500,3000) +sampleSize<-c(50,100,150,200) +ref.study.name<-"Blueprint (Tcells)" -de.raw.values<-NULL -for(s in 1:nrow(simulated.parameters)){ - - print(s) - - #Simulate prior for expression rank - ranks<-uniform.ranks.interval(start=1,end=simulated.parameters$rank.max[s], - numGenes=numGenes) - - #Simulate prior for effect sizes - set.seed(1) - effectSizes<-effectSize.DE.simulation(mean=simulated.parameters$mean.value[s], - sd=1,numGenes=numGenes) - - sample.DE.genes<-data.frame(ranks=ranks, FoldChange=effectSizes, - name="Simulated") - - max.values<-NULL - for(i in budget){ +repetition.rounds<-25 + +combis<-expand.grid(sampleSize,cellsPerPerson, + KEEP.OUT.ATTRS = FALSE) +colnames(combis)<-c("sampleSize","cellsPerPerson") + +for(c in 1:nrow(combis)){ + + nSamples<-combis$sampleSize[c] + nCells<-combis$cellsPerPerson[c] + + print(paste("Simulating parameter combination with",nSamples, + "samples",nCells,"cells")) + + #Run expression probability part of scPower + power.study<-power.general.restrictedDoublets(nSamples, + nCells, + transcriptome.mapped.reads, + ct.freq=1, + type="eqtl", + scPower::eqtl.ref.study, + study.name, + 20000, + scPower::read.umi.fit[read.umi.fit$type== + "10X_PBMC_1",], + gamma.mixed.fits, + ct, + disp.fun.param, + mappingEfficiency = 1, + multipletRate=0, + min.UMI.counts = 10, + perc.indiv=0.5, + nGenes=nGenes, + useSimulatedPower = TRUE, + returnResultsDetailed=TRUE) + + per.gene.estimates<-power.study$probs.df + + #Use the Bonferroni corrected threshold + sign.threshold<-0.05/(10*power.study$overview.df$expressedGenes) + + #Start simulation + for(j in 1:repetition.rounds){ - power.study<-optimize.constant.budget.restrictedDoublets(i,"de",ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - sample.DE.genes,"Simulated", - cellsPerChanel,read.umi.fit[ - read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange = cellsPerPerson, - readDepthRange = readDepth, - mappingEfficiency = mapping.efficiency, - min.UMI.counts = minUMI, - perc.indiv.expr = perc.indiv, - MTmethod = "FDR", - speedPowerCalc=TRUE) + foundDEgenes<-sapply(1:nrow(per.gene.estimates), function(i) + suppressWarnings(scPower:::power.eqtl.simulated(round(per.gene.estimates$mean.sum[i]), + per.gene.estimates$Rsq[i], + sign.threshold, + nSamples, + rep.times=1))) - max.values<-rbind(max.values, - power.study[which.max(power.study$powerDetect),]) + #Save results + power.eqtl.sim<-rbind(power.eqtl.sim, + data.frame(nSamples, + nCells, + sim.round=j, + sim.power=mean(foundDEgenes)) } - max.values$budget<-budget - max.values$parameters<-simulated.parameters$name[s] - - de.raw.values<-rbind(de.raw.values,max.values) -} +} -###### -# DE studies with observed priors - PBMC (10X) +#Calculate mean and sd deviation over all simulation runs +power.eqtl.sim<-power.eqtl.sim%>% + group_by(SampleSize,CellsPerson)%>% + summarize(sim_power_mean=mean(Sim.Power),sim_power_sd=sd(Sim.Power)) -#Filter the DE reference study for PBMC data sets -de.ref.study<-scPower::de.ref.study[de.ref.study$type=="PBMC",] +``` -de.real.values<-NULL -for(studyName in unique(de.ref.study$name)){ +```{r, fig.width=10,fig.height=11} +################################################################################ +# Part f (commparison with eQTL simulation) - max.values<-NULL - for(i in budget){ - - power.study<-optimize.constant.budget.restrictedDoublets(i,"de",ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - de.ref.study,studyName, - cellsPerChanel,read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange = cellsPerPerson, - readDepthRange = readDepth, - mappingEfficiency = mapping.efficiency, - min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, - MTmethod = "FDR", - speedPowerCalc=TRUE) - - max.values<-rbind(max.values, - power.study[which.max(power.study$powerDetect),]) - } - - max.values$budget<-budget - max.values$parameters<-studyName - de.real.values<-rbind(de.real.values,max.values) -} -``` +#Load precalculated values for eQTL power simulation (generated with code above) +power.eqtl.sim<-scPower::simulated.eQTL.values +power.fdr<-power.fdr[startsWith(power.fdr$method,"edgeR"),] + +power.eqtl.sim<-merge(power.eqtl.sim,power.eqtl[,c("sampleSize","totalCells","power")], + by.x=c("SampleSize","CellsPerson"), + by.y=c("sampleSize","totalCells")) + +g.f<-ggplot(power.eqtl.sim,aes(sim_power_mean,power))+ + geom_point()+geom_abline()+xlim(0,1)+ylim(0,1)+ + xlab("eQTL power - simulation")+ylab("eQTL power - scPower")+ + theme_bw() + +################################################################################ +# Part g,h (Runtime and memory requirement) + +#Create a table for the runtime (as a plot would not work visualizing the differences) +df<-data.frame(method=c("powsimR","muscat","scPower"), + approach=c("Simulation","Simulation","Analytic"), + runtime.num=c(192.06,75.34,0.14/60), + runtime.text=c("192 h","75 h","< 1 min"), + memory.num=c(48.03,34.75,1.12/1000), + memory.text=c("48 GB","35 GB","1 MB")) +df$method<-factor(df$method,levels=c("powsimR","muscat","scPower")) + +#Barplot with runtime +g.g<-ggplot(df,aes(x=method,y=runtime.num,fill=method))+ + geom_bar(stat="identity") + + geom_text(aes(x=method,y=runtime.num+10,label = runtime.text))+ + xlab("Method")+ylab("Runtime (h)")+ + scale_fill_manual(values=c("#F8766D","#00BFC4","#7CAE00"))+ + theme_bw()+ + theme(legend.position = "none") + + +#Barplot with memory +g.h<-ggplot(df,aes(x=method,y=memory.num,fill=method))+ + geom_bar(stat="identity") + + geom_text(aes(x=method,y=memory.num+2,label = memory.text))+ + xlab("Method")+ylab("Memory (GB)")+ + scale_fill_manual(values=c("#F8766D","#00BFC4","#7CAE00"))+ + theme_bw()+ + theme(legend.position = "none") + +g.cde<-ggarrange(g.c,g.d,g.e,ncol=3,nrow=1,labels=c("c","d","e"), + common.legend = TRUE, + legend="bottom") +g.fgh<-ggarrange(g.f,g.g,g.h,ncol=3,nrow=1,labels=c("f","g","h"), + common.legend = FALSE, align="hv") + +g<-ggarrange(g.ab,g.cde,g.fgh,ncol=1,nrow=3, heights=c(1,0.7,0.6), align="hv") + +print(g) -```{r} -#Parameters for eQTL studies -readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) -cellsPerPerson<-seq(500,9000,500) ``` -```{r, eval=FALSE} -##### -#Simulated eQTL studies - PBMC (10X) +Figure 3: Expression probability, DE/eQTL power and overall detection power and their validation in simulation studies. -simulated.parameters<-data.frame(mean.value=c(0.5,0.2,0.5,0.2), - rank.max=c(20000,20000,10000,10000), - name=c("highES_unifRank","lowES_unifRank", - "highES_highRank","lowES_highRank")) +a,b Power estimation using data driven priors for DE genes (a) and eQTL genes (b) dependent on the total sample size and the number of measured cells per cell type. The detection power is the product of the expression probability and the power to detect the genes as DE or eQTL genes, respectively. The fold change for DEGs and the R2 for eQTL genes were taken from published studies, together with the expression rank of the genes. For (a), the Blueprint CLL study with comparison iCLL vs mCLL was used, for (b), the Blueprint T cell study. The expression profile and expression probabilities in a single cell experiment with a specific number of samples and measured cells was estimated using our expression prior, setting the definition for expressed to > 10 counts in more than 50% of the individuals. Multiple testing correction was performed by using FDR adjusted p-values for DE power and FWER adjusted p-values for eQTL power. -print(simulated.parameters) +c-e The probabilities calculated in (a) were verified by the simulation-based methods powsimR and muscat with each point representing one parameter combination. -#Number simulated eQTL genes -numGenes<-2000 +f The eQTL power of (b) could be replicated with a self-implemented simulation. -eqtl.raw.values<-NULL -for(s in 1:nrow(simulated.parameters)){ - - print(s) - - #Simulate prior for expression rank - ranks<-uniform.ranks.interval(start=1,end=simulated.parameters$rank.max[s], - numGenes=numGenes) - - #Simulate prior for effect sizes - set.seed(1) - effectSizes<-effectSize.eQTL.simulation(mean=simulated.parameters$mean.value[s], - sd=0.2,numGenes=numGenes) - - sample.eqtl.genes<-data.frame(ranks=ranks,Rsq=effectSizes,name="Simulated") - - max.values<-NULL - for(i in budget){ - - power.study<-optimize.constant.budget.restrictedDoublets(i,"eqtl",ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - sample.eqtl.genes,"Simulated", - cellsPerChanel, - read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange = cellsPerPerson, - readDepthRange = readDepth, - mappingEfficiency = mapping.efficiency, - min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, - MTmethod = "Bonferroni", - speedPowerCalc=TRUE)) - - max.values<-rbind(max.values, - power.study[which.max(power.study$powerDetect),]) - } - max.values$budget<-budget - max.values$parameters<-simulated.parameters$name[s] - - eqtl.raw.values<-rbind(eqtl.raw.values,max.values) -} +g,h Runtime (g) and memory requirements (h) were drastically higher in the simulations than for our tool scPower during the evaluations of (c-e), showing the strength of our analytic model. +## Figure 4: Parameter optimization for constant budget. +Figure 4 shows two example calculations on how scPower selects the optimal experimental parameter combination for a specific budget, once for a DE study and once for an eQTL study. For an explanation of all the used parameters, please look into the introduction vignette. -###### -# eQTL studies with observed priors - PBMC (10X) +In contrast to the original publication, the parameter useSimulatedPower is set to FALSE and the parameter speedPowerCalc to TRUE for the eQTL budget optimization. Both parameters speed the calculation in the vignette, while changing the overall power only slightly. We show in the publication the more accurate version including simulated eQTL power for lowly expressed genes. +The difference between vignette and publication is especially visible in the eQTL power lines of Figure 4e,f, which have lower values here, as the power for genes with low expression values is set to 0 with speedPowerCalc=TRUE. -eqtl.real.values<-NULL -for(studyName in unique(eqtl.ref.study$name)){ +```{r,fig.width=10,fig.height=10} - max.values<-NULL - for(i in budget){ - - power.study<-optimize.constant.budget.restrictedDoublets(i,"eqtl",ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - eqtl.ref.study,studyName, - cellsPerChanel,read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange = cellsPerPerson, - readDepthRange = readDepth, - mappingEfficiency = mapping.efficiency, - min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, - MTmethod = "Bonferroni", - speedPowerCalc=TRUE)) - - max.values<-rbind(max.values, - power.study[which.max(power.study$powerDetect),]) - } - - max.values$budget<-budget - max.values$parameters<-studyName - eqtl.real.values<-rbind(eqtl.real.values,max.values) -} +costKit<-5600 +costFlowCell<-14032 +readsPerFlowcell<-4100*10^6 +cellsPerLane<-20000 +ct<-"CD4 T cells" +ct.freq<-0.25 +mappingEfficiency<-0.8 -``` +######## +# DE calculation -As mentioned above, percalculated values are loaded in the vignette instead, created with the code shown above (but set to eval=FALSE). +comp.type<-"de" +ref.study.name<-"Blueprint (CLL) iCLL-mCLL" +readDepth<-c(2000,5000,10000,15000,20000,30000,50000,70000) +cellsPerIndividual<-c(100,200,500,700,seq(1000,12000,500)) +totalBudget<-10000 -```{r, fig.height=8, fig.width=12} +power.study.de<-optimize.constant.budget.restrictedDoublets(totalBudget, + comp.type,ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, + de.ref.study,ref.study.name, + cellsPerLane, + read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits, + disp.fun.param, + nCellsRange=cellsPerIndividual, + readDepthRange=readDepth, + mappingEfficiency=mappingEfficiency, + sign.threshold = 0.05, + MTmethod="FDR") -#Get precalculated values -data(precalculatedBudgetOptim) #budget.optimization -eqtl.raw.values<-budget.optimization[budget.optimization$type=="eQTL_simulated_PBMC",] -de.raw.values<-budget.optimization[budget.optimization$type=="DE_simulated_PBMC",] -eqtl.real.values<-budget.optimization[budget.optimization$type=="eQTL_observed_PBMC",] -de.real.values<-budget.optimization[budget.optimization$type=="DE_observed_PBMC",] +######## +# eQTL subplot -#Replace probability names -var.labs<-setNames(c("Detection power","Cells per individual", - "Read depth","Sample size"), - c("powerDetect","totalCells","readDepth","sampleSize")) +comp.type<-"eqtl" +ref.study.name<-"Blueprint (Tcells)" +readDepth<-c(2000,5000,10000,15000,20000,30000,50000) +cellsPerIndividual<-c(100,200,500,700,seq(1000,9000,500)) +totalBudget<-30000 -#Data frame with dummy values to scale the y axes -dummy.plot<-data.frame(budget=c(min(budget),max(budget),min(budget),max(budget), - min(budget),max(budget),min(budget),max(budget)), - variable=c("powerDetect","powerDetect","readDepth", - "readDepth","totalCells","totalCells", - "sampleSize","sampleSize"), - value=c(0,1,min(readDepth),max(readDepth), - min(cellsPerPerson),max(cellsPerPerson),0,400)) +power.study.eqtl<-optimize.constant.budget.restrictedDoublets(totalBudget, + comp.type,ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, + eqtl.ref.study,ref.study.name, + cellsPerLane, + read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits, + disp.fun.param, + nCellsRange=cellsPerIndividual, + readDepthRange=readDepth, + mappingEfficiency=mappingEfficiency, + sign.threshold = 0.05, + MTmethod="Bonferroni", + #These two parameters are set differently + #in the publictions, but changed here to + #speed the calculation + useSimulatedPower = FALSE, #(in publication: TRUE) + speedPowerCalc = TRUE) #(in publication: FALSE) -#eQTL simulated values -plot.values<-eqtl.raw.values[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] -plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot -dummy.plot.temp$parameters<-plot.values$parameters[1] -g.eqtl.1<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ - geom_line()+geom_point()+ - facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ - geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Simulated parameters",values=col.set2)+ - theme_bw()+ - xlab("Budget")+ylab("Value") -#DE simulated values -plot.values<-de.raw.values[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] -plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot -dummy.plot.temp$parameters<-plot.values$parameters[1] -g.de.1<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ - geom_line()+geom_point()+ - facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ - geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Simulated parameters",values=col.set2)+ - theme_bw()+ - xlab("Budget")+ylab("Value") -#eQTL real values -plot.values<-eqtl.real.values[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] -plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot -dummy.plot.temp$parameters<-plot.values$parameters[1] -g.eqtl.