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vignettes/powsimR.Rmd

@@ -154,7 +154,7 @@ In this vignette, we illustrate the features of `powsimR` by assessing the power
 
 The basic workflow of `powsimR` is illustrated in Figure \@ref(fig:schematic): A) The mean-dispersion relationship is estimated from RNA-seq data, which can be either single cell or bulk data. The user can provide their own count table or a publicly available one and choose whether to fit a negative binomial or a zero-inflated negative binomial. The plot shows a representative example of the mean-dispersion relation estimated, assuming a negative binomial for the Ziegenhain data, the red line is the loess fit, that we later use for the simulations. B) These distribution parameters are then used to set up the simulations in which the user can freely choose the magnitude and amount of differential expression, the sample size configuration as well as the processing pipeline for DE-testing. C) Finally, the error rates are calculated. These can be either returned as marginal estimates per sample configuration, or stratified according to the estimates of mean expression, dispersion or dropout rate. Furthermore, the user can evaluate the analytical choices (e.g. imputation and normalisation).
 
-```{r schematic, fig.cap="PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