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ - geom_line()+geom_point()+ - facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ - geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Observed parameters",values=col.set2)+ +################################################################################ +# Create grid plot + +power.study.plot<-power.study.de +power.study.plot$totalCells<-as.factor(power.study.plot$totalCells) +power.study.plot$readDepth<-as.factor(power.study.plot$readDepth) + +g.de<-ggplot(power.study.plot,aes(x=readDepth,y=totalCells, + color=powerDetect,size=log10(sampleSize)))+ + geom_point()+ylab("Cells per individual")+xlab("Read depth")+ + ggtitle("DE study")+ + scale_color_viridis("Detection\npower",limits=c(0,1))+ + scale_size_continuous("Sample size",breaks=c(3,2,1),labels=c(1000,100,10))+ theme_bw()+ - xlab("Budget")+ylab("Value") + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.title=element_text(size=8), + legend.text=element_text(size=8), + plot.title = element_text(hjust = 0.5)) -#DE real values -plot.values<-de.real.values[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] -plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot -dummy.plot.temp$parameters<-plot.values$parameters[1] -g.de.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ - geom_line()+geom_point()+ - facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ - geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Observed parameters",values=col.set2)+ +power.study.plot<-power.study.eqtl +power.study.plot$totalCells<-as.factor(power.study.plot$totalCells) +power.study.plot$readDepth<-as.factor(power.study.plot$readDepth) + +g.eqtl<-ggplot(power.study.plot,aes(x=readDepth,y=totalCells, + color=powerDetect,size=log(sampleSize)))+ + geom_point()+ylab("Cells per individual")+xlab("Read depth")+ + ggtitle("eQTL study")+ + scale_color_viridis("Detection\npower",limits=c(0,1))+ + scale_size_continuous("Sample size",breaks=c(3,2,1),labels=c(1000,100,10))+ theme_bw()+ - xlab("Budget")+ylab("Value") + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.title=element_text(size=8), + legend.text=element_text(size=8), + plot.title = element_text(hjust = 0.5)) + +g.a<-ggarrange(g.de,g.eqtl,ncol=2,nrow=1, labels=c("a","b"), + common.legend = TRUE, legend="bottom") + +################################################################################ +# Create corresponding line plots + +#Print maximal combination of values +max.study<-power.study.de[which.max(power.study.de$powerDetect),] + +colnames(power.study.de)[2:4]<-c("Detection power","Expression probability", + "DE power") + +#Show two-dimensional plots with always one parameter fixed +#Fixed read depths +power.study.plot<-power.study.de[power.study.de$readDepth==max.study$readDepth,] +power.study.plot<-reshape2::melt(power.study.plot, + id.vars=c("name","sampleSize","totalCells", + "usableCells","multipletFraction", + "ctCells","readDepth","readDepthSinglet", + "mappedReadDepth","expressedGenes")) +power.study.plot$variable<-factor(power.study.plot$variable, + levels=c("Expression probability","DE power", + "Detection power")) + +g.rd<-ggplot(power.study.plot,aes(totalCells,value,color=variable))+geom_line() +labels<-as.numeric(ggplot_build(g.rd)$layout$panel_params[[1]]$x.labels) + +g.rd<-g.rd + + scale_x_continuous("Cells per individual",sec.axis=sec_axis(~., breaks=labels, + labels=sapply(labels,function(c)floor( + sampleSizeBudgetCalculation.restrictedDoublets(c,max.study$readDepth, + totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), + name="Sample size"))+ + geom_vline(xintercept = max.study$totalCells)+ + ylab("Probability")+ + scale_color_manual("",values=col.power)+ + ylim(0,1)+ + theme_bw() + + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position=c(0.85,0.3), + legend.title=element_blank(), + legend.text=element_text(size=8)) + +#Fixed number of cells +power.study.plot<-power.study.de[power.study.de$totalCells==max.study$totalCells,] +power.study.plot<-reshape2::melt(power.study.plot, + id.vars=c("name","sampleSize","totalCells", + "usableCells","multipletFraction", + "ctCells","readDepth","readDepthSinglet", + "mappedReadDepth","expressedGenes")) +power.study.plot$variable<-factor(power.study.plot$variable, + levels=c("Expression probability","DE power", + "Detection power")) + +g.cp<-ggplot(power.study.plot,aes(readDepth,value,color=variable))+geom_line() +labels<-as.numeric(ggplot_build(g.cp)$layout$panel_params[[1]]$x.labels) + +g.cp <- g.cp + scale_x_continuous("Read depth",sec.axis=sec_axis(~., breaks=labels, + labels=sapply(labels,function(c)floor( + sampleSizeBudgetCalculation.restrictedDoublets(max.study$totalCells, + c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ + geom_vline(xintercept = max.study$readDepth)+ + ylab("Probability")+ + scale_color_manual("",values=col.power)+ + ylim(0,1)+ + theme_bw() + + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position=c(0.85,0.3), + legend.title=element_blank(), + legend.text=element_text(size=8)) + +#Line graph for eQTL part + +#Print maximal combination of values +max.study<-power.study.eqtl[which.max(power.study.eqtl$powerDetect),] + +colnames(power.study.eqtl)[2:4]<-c("Detection power","Expression probability", + "eQTL power") + +#Show two-dimensional plots with always one parameter fixed +#Fixed read depths +power.study.plot<-power.study.eqtl[power.study.eqtl$readDepth==max.study$readDepth,] +power.study.plot<-reshape2::melt(power.study.plot, + id.vars=c("name","sampleSize","totalCells", + "usableCells","multipletFraction", + "ctCells","readDepth","readDepthSinglet", + "mappedReadDepth","expressedGenes")) +power.study.plot$variable<-factor(power.study.plot$variable, + levels=c("Expression probability","eQTL power", + "Detection power")) + +g.rd.b<-ggplot(power.study.plot,aes(totalCells,value,color=variable))+geom_line() +labels<-as.numeric(ggplot_build(g.rd)$layout$panel_params[[1]]$x.labels) + +g.rd.b<-g.rd.b + + scale_x_continuous("Cells per individual",sec.axis=sec_axis(~., breaks=labels, + labels=sapply(labels,function(c)floor(sampleSizeBudgetCalculation.restrictedDoublets( + max.study$totalCells, + c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ + geom_vline(xintercept = max.study$totalCells)+ + ylab("Probability")+ + scale_color_manual("",values=col.power)+ + ylim(0,1)+ + theme_bw() + + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position=c(0.85,0.3), + legend.title=element_blank(), + legend.text=element_text(size=8)) + +#Fixed number of cells +power.study.plot<-power.study.eqtl[power.study.eqtl$totalCells==max.study$totalCells,] +power.study.plot<-reshape2::melt(power.study.plot, + id.vars=c("name","sampleSize","totalCells", + "usableCells","multipletFraction", + "ctCells","readDepth","readDepthSinglet", + "mappedReadDepth","expressedGenes")) +power.study.plot$variable<-factor(power.study.plot$variable, + levels=c("Expression probability","eQTL power", + "Detection power")) + +g.cp.b<-ggplot(power.study.plot,aes(readDepth,value,color=variable))+geom_line() +labels<-as.numeric(ggplot_build(g.cp)$layout$panel_params[[1]]$x.labels) + +g.cp.b <- g.cp.b + scale_x_continuous("Read depth",sec.axis=sec_axis(~., breaks=labels, + labels=sapply(labels,function(c)floor(sampleSizeBudgetCalculation.restrictedDoublets( + max.study$totalCells, + c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ + geom_vline(xintercept = max.study$readDepth)+ + scale_color_manual("",values=col.power)+ + ylab("Probability")+ + ylim(0,1)+ + theme_bw() + + theme(axis.title=element_text(size=10), + axis.text=element_text(size=8), + legend.position=c(0.85,0.3), + legend.title=element_blank(), + legend.text=element_text(size=8)) -#Overview plot -g<-ggarrange(g.de.1,g.eqtl.1,g.de.2,g.eqtl.2, - ncol=1,nrow=4,labels=c("A","B","C","D"),align="hv") -print(g) +g.b<-ggarrange(g.rd,g.cp,g.rd.b,g.cp.b,ncol=4,nrow=1,labels=c("c","d","e","f"), + align="hv",common.legend=TRUE,legend="bottom") + +g<-ggarrange(g.a,g.b,ncol=1,nrow=2,heights=c(1.7,1)) +print(g) ``` -Figure 4: Optimal parameters for varying budgets and 10X Genomics data. The figure shows the maximal reachable detection power (y-axis, first column) for a given experimental budget (x-axis) and the corresponding optimal parameter combinations for that budget (y-axis, second till fourth column). The colored lines indicate different effect sizes and gene expression rank distributions. Subplots A-B visualize different simulated effect sizes and rank distributions (simulation names see text) for DEG studies (A) and eQTL studies (B) with models fitted on 10X PBMC data. Subplots C-D visualize effect sizes and rank distributions observed in cell type sorted bulk RNA-seq DEG studies (C) and eQTL studies (D) with model fits analogously to A-B. +Figure 4: Parameter optimization for constant budget. -## Figure 5: Optimal parameters for varying budgets and Drop-seq and Smart-seq data +a,b Maximizing detection power by selecting the best combination of cells per individual and read depth for a DE study with a budget of 10,000€ (a) and an eQTL study with a budget of 30,000€ (b). Sample size is uniquely defined given the other two parameters due to the budget restriction and visualized using the point size. -The same evaluation is done in Figure 5 than in Figure 4, only this time with priors of Drop-seq lung data and Smart-seq pancreas data instead of 10X Genomics PBMC data. Similar to Figure 4, the code for the calculation is given, but set to eval=FALSE due to long runtime. Instead, precalculated values are taken for the plotting. +c-f Overall detection power dependent on cost determining factors. Influence of the cells per individual given the optimized read depth (c,e) and of the read depth given the optimized number of cells per individual (d,f). (c,d) corresponds to the DE study in (a), visualized in (a) by the red frame around the row with the optimal number of cells (corresponding to (c)) and the red frame around the column with the optimal read depth (corresponding to (d)). Same frames for (e,f) in the eQTL study (b).The optimal sample size values are shown in the upper x axes for (c-f). Vertical line in the subplots marks the optimal parameter combination. + +Effect sizes were chosen as in Figure 3. Gene expression is defined as detected in >50% (DE analysis) or >9.5% (eQTL analysis) of individuals. + +## Figure 5: Optimal parameters for varying budgets and 10X data + +How the optimal parameters develop for an increasing experimental budget, is shown in Figures 5. As this means running the optimization function many times, this takes too long to run it in the vignette. Instead, the code is given, but the evaluation set to FALSE. Interested users can run the calculation, by setting it to TRUE. Calculations are done for DE and eQTL studies, with observed and simulated priors. ```{r} -#Drop-seq parameters -#Cell type -ct<-"NK cell" +##### +#General parameters + +ct<-"CD14+ Monocytes" ct.freq<-0.2 -cellsPerChanel<-NA -libPrepCell<-0.09 +cellsPerChanel<-20000 +costKit<-5600 costFlowCell<-14032 readsPerFlowcell<-4100*10^6 -readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) -cellsPerPerson<-c(100,200,500,700,seq(1000,9000,500)) - mapping.efficiency<-0.8 -nFittedGenes<-24000 - -#Model growth rate constant -multipletRateGrowth<-"constant" -multipletRate<-0.05 +minUMI<-3 +perc.indiv<-0.5 + +budget<-seq(5000,100000,by=5000) -#Number of DE genes to be modeled -numGenes<-250 +######## +#Parameters for DE studies +readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) +cellsPerPerson<-c(100,200,500,700,seq(1000,9000,500)) ``` ```{r, eval=FALSE} ##### -#Simulated DE studies - lung (drop-seq) +#Simulated DE studies - PBMC (10X) simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), rank.max=c(20000,20000,10000,10000), @@ -774,7 +970,10 @@ simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), print(simulated.parameters) -de.raw.values.lung<-NULL +#Number simulated DE genes +numGenes<-250 + +de.raw.values<-NULL for(s in 1:nrow(simulated.parameters)){ print(s) @@ -786,7 +985,7 @@ for(s in 1:nrow(simulated.parameters)){ #Simulate prior for effect sizes set.seed(1) effectSizes<-effectSize.DE.simulation(mean=simulated.parameters$mean.value[s], - sd=1, numGenes=numGenes) + sd=1,numGenes=numGenes) sample.DE.genes<-data.frame(ranks=ranks, FoldChange=effectSizes, name="Simulated") @@ -794,21 +993,18 @@ for(s in 1:nrow(simulated.parameters)){ max.values<-NULL for(i in budget){ - power.study<-optimize.constant.budget.libPrepCell(i,"de",ct,ct.freq, - libPrepCell,costFlowCell,readsPerFlowcell, + power.study<-optimize.constant.budget.restrictedDoublets(i,"de",ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, sample.DE.genes,"Simulated", cellsPerChanel,read.umi.fit[ read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits.drop, - disp.fun.param.drop, + gamma.mixed.fits, + disp.fun.param, nCellsRange = cellsPerPerson, readDepthRange = readDepth, mappingEfficiency = mapping.efficiency, - multipletRate = multipletRate, min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, - nGenes=nFittedGenes, - multipletRateGrowth = multipletRateGrowth, MTmethod = "FDR", speedPowerCalc=TRUE) @@ -818,36 +1014,33 @@ for(s in 1:nrow(simulated.parameters)){ max.values$budget<-budget max.values$parameters<-simulated.parameters$name[s] - de.raw.values.lung<-rbind(de.raw.values.lung,max.values) -} + de.raw.values<-rbind(de.raw.values,max.values) +} -##### -#Observed DE studies - lung (drop-seq) +###### +# DE studies with observed priors - PBMC (10X) -de.priors.lung<-de.ref.study[de.ref.study$type=="lung",] - -de.real.values.lung<-NULL -for(studyName in unique(de.priors.lung$name)){ +#Filter the DE reference study for PBMC data sets +de.ref.study<-scPower::de.ref.study[de.ref.study$type=="PBMC",] + +de.real.values<-NULL +for(studyName in unique(de.ref.study$name)){ max.values<-NULL for(i in budget){ - power.study<-optimize.constant.budget.libPrepCell(i,"de",ct,ct.freq, - libPrepCell,costFlowCell,readsPerFlowcell, - de.priors.lung,studyName, - cellsPerChanel,read.umi.fit[ - read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits.drop, - disp.fun.param.drop, - nCellsRange = cellsPerPerson, - readDepthRange = readDepth, - mappingEfficiency = mapping.efficiency, - multipletRate = multipletRate, - min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, - nGenes=nFittedGenes, - multipletRateGrowth = multipletRateGrowth, - MTmethod = "FDR", - speedPowerCalc=TRUE) + power.study<-optimize.constant.budget.restrictedDoublets(i,"de",ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, + de.ref.study,studyName, + cellsPerChanel,read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits, + disp.fun.param, + nCellsRange = cellsPerPerson, + readDepthRange = readDepth, + mappingEfficiency = mapping.efficiency, + min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, + MTmethod = "FDR", + speedPowerCalc=TRUE) max.values<-rbind(max.values, power.study[which.max(power.study$powerDetect),]) @@ -855,50 +1048,31 @@ for(studyName in unique(de.priors.lung$name)){ max.values$budget<-budget max.values$parameters<-studyName - de.real.values.lung<-rbind(de.real.values.lung,max.values) + de.real.values<-rbind(de.real.values,max.values) } - ``` ```{r} - -#Smart-seq parameters - -#Parameters -readDepth.smart<-c(20000,50000,seq(100000,1000000,100000),1500000,2000000) -cellsPerPerson.smart<-c(50,100,200,500,700,seq(1000,9000,500)) - -mapping.efficiency<-0.8 -min.norm.count<-minUMI -perc.indiv.expr<-perc.indiv - -ct<-"alpha" -ct.freq<-0.2 - -prepCostCell<-13 -costFlowCell<-14032 -readsPerFlowcell<-4100*10^6 - -nFittedGenes<-22000 - -#Number DE genes to be modeled -numGenes<-250 - +#Parameters for eQTL studies +readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) +cellsPerPerson<-seq(500,9000,500) ``` ```{r, eval=FALSE} - ##### -#Simulated DE studies - pancreas (smart-seq) +#Simulated eQTL studies - PBMC (10X) -simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), +simulated.parameters<-data.frame(mean.value=c(0.5,0.2,0.5,0.2), rank.max=c(20000,20000,10000,10000), name=c("highES_unifRank","lowES_unifRank", "highES_highRank","lowES_highRank")) print(simulated.parameters) -de.raw.values.panc<-NULL +#Number simulated eQTL genes +numGenes<-2000 + +eqtl.raw.values<-NULL for(s in 1:nrow(simulated.parameters)){ print(s) @@ -909,29 +1083,27 @@ for(s in 1:nrow(simulated.parameters)){ #Simulate prior for effect sizes set.seed(1) - effectSizes<-effectSize.DE.simulation(mean=simulated.parameters$mean.value[s], - sd=1,numGenes=numGenes) + effectSizes<-effectSize.eQTL.simulation(mean=simulated.parameters$mean.value[s], + sd=0.2,numGenes=numGenes) - sample.DE.genes<-data.frame(ranks=ranks, FoldChange=effectSizes, - geneLength=5000, name="Simulated") + sample.eqtl.genes<-data.frame(ranks=ranks,Rsq=effectSizes,name="Simulated") max.values<-NULL for(i in budget){ - power.study<-optimize.constant.budget.smartseq(i,"de",ct,ct.freq, - prepCostCell,costFlowCell, - readsPerFlowcell, - sample.DE.genes,"Simulated", - gamma.mixed.fits.smart, - disp.fun.param.smart, - nCellsRange = cellsPerPerson.smart, - readDepthRange = readDepth.smart, - mappingEfficiency = mapping.efficiency, - min.norm.count = min.norm.count, - perc.indiv.expr = perc.indiv.expr, - nGenes=nFittedGenes, - MTmethod = "FDR", - speedPowerCalc=TRUE) + power.study<-optimize.constant.budget.restrictedDoublets(i,"eqtl",ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, + sample.eqtl.genes,"Simulated", + cellsPerChanel, + read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits, + disp.fun.param, + nCellsRange = cellsPerPerson, + readDepthRange = readDepth, + mappingEfficiency = mapping.efficiency, + min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, + MTmethod = "Bonferroni", + speedPowerCalc=TRUE)) max.values<-rbind(max.values, power.study[which.max(power.study$powerDetect),]) @@ -939,140 +1111,248 @@ for(s in 1:nrow(simulated.parameters)){ max.values$budget<-budget max.values$parameters<-simulated.parameters$name[s] - de.raw.values.panc<-rbind(de.raw.values.panc,max.values) -} + eqtl.raw.values<-rbind(eqtl.raw.values,max.values) +} -##### -#Observed DE studies - pancreas (smart-seq) -de.priors.panc<-de.ref.study[de.ref.study$type=="pancreas",] - -de.real.values.panc<-NULL -for(studyName in unique(de.priors.panc$name)){ + + +###### +# eQTL studies with observed priors - PBMC (10X) + +eqtl.real.values<-NULL +for(studyName in unique(eqtl.ref.study$name)){ max.values<-NULL for(i in budget){ - power.study<-optimize.constant.budget.smartseq(i,"de",ct,ct.freq, - prepCostCell,costFlowCell,readsPerFlowcell, - de.priors.panc,studyName, - gamma.mixed.fits.smart, - disp.fun.param.smart, - nCellsRange = cellsPerPerson.smart, - readDepthRange = readDepth.smart, - mappingEfficiency = mapping.efficiency, - min.norm.count = min.norm.count, - perc.indiv.expr = perc.indiv.expr, - nGenes=nFittedGenes, - MTmethod = "FDR", - speedPowerCalc=TRUE) - + power.study<-optimize.constant.budget.restrictedDoublets(i,"eqtl",ct,ct.freq, + costKit,costFlowCell,readsPerFlowcell, + eqtl.ref.study,studyName, + cellsPerChanel,read.umi.fit[read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits, + disp.fun.param, + nCellsRange = cellsPerPerson, + readDepthRange = readDepth, + mappingEfficiency = mapping.efficiency, + min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, + MTmethod = "Bonferroni", + speedPowerCalc=TRUE)) + max.values<-rbind(max.values, power.study[which.max(power.study$powerDetect),]) } max.values$budget<-budget max.values$parameters<-studyName - de.real.values.panc<-rbind(de.real.values.panc,max.values) + eqtl.real.values<-rbind(eqtl.real.values,max.values) } ``` -```{r, fig.height=8, fig.width=12} +As mentioned above, percalculated values are loaded in the vignette instead, created with the code shown above (but set to eval=FALSE). + +```{r, fig.height=15, fig.width=15} #Get precalculated values -data(precalculatedBudgetOptim) -de.raw.values.lung<-budget.optimization[budget.optimization$type=="DE_simulated_lung",] -de.raw.values.panc<-budget.optimization[budget.optimization$type=="DE_simulated_pancreas",] -de.real.values.lung<-budget.optimization[budget.optimization$type=="DE_observed_lung",] -de.real.values.panc<-budget.optimization[budget.optimization$type=="DE_observed_pancreas",] +data(precalculatedBudgetOptim) #budget.optimization +eqtl.raw.values<-budget.optimization[budget.optimization$type=="eQTL_simulated_PBMC",] +de.raw.values<-budget.optimization[budget.optimization$type=="DE_simulated_PBMC",] +eqtl.real.values<-budget.optimization[budget.optimization$type=="eQTL_observed_PBMC",] +de.real.values<-budget.optimization[budget.optimization$type=="DE_observed_PBMC",] -dummy.plot.smart<-data.frame(budget=c(min(budget),max(budget),min(budget),max(budget), +#Replace probability names +var.labs<-setNames(c("Detection power","Cells per individual", + "Read depth","Sample size"), + c("powerDetect","totalCells","readDepth","sampleSize")) + +#Data frame with dummy values to scale the y axes +dummy.plot<-data.frame(budget=c(min(budget),max(budget),min(budget),max(budget), min(budget),max(budget),min(budget),max(budget)), - variable=c("powerDetect","powerDetect","readDepth","readDepth", - "totalCells","totalCells","sampleSize","sampleSize"), - value=c(0,1,0,500000, - min(cellsPerPerson.smart),1000,0,100)) + variable=c("powerDetect","powerDetect","readDepth", + "readDepth","totalCells","totalCells", + "sampleSize","sampleSize"), + value=c(0,1,min(readDepth),max(readDepth), + min(cellsPerPerson),max(cellsPerPerson),0,400)) -#DE lung simulated values -plot.values<-de.raw.values.lung[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] +#eQTL simulated values +plot.values<-eqtl.raw.values[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) dummy.plot.temp<-dummy.plot dummy.plot.temp$parameters<-plot.values$parameters[1] -g.lung<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ +g.eqtl.1<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ geom_line()+geom_point()+ facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Simulated parameters",values=col.set2)+ + scale_color_manual("Simulated parameters",values=col.paired[c(2,4,1,3)])+ theme_bw()+ - xlab("Budget")+ylab("Value") + xlab("Budget")+ylab("eQTL study") -#DE pancreas simulated values -plot.values<-de.raw.values.panc[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] +#DE simulated values +plot.values<-de.raw.values[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot.smart +dummy.plot.temp<-dummy.plot dummy.plot.temp$parameters<-plot.values$parameters[1] -g.panc<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ +g.de.1<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ geom_line()+geom_point()+ facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Simulated parameters",values=col.set2)+ + scale_color_manual("Simulated parameters",values=col.paired[c(2,4,1,3)])+ theme_bw()+ - xlab("Budget")+ylab("Value") + xlab("Budget")+ylab("DE study") -#De lung real values -plot.values<-de.real.values.lung[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] +#Merge values from eQTL study and DE study +all.studies<-c(unique(de.real.values$parameters), + unique(eqtl.real.values$parameters)) + +#eQTL real values +plot.values<-eqtl.real.values[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) dummy.plot.temp<-dummy.plot -dummy.plot.temp$parameters<-de.real.values.lung$parameters[1] -g.lung.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ +dummy.plot.temp$parameters<-plot.values$parameters[1] +g.eqtl.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ geom_line()+geom_point()+ facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Observed parameters",values=col.set2)+ + scale_color_manual("Observed parameters",values=col.set.new[5:10], + labels=all.studies,limits=all.studies)+ theme_bw()+ - xlab("Budget")+ylab("Value") - -#De pancreas real values -plot.values<-de.real.values.panc[,c("budget","powerDetect","totalCells","readDepth", - "sampleSize","parameters")] + xlab("Budget")+ylab("eQTL study") + +#DE real values +plot.values<-de.real.values[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) -dummy.plot.temp<-dummy.plot.smart -dummy.plot.temp$parameters<-de.real.values.panc$parameters[1] -g.panc.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ +dummy.plot.temp<-dummy.plot +dummy.plot.temp$parameters<-plot.values$parameters[1] +g.de.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ geom_line()+geom_point()+ facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ geom_blank(data=dummy.plot.temp)+ - scale_color_manual("Observed parameters",values=col.set2)+ + scale_color_manual("Observed parameters",values=col.set.new[5:10], + labels=all.studies,limits=all.studies)+ theme_bw()+ - xlab("Budget")+ylab("Value") + xlab("Budget")+ylab("DE study") #Overview plot -g<-ggarrange(g.lung,g.panc,g.lung.2,g.panc.2, - ncol=1,nrow=4,labels=c("A","B","C","D"),align="hv") +g.proto<-ggarrange(g.de.1,g.eqtl.1,nrow=2,common.legend = TRUE, + legend="bottom",labels=c("a","b")) + +g.real<-ggarrange(g.de.2,g.eqtl.2,nrow=2,common.legend = TRUE, + legend="bottom",labels=c("c","d")) + +row1 <- ggplot() + annotate(geom = 'text', x=1, y=1, + label="Prototypic scenarios", + size=6, angle = 90) + theme_void() + +row2 <- ggplot() + annotate(geom = 'text', x=1, y=1, + label="Real world scenarios", + size=6,angle = 90) + theme_void() + +g<-ggarrange(row1,g.proto,row2,g.real,nrow=2,ncol=2, + widths=c(0.03,1)) + print(g) ``` -Figure 5: Optimal parameters for varying budgets and Drop-seq and Smart-seq2 data. The figure shows the maximal reachable detection power (y-axis, first column) for a given experimental budget (x-axis) and the corresponding optimal parameter combinations for that budget (y-axis, second till fourth column). The colored lines indicate different effect sizes and gene expression rank distributions. Subplots A-B visualize different simulated effect sizes and rank distributions (simulation names see text) for DE studies with models fitted on Drop-seq lung data (A) and Smart-seq2 pancreas data (B). Subplots C-D visualize effect sizes and rank distributions observed in cell type sorted bulk RNA-seq DE studies with model fits analogously to A-B. +Figure 5: Optimal parameters for varying budgets and 10X Genomics data. The maximal reachable detection power (column 1) and the corresponding optimal parameter combinations (column 2-4) change depending on the given experimental budget (x-axis). The colored lines indicate different effect sizes and gene expression rank distributions. + +a,b Different simulated effect sizes and rank distributions for DEG studies (a) and eQTL studies (b) with models fitted on 10X PBMC data. highES = high effect sizes, lowES = low effect sizes, highRank = high expression ranks and unifRank = uniformly distributed expression ranks (always relative to effect sizes observed in published studies). + +c,d Effect sizes and rank distributions observed in cell type sorted bulk RNA-seq DEG studies (c) and eQTL studies (d) with model fits analogously to (a,b). + +Expression thresholds were chosen as for Figure 4. + + # Supplementary Figures -## Figure S1: PBMC data set +## Figure S1: Power to detect rare cell types + +Model to calculate the power to detect rare cell types. Please look into the introduction vignette, section "Part 3: Power estimation to detect a sufficent number of cells in a specific cell types" for an explanation of all parameters. The cell type frequencies are taken from BioRad (see citation in the publication), all other parameters are set to realistic values. + +```{r, fig.width=8, fig.height=6} + +prob=0.95 + +#Sample size +Nind <- c(100, 150, 200, 300) + +#Number of cells required +N.min.cells<-c(1,10,20,50) + +#Take cell type frequencies estimated from BioRad (see above) +cell.types.biorad<-data.frame(ct=c("B cells", "Monocytes", "NK cells", + "Dendritic cells","CD4 T cells","CD8 T cells"), + freq=c(13,13,7,1.5,30,13)/100) + + +filter.ct<-c("CD4 T cells", "NK cells", "Dendritic cells", "Monocytes") +cell.type.comp<-cell.types.biorad[cell.types.biorad$ct %in% filter.ct,] + +#Set frequency behind the cell type labels (add a small number to round up at 0.5) +cell.type.comp$ct<-paste0(cell.type.comp$ct, + " (",round(cell.type.comp$freq*100+0.01,1),"%)") + + +#Arrange the cell types in the order of frequency +cell.type.comp<-cell.type.comp[order(cell.type.comp$freq),] +cell.type.comp$ct<-factor(cell.type.comp$ct,levels=cell.type.comp$ct) + +#Test each possible parameter combination +parameter.combinations<-expand.grid(prob,N.min.cells,cell.type.comp$ct,Nind) +colnames(parameter.combinations)<-c("prob.cutoff","min.num.cells","ct","num.indivs") + +#Merge cell types with frequencies +parameter.combinations<-merge(parameter.combinations,cell.type.comp) + +parameter.combinations$result.ssize<-mapply(scPower::number.cells.detect.celltype, + parameter.combinations$prob.cutoff, + parameter.combinations$min.num.cells, + parameter.combinations$freq, + parameter.combinations$num.indivs) + + + +#Labels samples +axis.labeller<-function(variable,value){ + return(paste(value,"individuals")) +} + +g<-ggplot(parameter.combinations,aes(x=min.num.cells,y=result.ssize,col=ct))+ + geom_line()+geom_point()+facet_wrap(~num.indivs,ncol=2, labeller = axis.labeller)+ + xlab("Minimal number of cells from target cell type per individual")+ + ylab("Cells per individual")+ + scale_color_manual("Cell type",values=col.set2)+ + scale_y_log10()+ + theme_bw() + + theme(axis.title=element_text(size=12), + axis.text=element_text(size=8), + aspect.ratio = 0.8, + legend.position = "bottom")+ + guides(col=guide_legend(nrow=2,byrow=TRUE)) + +print(g) +``` + +Figure S1: Power to detect rare cell types. Required number of cells per individual (y-axis, log scale) to detect the minimal number of cells from a target cell type per individual (x-axis) with a probability of 95%. The probability depends on the total number of individuals and the frequency of the target cell type (purple, red, yellow, green). Note that the required number of cells per sample only counts “correctly measured” cells (no doublets etc), so the number is a lower bound for the required cells to be sequenced. + +## Figure S2: PBMC data set -Figure S1 shows the UMAP of the PBMC data set with cell type annotation and marker gene expression. Due to runtime reasons, the preprocessing is not performed here again. It is in detail described in the methods part of the publication. +Figure S2 shows the UMAP of the PBMC data set with cell type annotation and marker gene expression. Due to runtime reasons, the preprocessing is not performed here again. It is in detail described in the methods part of the publication. -## Figure S2 - S4: Steps fitting the gamma mixed model +## Figure S3 - S5: Steps fitting the gamma mixed model -Fitting the gamma mixed model is too runtime intensive for the vignette. The fitting for a small data set is shown in the introduction vignette. For our large data set, it was done exactly the same way, using the same functions. Also the corresponding plots to Figure S2 to S4 are shown in the introduction vignette and are therefore not reproduced here again. Figure S2 shows how mean values sampled for the fitted gamma mixture distribution are compared to the observed mean values, this is shown in the introduction vignette, section "Comparison of gamma mixed fits with original means". The next section "Parameterization of the parameters of the gamma fits by the mean UMI counts per cell" shows the relationship between the gamma mixed parameters and the mean UMI counts per cell, as depicted in Figure S3. And the relationship between UMI counts and mapped read depth is shown in section "Fitting a functions for UMI counts dependent on read depth", as in Figure S4. +Fitting the gamma mixed model is too runtime intensive for the vignette. The fitting for a small data set is shown in the introduction vignette. For our large data set, it was done exactly the same way, using the same functions. Also the corresponding plots to Figure S3 to S5 are shown in the introduction vignette and are therefore not reproduced here again. Figure S3 shows how mean values sampled for the fitted gamma mixture distribution are compared to the observed mean values, this is shown in the introduction vignette, section "Comparison of gamma mixed fits with original means". The next section "Parameterization of the parameters of the gamma fits by the mean UMI counts per cell" shows the relationship between the gamma mixed parameters and the mean UMI counts per cell, as depicted in Figure S4. And the relationship between UMI counts and mapped read depth is shown in section "Fitting a functions for UMI counts dependent on read depth", as in Figure S5. -## Figure S5: Expression probability model for a count threshold larger than zero. +## Figure S6: Expression probability model for a count threshold > 0 The supplementary figure shows the expression probability model for all runs of our own data set. It is an extension to Figure 2, where only the results for "Run 5" are shown. For an explanation of the calculation, please look at the description text for Figure 2. -```{r, fig.height=12,fig.width=8} +```{r, fig.height=10} #Load observed gene counts (precalculated) data(precalculatedObservedGeneCounts) #observed.gene.counts @@ -1127,24 +1407,25 @@ g<-ggplot(plot.estimates,aes(x=num.cells,y=value))+ scale_color_manual("Target read depth",values=col.set2)+ scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ scale_fill_manual(values=col.set2)+ + scale_linetype_discrete(name="Curve",labels=c("Observed","Estimated")) + theme_bw() + theme(axis.title=element_text(size=12), axis.text=element_text(size=8), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical")+ - guides(linetype=FALSE,fill=FALSE) + guides(fill=FALSE) print(g) ``` -Figure S5: Expression probability model for a count threshold larger than zero. Gene curve fits using our gamma mixed models parameterized over UMI counts per cell for all our six runs. The definition for an expressed gene was parameterized in the way that a gene needs to have a count > 0 in more than 50% of the individuals. +Figure S6: Expression probability model for a count threshold > 0. The number of expressed genes expected under our model (dashed line) closely matches the observed number of expressed genes (solid line) dependent on the number of cells per cell type (cell type indicated by point symbol) for all 6 batches (see panel titles) of the training PBMC data set (Supplementary Table S2). A gene is defined as expressed if it has > 0 counts in more than 50% of the individuals. -## Figure S6: Expression probability model for a count threshold larger than ten. +## Figure S7: Expression probability model for a count threshold > 10 -The supplementary figure shows the same as Figure S5, but with a count threshold of ten instead of zero. +The supplementary figure shows the same as Figure S6, but with a count threshold of 10 instead of 0 -```{r, fig.height=12,fig.width=8} +```{r, fig.height=10} #Load observed gene counts (precalculated) data(precalculatedObservedGeneCounts) #observed.gene.counts @@ -1199,73 +1480,18 @@ g<-ggplot(plot.estimates,aes(x=num.cells,y=value))+ scale_color_manual("Target read depth",values=col.set2)+ scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ scale_fill_manual(values=col.set2)+ + scale_linetype_discrete(name="Curve",labels=c("Observed","Estimated")) + theme_bw() + theme(axis.title=element_text(size=12), axis.text=element_text(size=8), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical")+ - guides(linetype=FALSE,fill=FALSE) + guides(fill=FALSE) print(g) ``` -Figure S6: Expression probability model for a count threshold larger than ten. Gene curve fits using our gamma mixed models parameterized over UMI counts per cell for all our six runs. The definition for an expressed gene was parameterized in the way that a gene needs to have a count > 10 in more than 50% of the individuals. - -## Figure S7: Influence of the number of measured cells per cell type and individual and the mean reads per cell on the number of detectable genes. - -The supplemenatry figure shows how the expected number of expressed/detectable genes depends on the experimental parameters cells per person and read depth. It is an extension of Figure 3, where only the expression probability is shown. - -```{r,fig.width=6} - -ct<-"CD4 T cells" -cellCounts<-c(100,200,500,1000,1500,2000,2500,3000) -readDepths<-c(12500,25000,37500,50000) - -minUMI.counts<-10 -measuredIndivs<-14 -numInd.threshold<-0.5 -nGenes<-21000 - -geneCounts<-NULL -for(r in readDepths){ - for(c in cellCounts){ - count.est<-scPower::estimate.exp.prob.param(nSamples=measuredIndivs, - readDepth=r,nCellsCt=c, - scPower::read.umi.fit[ - read.umi.fit$type=="10X_PBMC_1",], - scPower::gamma.mixed.fits, - ct=ct, - scPower::disp.fun.param, - nGenes=nGenes, - min.counts=minUMI.counts, - perc.indiv=numInd.threshold) - - geneCounts<-rbind(geneCounts,data.frame(meanReads=r,numCells=c, - count.est=round(sum(count.est)))) - } -} - -geneCounts$meanReads<-as.factor(geneCounts$meanReads) -geneCounts$numCells<-as.factor(geneCounts$numCells) -g<-ggplot(geneCounts,aes(x=numCells,y=meanReads,fill=count.est))+ - geom_tile()+ - geom_text(aes(label=count.est,color=count.est>8000))+ - scale_colour_manual(values=c("white", "black"))+ - xlab("Number of measured cells per cell type and individual")+ - ylab("Mean reads per cell")+ - scale_fill_viridis("Detectable genes")+ - theme_bw()+ - theme(axis.title=element_text(size=12), - axis.text=element_text(size=10), - legend.title=element_text(size=10), - legend.text=element_text(size=10))+ - guides(colour=FALSE) - -print(g) - -``` - -Figure S7: Influence of the number of measured cells per cell type and individual and the mean reads per cell on the number of detectable genes. Exemplarily, it is shown for the cell type CD4 T cells and a study size of 14 individuals. The definition for a detectable gene is set to at least 10 counts in more than 50% of all individuals. +Figure S7: Expression probability model for a count threshold > 10. Variant of Figure S6 with a count threshold of 10 instead of 0. ## Figure S8: Expression rank priors from cell type sorted bulk studies @@ -1282,7 +1508,7 @@ de.ranks.panc<-scPower::de.ref.study[de.ref.study$type=="pancreas",] eqtl.ranks$name<-paste0("Blueprint (",eqtl.ranks$cellType,")") eqtl.g<-ggplot(eqtl.ranks, aes(rank, col=name)) + stat_ecdf(geom = "step") + xlab("Number of expressed genes") +ylab("Percentage of eQTL genes")+ - scale_color_manual("Study", values=col.set2[5:6])+ + scale_color_manual("Study", values=col.set.new[1:2])+ theme_bw() + xlim(0,30000) + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1294,7 +1520,7 @@ eqtl.g<-ggplot(eqtl.ranks, aes(rank, col=name)) + stat_ecdf(geom = "step") + de.g<-ggplot(de.ranks, aes(rank, col=name)) + stat_ecdf(geom = "step") + xlab("Number of expressed genes") +ylab("Percentage of DE genes")+ - scale_color_manual("Study", values=col.set2)+ + scale_color_manual("Study", values=col.set.new[3:6])+ theme_bw() + xlim(0,30000) + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1307,7 +1533,7 @@ de.g<-ggplot(de.ranks, aes(rank, col=name)) + stat_ecdf(geom = "step") + de.ranks.lung$name<-gsub("_"," ",de.ranks.lung$name) de.lung<-ggplot(de.ranks.lung, aes(rank, col=name)) + stat_ecdf(geom = "step") + xlab("Number of expressed genes") +ylab("Percentage of DE genes")+ - scale_color_manual("Study", values=col.set2)+ + scale_color_manual("Study", values=col.set.new[7])+ theme_bw() + xlim(0,30000) + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1320,7 +1546,7 @@ de.lung<-ggplot(de.ranks.lung, aes(rank, col=name)) + stat_ecdf(geom = "step") + de.ranks.panc$name<-gsub("_"," ",de.ranks.panc$name) de.panc<-ggplot(de.ranks.panc, aes(rank, col=name)) + stat_ecdf(geom = "step") + xlab("Number of expressed genes") +ylab("Percentage of DE genes")+ - scale_color_manual("Study", values=col.set2)+ + scale_color_manual("Study", values=col.set.new[8:9])+ theme_bw() + xlim(0,30000) + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1330,13 +1556,13 @@ de.panc<-ggplot(de.ranks.panc, aes(rank, col=name)) + stat_ecdf(geom = "step") + aspect.ratio = 0.8)+ guides(col=guide_legend(nrow=2,byrow=TRUE)) -g<-ggarrange(eqtl.g,de.g,de.lung,de.panc,ncol=2,nrow=2,labels=c("A","B","C","D"), +g<-ggarrange(eqtl.g,de.g,de.lung,de.panc,ncol=2,nrow=2,labels=c("a","b","c","d"), align="hv") print(g) ``` -Figure S8: Expression rank priors from cell type sorted bulk studies. Gene expression distribution relative to all genes (i.e. gene expression ranks) gained from cell type sorted bulk studies for A. eQTL studies of PBMCs B. DE studies of PBMCs C. DE studies of lung tissue and D. DE studies of pancreas tissue. The figure shows which percentage of the DE/eQTL genes is expressed for a certain number of expressed genes. +Figure S8: Expression rank priors from cell type sorted bulk studies. Gene expression level of DEGs / eQTL genes (y axis) relative to all genes (x axis) (i.e. gene expression ranks) gained from cell type sorted bulk studies for eQTL studies of PBMCs (a) and for DE studies of PBMCs (b), lung tissue (c) and pancreas tissue (d). ## Figure S9: Effect size priors from cell type sorted bulk studies @@ -1348,7 +1574,7 @@ eqtl.ranks$name<-paste0("Blueprint (",eqtl.ranks$cellType,")") eqtl.g<-ggplot(eqtl.ranks, aes(x=beta, fill=name))+ geom_histogram(position="dodge")+ xlab("Beta values") +ylab("Counts")+ - scale_fill_manual("Study", values=col.set2[5:6])+ + scale_fill_manual("Study", values=col.set.new[1:2])+ theme_bw() + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1362,7 +1588,7 @@ de.ranks$log2FoldChange<-log2(de.ranks$FoldChange) de.g<-ggplot(de.ranks, aes(x=log2FoldChange, fill=name)) + geom_histogram(position="dodge")+ xlab("Log fold change") +ylab("Counts")+ - scale_fill_manual("Study", values=col.set2)+ + scale_fill_manual("Study", values=col.set.new[3:6])+ theme_bw() + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1376,7 +1602,7 @@ de.ranks.lung$log2FoldChange<-log2(de.ranks.lung$FoldChange) de.lung<-ggplot(de.ranks.lung, aes(x=log2FoldChange, fill=name)) + geom_histogram(position="dodge")+ xlab("Log fold change") +ylab("Counts")+ - scale_fill_manual("Study", values=col.set2)+ + scale_fill_manual("Study", values=col.set.new[7])+ theme_bw() + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1390,7 +1616,7 @@ de.ranks.panc$log2FoldChange<-log2(de.ranks.panc$FoldChange) de.panc<-ggplot(de.ranks.panc, aes(x=log2FoldChange, fill=name)) + geom_histogram(position="dodge")+ xlab("Log fold change") +ylab("Counts")+ - scale_fill_manual("Study", values=col.set2)+ + scale_fill_manual("Study", values=col.set.new[8:9])+ theme_bw() + theme(axis.title=element_text(size=10), axis.text=element_text(size=7), @@ -1401,75 +1627,16 @@ de.panc<-ggplot(de.ranks.panc, aes(x=log2FoldChange, fill=name)) + override.aes = list(size = 0.5))) g<-ggarrange(eqtl.g,de.g,de.lung,de.panc,ncol=2,nrow=2, - labels=c("A","B","C","D"),align="hv") + labels=c("a","b","c","d"),align="hv") print(g) ``` -Figure S9: Effect size priors from cell type sorted bulk studies. Effect sizes gained from cell type sorted bulk studies for A. eQTL studies of PBMCs B. DE studies of PBMCs C. DE studies of lung tissue and D. DE studies of pancreas tissue. The effect size is quantified as beta values for eQTL studies and as log fold changes for DE studies. - -## Figure S10: Relation between eQTL power and expression mean in a simulation study - -Figure S10 shows how large the deviation between simulated and analytic mean values is for small mean values, using a range of typical parameter combinations for sample size, effect size (Rsq), mean and Bonferroni adjustetd p-values. - -To speed uo calculations, the plot is only shown for a reduced number of parameter combinations. - -```{r, fig.width=8, fig.height=4} - -#Parameters used for the original plot -# count.mean<-c(seq(1,20),50,100,150) -# nGenes<-c(1000,2000,5000,10000) - -#Reduced parameters used here to accelerate -count.mean<-seq(1,9) -nGenes<-1000 - -#Simulate look-up table of typical parameter combinations -sampleSize<-c(20,50,100,200) -Rsq<-quantile(abs(scPower::eqtl.ref.study$Rsq)) - -#Check also different p-values (all approximating Bonferroni corrected p-values) -pvals<-0.05/(10*nGenes) - -param.combis<-expand.grid(sampleSize,Rsq,count.mean,pvals,KEEP.OUT.ATTRS = FALSE) -colnames(param.combis)<-c("sampleSize","Rsq","count.mean","pvals") - -#Get simulated power -param.combis$power.simulated<-sapply(1:nrow(param.combis),function(i) - scPower:::power.eqtl.simulated(param.combis$count.mean[i], - param.combis$Rsq[i], - param.combis$pvals[i], - param.combis$sampleSize[i])) - -#Get analytic power -param.combis$power.analytic<-sapply(1:nrow(param.combis),function(i) - scPower:::power.eqtl.ftest(param.combis$Rsq[i], - param.combis$pvals[i], - param.combis$sampleSize[i])) - -#Calculate deviation -param.combis$deviation<-param.combis$power.analytic-param.combis$power.simulated - -g.a<-ggplot(param.combis[param.combis$count.mean<=20,], - aes(x=power.simulated,y=power.analytic,color=count.mean))+ - geom_point()+geom_abline()+ - xlab("Power - eQTL simulation") + ylab("Power - scPower")+ - theme_bw()+ - scale_color_viridis("Mean") - -g.b<-ggplot(param.combis,aes(x=as.factor(count.mean),y=deviation))+geom_boxplot()+ - xlab("Deviation of analytic power and simulated power")+xlab("Mean")+ - theme_bw() - -g<-ggarrange(g.a,g.b,ncol=2,labels=c("A","B")) +Figure S9: Effect size priors from cell type sorted bulk studies. Effect sizes gained from cell type sorted bulk studies for eQTL studies of PBMCs (a) and for DE studies of PBMCs (b), lung tissue (c) and pancreas tissue (d). The effect size is quantified as beta values for eQTL studies and as log fold changes for DE studies. -print(g) - -``` - -## Figure S11: Detection power using observed priors from reference studies +## Figure S10: Detection power using observed priors from reference studies -Figure S11 is an extension of Figure 3, showing the detection power for all analysed reference studies with PBMC cell types and matched cell type expression model, while in Figure 3 it is only shown for one DE and one eQTL study. +Figure S10 is an extension of Figure 3a,b, showing the detection power for all analysed reference studies with PBMC cell types and matched cell type expression model, while in Figure 3a,b it is only shown for one DE and one eQTL study. In contrast to the original publication, the parameter useSimulatedPower is set to FALSE for the eQTL budget optimization. Both parameters speed the calculation in the vignette, while changing the overall power only slightly. We show in the publication the more accurate version including simulated eQTL power for lowly expressed genes. @@ -1594,21 +1761,89 @@ g.eQTL<-ggplot(power.eqtl.study,aes(x=totalCells,y=sampleSize,fill=powerDetect)) legend.title=element_text(size=8), legend.text=element_text(size=8)) -g<-ggarrange(g.DE,g.eQTL,ncol=1,nrow=2,heights=c(2,1),labels=c("A","B"), - align="hv",common.legend = TRUE,legend="right") +g<-ggarrange(g.DE,g.eQTL,ncol=1,nrow=2,heights=c(2,1),labels=c("a","b"), + align="hv",common.legend = TRUE,legend="right") + +print(g) + + +``` + +Figure S10: Detection power using observed priors from reference studies. Detection power for DE genes (a) and eQTL genes (b) dependent on the study, total sample size and the number of measured cells per cell type for a transcriptome mapped read depth per cell of 20,000. The detection power is the product of the probability that the gene is expressed and the power to detect it as a DE or eQTL gene, respectively, assuming that it is expressed. The fold change for DE genes and the R2 for eQTL genes is taken from published studies, together with the expression rank of the genes (studies shown in panel titles). The expression profile and expression probabilities in a single cell experiment with a specific number of samples and measured cells was estimated using our expression prior, setting the definition for expressed to > 10 counts in more than 50% of the individuals. Multiple testing correction was performed by using FDR adjusted p-values for DE analysis and FWER adjusted p-values for eQTL analysis. + +## Figure S11: Variant of Main Figure 3 with FWER adjusted significance thresholds + +This figure was generated with exactly the same code as Main Figures 3a,b, only setting the multiple testing method (MTmethod) to "FWER" instead of "FDR" for the DE power. + +## Figure S12: Relation between eQTL power and expression mean in a simulation study + +Figure S12 shows how large the deviation between simulated and analytic mean values is for small mean values, using a range of typical parameter combinations for sample size, effect size (Rsq), mean and Bonferroni adjustetd p-values. + +To speed up calculations, the plot is only shown for a reduced number of parameter combinations in this vignette. + +```{r, fig.width=8, fig.height=4} + +#Parameters used for the original plot +# count.mean<-c(seq(1,20),50,100,150) +# nGenes<-c(1000,2000,5000,10000) + +#Reduced parameters used here to accelerate +count.mean<-seq(1,9) +nGenes<-1000 + +#Simulate look-up table of typical parameter combinations +sampleSize<-c(20,50,100,200) +Rsq<-quantile(abs(scPower::eqtl.ref.study$Rsq)) + +#Check also different p-values (all approximating Bonferroni corrected p-values) +pvals<-0.05/(10*nGenes) + +param.combis<-expand.grid(sampleSize,Rsq,count.mean,pvals,KEEP.OUT.ATTRS = FALSE) +colnames(param.combis)<-c("sampleSize","Rsq","count.mean","pvals") + +#Get simulated power +param.combis$power.simulated<-sapply(1:nrow(param.combis),function(i) + scPower:::power.eqtl.simulated(param.combis$count.mean[i], + param.combis$Rsq[i], + param.combis$pvals[i], + param.combis$sampleSize[i])) + +#Get analytic power +param.combis$power.analytic<-sapply(1:nrow(param.combis),function(i) + scPower:::power.eqtl.ftest(param.combis$Rsq[i], + param.combis$pvals[i], + param.combis$sampleSize[i])) + +#Calculate deviation +param.combis$deviation<-param.combis$power.analytic-param.combis$power.simulated + +g.a<-ggplot(param.combis[param.combis$count.mean<=20,], + aes(x=power.simulated,y=power.analytic,color=count.mean))+ + geom_point()+geom_abline()+ + xlab("Power - eQTL simulation") + ylab("Power - scPower")+ + theme_bw()+ + scale_color_viridis("Mean") + +g.b<-ggplot(param.combis,aes(x=as.factor(count.mean),y=deviation))+geom_boxplot()+ + xlab("Deviation of analytic power and simulated power")+xlab("Mean")+ + theme_bw() -print(g) +g<-ggarrange(g.a,g.b,ncol=2,labels=c("a","b")) +print(g) ``` -Figure S11: Detection power using observed priors from reference studies. Detection power for A. DE genes and B. eQTL genes dependent on the study, total sample size and the number of measured cells per cell type for a transcriptome mapped read depth per cell of 20,000. The detection power is the product of the probability that the gene is expressed and the power to detect it as a DE or eQTL gene, respectively, assuming that it is expressed. The fold change for DE genes and the R^2 for eQTL genes is taken from the published study, together with the expression rank of the genes. The expression profile in a single cell experiment with a specific number of samples and measured cells is estimated using our gamma mixed models. +Figure S12: Relation between eQTL power and expression mean in a simulation study. -## Figure S12: Variant of Main Figure 3 with FWER adjusted significance thresholds +a The simulated eQTL power is compared to the analytic power calculated with scPower for a range of effect sizes (R2 between 0.1 and 0.6), sample sizes (between 20 and 200) and Bonferroni-corrected p-values (here only 0.05/(10\*1,000) to speed calculations). The color coding shows the mean count value. -This figure was generated with exactly the same code as Main Figures 3, only setting the multiple testing method (MTmethod) to "FWER" instead of "FDR" for the DE power. +b The deviation between the analytic power and the simulated power is stratified by the expression mean used in the simulation. Boxplots show medians (centre lines), first and third quartiles (lower and upper box limits, respectively), 1.5-fold interquartile ranges (whisker extents) and outliers (black circles). -## Figure S13: Comparison of powsimR in combination with different DE methods and scPower +## Figure S13: Comparison of scPower with the simulation-based methods powsimR and muscat in combination with different DE methods. + +Extension of Figure 3c-e: more extensive comparison between scPower and simulation-based methods +combining the simulation with different DE methods (in Figure 3 only showing the results for edgeR). ```{r, fig.width=8,fig.height=8} @@ -1681,6 +1916,12 @@ colnames(overview.results)<-colnames(power.fwer) power.fwer<-rbind(power.fwer,overview.results) power.fwer$totalCells<-as.factor(power.fwer$totalCells) +#Format all methods as factors so that points are sorted correctly +method_ordering<-sort(unique(power.fwer$method)) +#switch limma-voom and trend to match the color conding better +method_ordering<-method_ordering[c(1,2,3,4,6,7,5,8)] +power.fwer$method<-factor(power.fwer$method,levels=method_ordering) + g.a<-ggplot(power.fwer,aes(x=totalCells,y=expressed.genes,fill=method))+ geom_bar(stat="identity",position="dodge")+ geom_errorbar(aes(ymin=expressed.genes-expressed.genes.sd, @@ -1689,7 +1930,7 @@ g.a<-ggplot(power.fwer,aes(x=totalCells,y=expressed.genes,fill=method))+ ylab("Expressed genes")+xlab("Cells per person")+ facet_wrap(~sampleSize)+ theme_bw()+ - scale_fill_manual("Method",values=col.set2) + scale_fill_manual("Method",values=method.colors) g.b<-ggplot(data=power.fwer,aes(x=totalCells,y=power,fill=method))+ geom_bar(stat="identity",position="dodge")+ @@ -1698,7 +1939,7 @@ g.b<-ggplot(data=power.fwer,aes(x=totalCells,y=power,fill=method))+ ylab("DE power")+xlab("Cells per person")+ facet_wrap(~sampleSize)+ylim(0,1)+ theme_bw()+ - scale_fill_manual("Method",values=col.set2) + scale_fill_manual("Method",values=method.colors) g.c<-ggplot(data=power.fwer,aes(x=totalCells,y=overall.power,fill=method))+ geom_bar(stat="identity",position="dodge")+ @@ -1708,7 +1949,7 @@ g.c<-ggplot(data=power.fwer,aes(x=totalCells,y=overall.power,fill=method))+ ylab("Overall detection power")+xlab("Cells per person")+ facet_wrap(~sampleSize)+ylim(0,1)+ theme_bw()+ - scale_fill_manual("Method",values=col.set2) + scale_fill_manual("Method",values=method.colors) ###################################################################### #Test results for each parameter combination with MT method FDR @@ -1761,6 +2002,9 @@ colnames(overview.results)<-colnames(power.fdr) power.fdr<-rbind(power.fdr,overview.results) power.fdr$totalCells<-as.factor(power.fdr$totalCells) +#Format all methods as factors so that points are sorted correctly +power.fdr$method<-factor(power.fdr$method,levels=method_ordering) + g.d<-ggplot(power.fdr,aes(x=totalCells,y=expressed.genes,fill=method))+ geom_bar(stat="identity",position="dodge")+ geom_errorbar(aes(ymin=expressed.genes-expressed.genes.sd, @@ -1769,375 +2013,153 @@ g.d<-ggplot(power.fdr,aes(x=totalCells,y=expressed.genes,fill=method))+ ylab("Expressed genes")+xlab("Cells per person")+ facet_wrap(~sampleSize)+ theme_bw()+ - scale_fill_manual("Method",values=col.set2) + scale_fill_manual("Method",values=method.colors) g.e<-ggplot(data=power.fdr,aes(x=totalCells,y=power,fill=method))+ geom_bar(stat="identity",position="dodge")+ - geom_errorbar(aes(ymin=power-power.sd, ymax=power+power.sd), width=.2, - position=position_dodge(.9))+ - ylab("DE power")+xlab("Cells per person")+ - facet_wrap(~sampleSize)+ylim(0,1)+ - theme_bw()+ - scale_fill_manual("Method",values=col.set2) - -g.f<-ggplot(data=power.fdr,aes(x=totalCells,y=overall.power,fill=method))+ - geom_bar(stat="identity",position="dodge")+ - geom_errorbar(aes(ymin=overall.power-overall.power.sd, - ymax=overall.power+overall.power.sd), width=.2, - position=position_dodge(.9))+ - ylab("Overall detection power")+xlab("Cells per person")+ - facet_wrap(~sampleSize)+ylim(0,1)+ - theme_bw()+ - scale_fill_manual("Method",values=col.set2) - -g<-ggarrange(g.a,g.d, - g.b,g.e, - g.c,g.f, - ncol=2,nrow=3, - labels=c("A","B","C","D","E","F"), - common.legend=TRUE, - legend="bottom",align="hv") - -print(g) - -``` - - -## Figure S14: Comparison between powsimR and scPower over a large range of experimental design - -Figure S14 extends the comparison between powsimR and scPower for a large range of parameter combinations and only evaluating it for powsimR in combination with edgeR. The used parameter combinations are: - -```{r} -cellsPerCelltype<-c(100,200,500,1000,1500,2000,2500,3000) -sampleSize<-c(4,6,8,10,16,20) -``` - -The final plots (restricted for the parameter combinations generated in Figure S13) are generated as: - -```{r, fig.width=10,fig.height=6} - -#Create plot showing reproduction of main figure 3 - -#FWER corrected threshold -power.fwer.edgeR<-power.fwer[power.fwer$method == "edgeR-LRT",] -power.fwer.scpower<-power.fwer[power.fwer$method == "scPower",] -power.fwer<-merge(power.fwer.edgeR,power.fwer.scpower, - by=c("sampleSize","totalCells"), - suffixes=c("",".scpower")) - -g.a<-ggplot(power.fwer,aes(x=expressed.genes,y=expressed.genes.scpower))+ - geom_point()+geom_abline()+geom_hline(yintercept = 16171)+ - xlab("Expressed genes - powsimR")+ylab("Expressed genes - scPower")+ - theme_bw() - -g.b<-ggplot(power.fwer,aes(x=power,y=power.scpower))+ - geom_point()+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ - xlab("DE power - powsimR")+ylab("DE power - scPower")+ - theme_bw() - -g.c<-ggplot(power.fwer,aes(x=overall.power,y=overall.power.scpower))+ - geom_point()+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ - xlab("Overall power - powsimR")+ - ylab("Overall power - scPower")+ - theme_bw() - -#FDR corrected thresold -power.fdr.edgeR<-power.fdr[power.fdr$method == "edgeR-LRT",] -power.fdr.scpower<-power.fdr[power.fdr$method == "scPower",] -power.fdr<-merge(power.fdr.edgeR,power.fdr.scpower, - by=c("sampleSize","totalCells"), - suffixes=c("",".scpower")) - -g.d<-ggplot(power.fdr,aes(x=expressed.genes,y=expressed.genes.scpower))+ - geom_point()+geom_abline()+geom_hline(yintercept = 16171)+ - xlab("Expressed genes - powsimR")+ylab("Expressed genes - scPower")+ - theme_bw() - -g.e<-ggplot(power.fdr,aes(x=power,y=power.scpower))+ - geom_point()+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ - xlab("DE power - powsimR")+ylab("DE power - scPower")+ - theme_bw() - -g.f<-ggplot(power.fdr,aes(x=overall.power,y=overall.power.scpower))+ - geom_point()+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ - xlab("Overall power - powsimR")+ - ylab("Overall power - scPower")+ - theme_bw() - -g<-ggarrange(g.a,g.b,g.c, - g.d,g.e,g.f, - ncol=3,nrow=2, - labels=c("A","B","C","D","E","F"),align="hv") - -print(g) - -``` - - -## Figure S15: Parameter optimization for constant budget - -Figure S15 shows two example calculations on how scPower selects the optimal experimental parameter combination for a specific budget, once for a DE study and once for an eQTL study. For an explanation of all the used parameters, please look into the introduction vignette. - -In contrast to the original publication, the parameter useSimulatedPower is set to FALSE and the parameter speedPowerCalc to TRUE for the eQTL budget optimization. Both parameters speed the calculation in the vignette, while changing the overall power only slightly. We show in the publication the more accurate version including simulated eQTL power for lowly expressed genes. - -```{r,fig.width=8,fig.height=8} - -costKit<-5600 -costFlowCell<-14032 -readsPerFlowcell<-4100*10^6 -cellsPerLane<-20000 -ct<-"CD4 T cells" -ct.freq<-0.25 -mappingEfficiency<-0.8 -######## -# DE subplot - -comp.type<-"de" -ref.study.name<-"Blueprint (CLL) iCLL-mCLL" -readDepth<-c(2000,5000,10000,15000,20000,30000,50000,70000) -cellsPerIndividual<-c(100,200,500,700,seq(1000,12000,500)) -totalBudget<-10000 - -power.study.de<-optimize.constant.budget.restrictedDoublets(totalBudget, - comp.type,ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - de.ref.study,ref.study.name, - cellsPerLane, - read.umi.fit[ - read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange=cellsPerIndividual, - readDepthRange=readDepth, - mappingEfficiency=mappingEfficiency, - sign.threshold = 0.05, - MTmethod="FDR") - -#Print maximal combination of values -max.study<-power.study.de[which.max(power.study.de$powerDetect),] - -#Replace subsampling -colnames(power.study.de)[2:4]<-c("Detection power","Expression probability", - "DE power") - -#Show two-dimensional plots with always one parameter fixed -#Fixed read depths -power.study.plot<-power.study.de[power.study.de$readDepth==max.study$readDepth,] -power.study.plot<-reshape2::melt(power.study.plot, - id.vars=c("name","sampleSize","totalCells", - "usableCells","multipletFraction", - "ctCells","readDepth","readDepthSinglet", - "mappedReadDepth","expressedGenes")) -power.study.plot$variable<-factor(power.study.plot$variable, - levels=c("Expression probability","DE power", - "Detection power")) - -g.rd<-ggplot(power.study.plot,aes(totalCells,value,color=variable))+geom_line() -labels<-as.numeric(ggplot_build(g.rd)$layout$panel_params[[1]]$x.labels) - -g.rd<-g.rd + - scale_x_continuous("Cells per individual",sec.axis=sec_axis(~., breaks=labels, - labels=sapply(labels,function(c)floor( - sampleSizeBudgetCalculation.restrictedDoublets(c,max.study$readDepth, - totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), - name="Sample size"))+ - geom_vline(xintercept = max.study$totalCells)+ - ylab("Probability")+ - scale_color_manual("",values=col.set)+ - ylim(0,1)+ - theme_bw() + - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.position=c(0.85,0.3), - legend.title=element_blank(), - legend.text=element_text(size=8)) - -#Fixed number of cells -power.study.plot<-power.study.de[power.study.de$totalCells==max.study$totalCells,] -power.study.plot<-reshape2::melt(power.study.plot, - id.vars=c("name","sampleSize","totalCells", - "usableCells","multipletFraction", - "ctCells","readDepth","readDepthSinglet", - "mappedReadDepth","expressedGenes")) -power.study.plot$variable<-factor(power.study.plot$variable, - levels=c("Expression probability","DE power", - "Detection power")) - -g.cp<-ggplot(power.study.plot,aes(readDepth,value,color=variable))+geom_line() -labels<-as.numeric(ggplot_build(g.cp)$layout$panel_params[[1]]$x.labels) - -g.cp <- g.cp + scale_x_continuous("Read depth",sec.axis=sec_axis(~., breaks=labels, - labels=sapply(labels,function(c)floor( - sampleSizeBudgetCalculation.restrictedDoublets(max.study$totalCells, - c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ - geom_vline(xintercept = max.study$readDepth)+ - ylab("Probability")+ - scale_color_manual("",values=col.set)+ - ylim(0,1)+ - theme_bw() + - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.position=c(0.85,0.3), - legend.title=element_blank(), - legend.text=element_text(size=8)) - -g.a<-ggarrange(g.rd,g.cp,ncol=2,nrow=1,labels=c("A","B"), - align="hv",common.legend=TRUE,legend="bottom") - -######## -# eQTL subplot - -comp.type<-"eqtl" -ref.study.name<-"Blueprint (Tcells)" -readDepth<-c(2000,5000,10000,15000,20000,30000,50000) -cellsPerIndividual<-c(100,200,500,700,seq(1000,9000,500)) -totalBudget<-30000 - -power.study.eqtl<-optimize.constant.budget.restrictedDoublets(totalBudget, - comp.type,ct,ct.freq, - costKit,costFlowCell,readsPerFlowcell, - eqtl.ref.study,ref.study.name, - cellsPerLane, - read.umi.fit[ - read.umi.fit$type=="10X_PBMC_1",], - gamma.mixed.fits, - disp.fun.param, - nCellsRange=cellsPerIndividual, - readDepthRange=readDepth, - mappingEfficiency=mappingEfficiency, - sign.threshold = 0.05, - MTmethod="Bonferroni", - #These two parameters are set differently - #in the publictions, but changed here to - #speed the calculation - useSimulatedPower = FALSE, #(in publication: TRUE) - speedPowerCalc = TRUE) #(in publication: FALSE) - -#Print maximal combination of values -max.study<-power.study.eqtl[which.max(power.study.eqtl$powerDetect),] - -#Replace subsampling -colnames(power.study.eqtl)[2:4]<-c("Detection power","Expression probability", - "eQTL power") - -#Show two-dimensional plots with always one parameter fixed -#Fixed read depths -power.study.plot<-power.study.eqtl[power.study.eqtl$readDepth==max.study$readDepth,] -power.study.plot<-reshape2::melt(power.study.plot, - id.vars=c("name","sampleSize","totalCells", - "usableCells","multipletFraction", - "ctCells","readDepth","readDepthSinglet", - "mappedReadDepth","expressedGenes")) -power.study.plot$variable<-factor(power.study.plot$variable, - levels=c("Expression probability","eQTL power", - "Detection power")) - -g.rd<-ggplot(power.study.plot,aes(totalCells,value,color=variable))+geom_line() -labels<-as.numeric(ggplot_build(g.rd)$layout$panel_params[[1]]$x.labels) - -g.rd<-g.rd + - scale_x_continuous("Cells per individual",sec.axis=sec_axis(~., breaks=labels, - labels=sapply(labels,function(c)floor(sampleSizeBudgetCalculation.restrictedDoublets( - max.study$totalCells, - c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ - geom_vline(xintercept = max.study$totalCells)+ - ylab("Probability")+ - scale_color_manual("",values=col.set)+ - ylim(0,1)+ - theme_bw() + - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.position=c(0.85,0.3), - legend.title=element_blank(), - legend.text=element_text(size=8)) + geom_errorbar(aes(ymin=power-power.sd, ymax=power+power.sd), width=.2, + position=position_dodge(.9))+ + ylab("DE power")+xlab("Cells per person")+ + facet_wrap(~sampleSize)+ylim(0,1)+ + theme_bw()+ + scale_fill_manual("Method",values=method.colors) -#Fixed number of cells -power.study.plot<-power.study.eqtl[power.study.eqtl$totalCells==max.study$totalCells,] -power.study.plot<-reshape2::melt(power.study.plot, - id.vars=c("name","sampleSize","totalCells", - "usableCells","multipletFraction", - "ctCells","readDepth","readDepthSinglet", - "mappedReadDepth","expressedGenes")) -power.study.plot$variable<-factor(power.study.plot$variable, - levels=c("Expression probability","eQTL power", - "Detection power")) +g.f<-ggplot(data=power.fdr,aes(x=totalCells,y=overall.power,fill=method))+ + geom_bar(stat="identity",position="dodge")+ + geom_errorbar(aes(ymin=overall.power-overall.power.sd, + ymax=overall.power+overall.power.sd), width=.2, + position=position_dodge(.9))+ + ylab("Overall detection power")+xlab("Cells per person")+ + facet_wrap(~sampleSize)+ylim(0,1)+ + theme_bw()+ + scale_fill_manual("Method",values=method.colors) -g.cp<-ggplot(power.study.plot,aes(readDepth,value,color=variable))+geom_line() -labels<-as.numeric(ggplot_build(g.cp)$layout$panel_params[[1]]$x.labels) +g<-ggarrange(g.a,g.d, + g.b,g.e, + g.c,g.f, + ncol=2,nrow=3, + labels=c("a","b","c","d","e","f"), + common.legend=TRUE, + legend="bottom",align="hv") -g.cp <- g.cp + scale_x_continuous("Read depth",sec.axis=sec_axis(~., breaks=labels, - labels=sapply(labels,function(c)floor(sampleSizeBudgetCalculation.restrictedDoublets( - max.study$totalCells, - c,totalBudget, costKit, cellsPerLane, costFlowCell,readsPerFlowcell))), name="Sample size"))+ - geom_vline(xintercept = max.study$readDepth)+ - scale_color_manual("",values=col.set)+ - ylab("Probability")+ - ylim(0,1)+ - theme_bw() + - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.position=c(0.85,0.3), - legend.title=element_blank(), - legend.text=element_text(size=8)) +print(g) + +``` -g.b<-ggarrange(g.rd,g.cp,ncol=2,nrow=1,labels=c("C","D"), - align="hv",common.legend=TRUE,legend="bottom") +Figure S13: Comparison of scPower with the simulation-based methods powsimR and muscat in combination with different DE methods. Difference in number of expressed genes (a,b), DE power (c,d) and overall detection power (e,f) between scPower and simulations. The adapted version of powsimR (see methods) was run with DESeq2, edgeR-LRT and limma-voom, using the mean-ratio method (MR) for normalization. The adapted version of muscat (see methods) was run with DESeq2, edgeR, limma-trend and limma-voom. The power was evaluated for sample sizes of 4, 8 and 16 (see panel titles) and for 200, 1000 and 3000 cells per person (x axis). Both FWER adjusted power (a,c,e) and FDR adjusted power (b,d,f) were evaluated. The barplots represent the mean power over n=25 simulation runs of powsimR and muscat and the error bar shows the standard deviation. -g<-ggarrange(g.a,g.b,ncol=1,nrow=2) +## Figure S14: Comparison between scPower with the simulation-based methods powsimR and muscat over a large range of experimental design -print(g) +Extensing of Figure 3c-e: showing the comparison of scPower with the simulation-based methods also for FWER correction. +```{r} +cellsPerCelltype<-c(100,200,500,1000,1500,2000,2500,3000) +sampleSize<-c(4,6,8,10,16,20) ``` -Figure S15: Parameter optimization for constant budget. Optimized parameters (sample size, read depth and cells per individual) to maximize the detection power for a DE study with a budget of 10,000€ (A,B) and an eQTL study with a budget of 30,000€ (D,E), both for a cell type with a frequency of 25%. Suplots A and C show the influence of the cells per individual given the optimized read depth, subplots B and D show the influence of the cells per individual given the optimized number of cells per individual. The third parameter, the sample size, is defined uniquely given the other two parameters due to the budget restriction. The optimal sample size values are shown in the upper x axes (no linear scale, but matching numbers to the parameter on the lower y axis under the given budget). The horizontal line in the subplots visualizes the optimal parameter combination. The same effect sizes and expression definition as in Figure 3 was taken. +The final plots (restricted for the parameter combinations generated in Figure S13) are generated as: -## Figure S16: Parameter optimization grid +```{r, fig.width=10,fig.height=6} -Figure S16 shows the parameter grids for the calculations done for Figure S15. +#Create plot showing reproduction of main figure 3 -```{r,fig.width=8} +#FWER corrected threshold +power.fwer$method<-as.character(power.fwer$method) +power.fwer.edgeR<-power.fwer[startsWith(power.fwer$method,"edgeR"),] +power.fwer.scpower<-power.fwer[power.fwer$method == "scPower",] +power.fwer<-merge(power.fwer.edgeR,power.fwer.scpower, + by=c("sampleSize","totalCells"), + suffixes=c("",".scpower")) +power.fwer$variable<-ifelse(endsWith(power.fwer$method,"powsimR"), + "powsimR","muscat") +#Reorder variable for plotting +power.fwer$variable<-factor(power.fwer$variable,levels=c("powsimR","muscat")) + +g.a<-ggplot(power.fwer,aes(x=expressed.genes,y=expressed.genes.scpower, + color=variable))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + geom_point(alpha=0.7)+geom_abline()+geom_hline(yintercept = 16171)+ + xlab("Expressed genes - simulations")+ylab("Expressed genes - scPower")+ + theme_bw() -power.study.plot<-power.study.de -power.study.plot$totalCells<-as.factor(power.study.plot$totalCells) -power.study.plot$readDepth<-as.factor(power.study.plot$readDepth) -g.de<-ggplot(power.study.plot,aes(x=readDepth,y=totalCells,fill=`Detection power`))+ - geom_tile()+ylab("Cells per individual")+xlab("Read depth")+ - scale_fill_viridis("Detection power")+ - theme_bw()+ - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.title=element_text(size=8), - legend.text=element_text(size=8)) +g.b<-ggplot(power.fwer,aes(x=power,y=power.scpower, + color=variable))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + xlab("DE power - simulations")+ylab("DE power - scPower")+ + theme_bw() + +g.c<-ggplot(power.fwer,aes(x=overall.power,y=overall.power.scpower, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("Overall power - simulations")+ + ylab("Overall power - scPower")+ + theme_bw() -power.study.plot<-power.study.eqtl -power.study.plot$totalCells<-as.factor(power.study.plot$totalCells) -power.study.plot$readDepth<-as.factor(power.study.plot$readDepth) -g.eqtl<-ggplot(power.study.plot,aes(x=readDepth,y=totalCells,fill=`Detection power`))+ - geom_tile()+ylab("Cells per individual")+xlab("Read depth")+ - scale_fill_viridis("Detection power")+ - theme_bw()+ - theme(axis.title=element_text(size=10), - axis.text=element_text(size=8), - legend.title=element_text(size=8), - legend.text=element_text(size=8)) +#FDR corrected thresold +power.fdr$method<-as.character(power.fdr$method) +power.fdr.edgeR<-power.fdr[startsWith(power.fdr$method,"edgeR"),] +power.fdr.scpower<-power.fdr[power.fdr$method == "scPower",] +power.fdr<-merge(power.fdr.edgeR,power.fdr.scpower, + by=c("sampleSize","totalCells"), + suffixes=c("",".scpower")) +power.fdr$variable<-ifelse(endsWith(power.fdr$method,"powsimR"), + "powsimR","muscat") +#Reorder variable for plotting +power.fdr$variable<-factor(power.fdr$variable,levels=c("powsimR","muscat")) + +g.d<-ggplot(power.fdr,aes(x=expressed.genes,y=expressed.genes.scpower, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+geom_hline(yintercept = 16171)+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("Expressed genes - simulations")+ + ylab("Expressed genes - scPower")+ + theme_bw() + +g.e<-ggplot(power.fdr,aes(x=power,y=power.scpower, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("DE power - simulations")+ + ylab("DE power - scPower")+ + theme_bw() + +g.f<-ggplot(power.fdr,aes(x=overall.power,y=overall.power.scpower, + color=variable))+ + geom_point(alpha=0.7)+geom_abline()+ylim(c(0,1))+xlim(c(0,1))+ + scale_color_discrete("Simulation method",labels=c("powsimR","muscat"))+ + xlab("Overall power - simulations")+ + ylab("Overall power - scPower")+ + theme_bw() + +g<-ggarrange(g.a,g.b,g.c, + g.d,g.e,g.f, + ncol=3,nrow=2, + labels=c("a","b","c","d","e","f"),align="hv", + common.legend = TRUE,legend="bottom") -g<-ggarrange(g.de,g.eqtl,ncol=2,nrow=1, labels=c("A","B"), - common.legend = TRUE, legend="right") print(g) ``` -Figure S16: Parameter optimization grid. Maximizing detection power by selecting the best combination of cells per individual and read depth for A. a DE study with a budget of 10,000€ and B. a eQTL study with a budget of 30,000€, both for a cell type with a frequency of 25%. The sample size is defined uniquely given the other two parameters due to the budget restriction. The same effect sizes and expression definition as in Figure 3 were taken. +Figure S14: Comparison between scPower with the simulation-based methods powsimR and muscat over a large range of experimental design. Difference in number of expressed genes (a,d), DE power (b,e) and overall detection power (c,f) between scPower (y axis) and simulations of powsimR and muscat (x axis). The adapted version of powsimR (see Methods) was run with edgeR-LRT using the mean-ratio method (MR) for normalization and the adapted version of muscat with edgeR. The power was evaluated for all combinations of main Figure 3 (sample sizes of 4, 6, 8, 16 and 20 and cells per person of 100, 200, 500, 1000, 1500, 2000, 2500 and 3000). Both FWER adjusted p-values (a-c) and FDR adjusted p-values (d-f) were evaluated. In (a) and (d), the horizontal line represents the maximum number of possible simulated genes for powsimR . + +## Figure S15&S16: Comparison between scPower with the simulation-based methods with specific simulation setting (batch effects and imbalanced cell proportions) -## Figure S17-S18: Variant of Main Figure 4 & 5 with FWER adjusted significance thresholds +In Figure S15, we evaluated the influence of batch effects using powsimR, and respectively, +in Figure S16, the influence of imbalanced cell proportions between the DE groups using muscat. In both cases, the parameters of the simulation tools were set correspondingly, +but the comparison with scPower including the plotting is analogously done as in Figure S13. -These two figures were generated with exactly the same code as Main Figures 4 and 5, only setting the multiple testing method (MTmethod) to "FWER" instead of "FDR" for the DE power. +## Figure S17: Variant of Main Figure 5 with FWER adjusted significance thresholds -## Figure S19: Gene curve fits for different single cell technologies +This figure was generated with exactly the same code as Main Figures 5, only setting the multiple testing method (MTmethod) to "FWER" instead of "FDR" for the DE power. + +## Figure S18: Gene curve fits for different single cell technologies The figure shows the gene expression model for a Drop-seq lung and a Smart-seq pancreas data set, corresponding to the models shown Figure 2, S5 and S6 for a 10X Genomics PBMC data set. -```{r, fig.width=8,fig.height=6} +```{r, fig.width=8,fig.height=4} #Load observed gene counts (precalculated) data(precalculatedObservedGeneCounts) #observed.gene.counts @@ -2193,6 +2215,7 @@ g.drop<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ scale_fill_manual(values=col.set2)+ scale_shape_manual("Cell type",values=seq(1,13))+ scale_color_manual("Read depth",values=col.set2)+ + scale_linetype_discrete(name="Curve",labels=c("Observed","Estimated")) + theme_bw() + theme(axis.title=element_text(size=12), axis.text=element_text(size=8), @@ -2201,7 +2224,7 @@ g.drop<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ aspect.ratio = 0.8, legend.direction = "vertical", legend.box = "horizontal", legend.position="bottom")+ - guides(linetype=FALSE,fill=FALSE,shape=guide_legend(ncol=3)) + guides(fill=FALSE,shape=guide_legend(ncol=3)) ################ #Smart-seq data @@ -2231,121 +2254,398 @@ for(i in 1:nrow(estimates.allSamples)){ } -plot.estimates<-reshape2::melt(estimates.allSamples, - id.vars=c("run","sample","cell.type","num.cells", - "meanUMI","evaluation")) +plot.estimates<-reshape2::melt(estimates.allSamples, + id.vars=c("run","sample","cell.type","num.cells", + "meanUMI","evaluation")) + +#Replace subsampling +subsampled.names<-setNames(c("500000","375000","250000","125000"), + c("complete","subsampling0.75","subsampling0.5", + "subsampling0.25")) +plot.estimates$sample<-as.character(plot.estimates$sample) +plot.estimates$sample<-subsampled.names[plot.estimates$sample] +plot.estimates$sample<-factor(plot.estimates$sample, + levels=c("500000","375000","250000","125000")) + +#Order variable level, first expressed.genes than estimated +plot.estimates$variable<-factor(plot.estimates$variable, + levels=c("expressed.genes","estimated.counts")) + +g.smart<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ + geom_line(aes(color=sample,linetype=variable))+ + geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ + xlab("Number of cells per cell type")+ + ylab("Expressed genes")+ + scale_fill_manual(values=col.set2)+ + scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ + scale_color_manual("Read depth",values=col.set2)+ + scale_linetype_discrete(name="Curve",labels=c("Observed","Estimated")) + + theme_bw() + + theme(axis.title=element_text(size=12), + axis.text=element_text(size=8), + legend.title=element_text(size=6), + legend.text=element_text(size=6), + aspect.ratio = 0.8, + legend.direction = "vertical", legend.box = "horizontal", + legend.position="bottom")+ + guides(fill=FALSE) + +##### +#Combine both +g<-ggarrange(g.drop,g.smart,ncol=2,labels=c("a","b"),align="hv") + +print(g) + +``` + +Figure S18: Gene curve fits for different single cell technologies. Evaluation of the expression probability from scPower for a lung data set measured with Drop-seq (a) and for a pancreas data set measured with Smart-seq2 (b), both subsampled to different read depths (represented by line color). The solid lines represent the observed gene curves, the dashed lines the fitted curves. The point symbol visualizes the cell type. Gene expression criteria are chosen as UMI counts > 10 in all cells for Drop-seq (a) and read counts > 10 per kilobase transcript in all cells for Smart-Seq2 (b). + +## Figure S19: Comparison between powsimR and scPower for Drop-Seq and Smart-seq + +Same as in Figure S14, only run for Drop-Seq and Smart-Seq data. + +## Figure S20: Optimal parameters for varying budgets and Drop-seq and Smart-seq data + +The same evaluation is done in Figure 5 than in Figure 4, only this time with priors of Drop-seq lung data and Smart-seq pancreas data instead of 10X Genomics PBMC data. Similar to Figure 4, the code for the calculation is given, but set to eval=FALSE due to long runtime. Instead, precalculated values are taken for the plotting. + +```{r} +#Drop-seq parameters + +#Cell type +ct<-"NK cell" +ct.freq<-0.2 + +cellsPerChanel<-NA +libPrepCell<-0.09 +costFlowCell<-14032 +readsPerFlowcell<-4100*10^6 + +readDepth<-c(2000,5000,10000,15000,20000,30000,40000,50000,60000,70000,100000) +cellsPerPerson<-c(100,200,500,700,seq(1000,9000,500)) + +mapping.efficiency<-0.8 +nFittedGenes<-24000 + +#Model growth rate constant +multipletRateGrowth<-"constant" +multipletRate<-0.05 + +#Number of DE genes to be modeled +numGenes<-250 + +``` + +```{r, eval=FALSE} + +##### +#Simulated DE studies - lung (drop-seq) + +simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), + rank.max=c(20000,20000,10000,10000), + name=c("highES_unifRank","lowES_unifRank", + "highES_highRank","lowES_highRank")) + +print(simulated.parameters) + +de.raw.values.lung<-NULL +for(s in 1:nrow(simulated.parameters)){ + + print(s) + + #Simulate prior for expression rank + ranks<-uniform.ranks.interval(start=1,end=simulated.parameters$rank.max[s], + numGenes=numGenes) + + #Simulate prior for effect sizes + set.seed(1) + effectSizes<-effectSize.DE.simulation(mean=simulated.parameters$mean.value[s], + sd=1, numGenes=numGenes) + + sample.DE.genes<-data.frame(ranks=ranks, FoldChange=effectSizes, + name="Simulated") + + max.values<-NULL + for(i in budget){ + + power.study<-optimize.constant.budget.libPrepCell(i,"de",ct,ct.freq, + libPrepCell,costFlowCell,readsPerFlowcell, + sample.DE.genes,"Simulated", + cellsPerChanel,read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits.drop, + disp.fun.param.drop, + nCellsRange = cellsPerPerson, + readDepthRange = readDepth, + mappingEfficiency = mapping.efficiency, + multipletRate = multipletRate, + min.UMI.counts = minUMI, + perc.indiv.expr = perc.indiv, + nGenes=nFittedGenes, + multipletRateGrowth = multipletRateGrowth, + MTmethod = "FDR", + speedPowerCalc=TRUE) + + max.values<-rbind(max.values, + power.study[which.max(power.study$powerDetect),]) + } + max.values$budget<-budget + max.values$parameters<-simulated.parameters$name[s] + + de.raw.values.lung<-rbind(de.raw.values.lung,max.values) +} + +##### +#Observed DE studies - lung (drop-seq) + +de.priors.lung<-de.ref.study[de.ref.study$type=="lung",] + +de.real.values.lung<-NULL +for(studyName in unique(de.priors.lung$name)){ + + max.values<-NULL + for(i in budget){ + + power.study<-optimize.constant.budget.libPrepCell(i,"de",ct,ct.freq, + libPrepCell,costFlowCell,readsPerFlowcell, + de.priors.lung,studyName, + cellsPerChanel,read.umi.fit[ + read.umi.fit$type=="10X_PBMC_1",], + gamma.mixed.fits.drop, + disp.fun.param.drop, + nCellsRange = cellsPerPerson, + readDepthRange = readDepth, + mappingEfficiency = mapping.efficiency, + multipletRate = multipletRate, + min.UMI.counts = minUMI, perc.indiv.expr = perc.indiv, + nGenes=nFittedGenes, + multipletRateGrowth = multipletRateGrowth, + MTmethod = "FDR", + speedPowerCalc=TRUE) + + max.values<-rbind(max.values, + power.study[which.max(power.study$powerDetect),]) + } + + max.values$budget<-budget + max.values$parameters<-studyName + de.real.values.lung<-rbind(de.real.values.lung,max.values) +} + +``` -#Replace subsampling -subsampled.names<-setNames(c("500000","375000","250000","125000"), - c("complete","subsampling0.75","subsampling0.5", - "subsampling0.25")) -plot.estimates$sample<-as.character(plot.estimates$sample) -plot.estimates$sample<-subsampled.names[plot.estimates$sample] -plot.estimates$sample<-factor(plot.estimates$sample, - levels=c("500000","375000","250000","125000")) +```{r} -#Order variable level, first expressed.genes than estimated -plot.estimates$variable<-factor(plot.estimates$variable, - levels=c("expressed.genes","estimated.counts")) +#Smart-seq parameters -g.smart<-ggplot(data=plot.estimates,aes(x=num.cells,y=value))+ - geom_line(aes(color=sample,linetype=variable))+ - geom_point(aes(shape=cell.type,color=sample,fill=sample),size=2)+ - xlab("Number of cells per cell type")+ - ylab("Expressed genes")+ - scale_fill_manual(values=col.set2)+ - scale_shape_manual("Cell type",values=c(21,22,23,24,25,8,4))+ - scale_color_manual("Read depth",values=col.set2)+ - theme_bw() + - theme(axis.title=element_text(size=12), - axis.text=element_text(size=8), - legend.title=element_text(size=6), - legend.text=element_text(size=6), - aspect.ratio = 0.8, - legend.direction = "vertical", legend.box = "horizontal", - legend.position="bottom")+ - guides(linetype=FALSE,fill=FALSE) +#Parameters +readDepth.smart<-c(20000,50000,seq(100000,1000000,100000),1500000,2000000) +cellsPerPerson.smart<-c(50,100,200,500,700,seq(1000,9000,500)) -##### -#Combine both -g<-ggarrange(g.drop,g.smart,ncol=2,labels=c("A","B"),align="hv") +mapping.efficiency<-0.8 +min.norm.count<-minUMI +perc.indiv.expr<-perc.indiv -print(g) +ct<-"alpha" +ct.freq<-0.2 -``` +prepCostCell<-13 +costFlowCell<-14032 +readsPerFlowcell<-4100*10^6 -Figure S19: Gene curve fits for different single cell technologies. Plot A shows the gene curve fit for a lung data set measured with Drop-seq and plot B for a pancreas data set measured with Smart-seq2, both subsampled to different read depths. The solid lines represent the observed gene curves, the dashed lines the fitted curves. The point symbol visualizes the cell type. The definition for an expressed gene was parameterized in the following way: in Plot A, the gene needs to have UMI counts > 10 in all measured cells in total, in Plot B, read counts > 10 per kilobase transcript in all measured cells in total. +nFittedGenes<-22000 -## Figure S20: Comparison between powsimR and scPower for Drop-Seq and Smart-seq +#Number DE genes to be modeled +numGenes<-250 -Same as in Figure S14, only run for Drop-Seq and Smart-Seq data. +``` -## Figure S21: Power to detect rare cell types +```{r, eval=FALSE} -Model to calculate the power to detect rare cell types. Please look into the introduction vignette, section "Part 3: Power estimation to detect a sufficent number of cells in a specific cell types" for an explanation of all parameters. The cell type frequencies are taken from BioRad (see citation in the publication), all other parameters are set to realistic values. +##### +#Simulated DE studies - pancreas (smart-seq) -```{r, fig.width=8, fig.height=6} +simulated.parameters<-data.frame(mean.value=c(2,0.5,2,0.5), + rank.max=c(20000,20000,10000,10000), + name=c("highES_unifRank","lowES_unifRank", + "highES_highRank","lowES_highRank")) -prob=0.95 +print(simulated.parameters) -#Sample size -Nind <- c(100, 150, 200, 300) +de.raw.values.panc<-NULL +for(s in 1:nrow(simulated.parameters)){ + + print(s) + + #Simulate prior for expression rank + ranks<-uniform.ranks.interval(start=1,end=simulated.parameters$rank.max[s], + numGenes=numGenes) + + #Simulate prior for effect sizes + set.seed(1) + effectSizes<-effectSize.DE.simulation(mean=simulated.parameters$mean.value[s], + sd=1,numGenes=numGenes) + + sample.DE.genes<-data.frame(ranks=ranks, FoldChange=effectSizes, + geneLength=5000, name="Simulated") + + max.values<-NULL + for(i in budget){ + + power.study<-optimize.constant.budget.smartseq(i,"de",ct,ct.freq, + prepCostCell,costFlowCell, + readsPerFlowcell, + sample.DE.genes,"Simulated", + gamma.mixed.fits.smart, + disp.fun.param.smart, + nCellsRange = cellsPerPerson.smart, + readDepthRange = readDepth.smart, + mappingEfficiency = mapping.efficiency, + min.norm.count = min.norm.count, + perc.indiv.expr = perc.indiv.expr, + nGenes=nFittedGenes, + MTmethod = "FDR", + speedPowerCalc=TRUE) + + max.values<-rbind(max.values, + power.study[which.max(power.study$powerDetect),]) + } + max.values$budget<-budget + max.values$parameters<-simulated.parameters$name[s] + + de.raw.values.panc<-rbind(de.raw.values.panc,max.values) +} -#Number of cells required -N.min.cells<-c(1,10,20,50) +##### +#Observed DE studies - pancreas (smart-seq) +de.priors.panc<-de.ref.study[de.ref.study$type=="pancreas",] + +de.real.values.panc<-NULL +for(studyName in unique(de.priors.panc$name)){ -#Take cell type frequencies estimated from BioRad (see above) -cell.types.biorad<-data.frame(ct=c("B cells", "Monocytes", "NK cells", - "Dendritic cells","CD4 T cells","CD8 T cells"), - freq=c(13,13,7,1.5,30,13)/100) + max.values<-NULL + for(i in budget){ + + power.study<-optimize.constant.budget.smartseq(i,"de",ct,ct.freq, + prepCostCell,costFlowCell,readsPerFlowcell, + de.priors.panc,studyName, + gamma.mixed.fits.smart, + disp.fun.param.smart, + nCellsRange = cellsPerPerson.smart, + readDepthRange = readDepth.smart, + mappingEfficiency = mapping.efficiency, + min.norm.count = min.norm.count, + perc.indiv.expr = perc.indiv.expr, + nGenes=nFittedGenes, + MTmethod = "FDR", + speedPowerCalc=TRUE) + + max.values<-rbind(max.values, + power.study[which.max(power.study$powerDetect),]) + } + + max.values$budget<-budget + max.values$parameters<-studyName + de.real.values.panc<-rbind(de.real.values.panc,max.values) +} +``` -filter.ct<-c("CD4 T cells", "NK cells", "Dendritic cells", "Monocytes") -cell.type.comp<-cell.types.biorad[cell.types.biorad$ct %in% filter.ct,] +```{r, fig.height=14, fig.width=14} -#Set frequency behind the cell type labels (add a small number to round up at 0.5) -cell.type.comp$ct<-paste0(cell.type.comp$ct, - " (",round(cell.type.comp$freq*100+0.01,1),"%)") +#Get precalculated values +data(precalculatedBudgetOptim) +de.raw.values.lung<-budget.optimization[budget.optimization$type=="DE_simulated_lung",] +de.raw.values.panc<-budget.optimization[budget.optimization$type=="DE_simulated_pancreas",] +de.real.values.lung<-budget.optimization[budget.optimization$type=="DE_observed_lung",] +de.real.values.panc<-budget.optimization[budget.optimization$type=="DE_observed_pancreas",] +dummy.plot.smart<-data.frame(budget=c(min(budget),max(budget),min(budget),max(budget), + min(budget),max(budget),min(budget),max(budget)), + variable=c("powerDetect","powerDetect","readDepth","readDepth", + "totalCells","totalCells","sampleSize","sampleSize"), + value=c(0,1,0,500000, + min(cellsPerPerson.smart),1000,0,100)) -#Arrange the cell types in the order of frequency -cell.type.comp<-cell.type.comp[order(cell.type.comp$freq),] -cell.type.comp$ct<-factor(cell.type.comp$ct,levels=cell.type.comp$ct) +#DE lung simulated values +plot.values<-de.raw.values.lung[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] +plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) +dummy.plot.temp<-dummy.plot +dummy.plot.temp$parameters<-plot.values$parameters[1] +g.lung<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ + geom_line()+geom_point()+ + facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ + geom_blank(data=dummy.plot.temp)+ + scale_color_manual("Simulated parameters",values=col.paired[c(2,4,1,3)])+ + theme_bw()+ + xlab("Budget")+ylab("Value") -#Test each possible parameter combination -parameter.combinations<-expand.grid(prob,N.min.cells,cell.type.comp$ct,Nind) -colnames(parameter.combinations)<-c("prob.cutoff","min.num.cells","ct","num.indivs") +#DE pancreas simulated values +plot.values<-de.raw.values.panc[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] +plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) +dummy.plot.temp<-dummy.plot.smart +dummy.plot.temp$parameters<-plot.values$parameters[1] +g.panc<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ + geom_line()+geom_point()+ + facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ + geom_blank(data=dummy.plot.temp)+ + scale_color_manual("Simulated parameters",values=col.paired[c(2,4,1,3)])+ + theme_bw()+ + xlab("Budget")+ylab("Value") -#Merge cell types with frequencies -parameter.combinations<-merge(parameter.combinations,cell.type.comp) +#Merge values from eQTL study and DE study +all.studies<-c(unique(de.real.values.lung$parameters), + unique(de.real.values.panc$parameters)) -parameter.combinations$result.ssize<-mapply(scPower::number.cells.detect.celltype, - parameter.combinations$prob.cutoff, - parameter.combinations$min.num.cells, - parameter.combinations$freq, - parameter.combinations$num.indivs) +#De lung real values +plot.values<-de.real.values.lung[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] +plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) +dummy.plot.temp<-dummy.plot +dummy.plot.temp$parameters<-de.real.values.lung$parameters[1] +g.lung.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ + geom_line()+geom_point()+ + facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ + geom_blank(data=dummy.plot.temp)+ + scale_color_manual("Observed parameters",values=col.set.new[5:10], + labels=all.studies,limits=all.studies)+ + theme_bw()+ + xlab("Budget")+ylab("Value") + +#De pancreas real values +plot.values<-de.real.values.panc[,c("budget","powerDetect","totalCells","readDepth", + "sampleSize","parameters")] +plot.values<-reshape2::melt(plot.values,id.vars=c("budget","parameters")) +dummy.plot.temp<-dummy.plot.smart +dummy.plot.temp$parameters<-de.real.values.panc$parameters[1] +g.panc.2<-ggplot(plot.values,aes(x=budget,y=value,color=parameters))+ + geom_line()+geom_point()+ + facet_wrap(~variable,scales="free_y",ncol=4,labeller = labeller(variable=var.labs))+ + geom_blank(data=dummy.plot.temp)+ + scale_color_manual("Observed parameters",values=col.set.new[5:10], + labels=all.studies,limits=all.studies)+ + theme_bw()+ + xlab("Budget")+ylab("Value") +g.proto<-ggarrange(g.lung,g.panc,nrow=2,common.legend = TRUE, + legend="bottom",labels=c("a","b")) +g.real<-ggarrange(g.lung.2,g.panc.2,nrow=2,common.legend = TRUE, + legend="bottom",labels=c("c","d")) -#Labels samples -axis.labeller<-function(variable,value){ - return(paste(value,"individuals")) -} +row1 <- ggplot() + annotate(geom = 'text', x=1, y=1, + label="Prototypic scenarios", + size=6, angle = 90) + theme_void() -g<-ggplot(parameter.combinations,aes(x=min.num.cells,y=result.ssize,col=ct))+ - geom_line()+geom_point()+facet_wrap(~num.indivs,ncol=2, labeller = axis.labeller)+ - xlab("Minimal number of cells from target cell type per individual")+ - ylab("Cells per individual")+ - scale_color_manual("Cell type",values=col.set2)+ - scale_y_log10()+ - theme_bw() + - theme(axis.title=element_text(size=12), - axis.text=element_text(size=8), - aspect.ratio = 0.8, - legend.position = "bottom")+ - guides(col=guide_legend(nrow=2,byrow=TRUE)) +row2 <- ggplot() + annotate(geom = 'text', x=1, y=1, + label="Real world scenarios", + size=6,angle = 90) + theme_void() + +g<-ggarrange(row1,g.proto,row2,g.real,nrow=2,ncol=2, + widths=c(0.03,1)) print(g) + ``` -Figure S21: Power to detect rare cell types. The figure shows the required number of cells per individual (y-axis, log scale) to detect the minimal number of cells from a target cell type per individual (x-axis) with a probability of 95%. The probability depends on the total number of individuals and the frequency of the target cell type (purple, red, yellow, green). 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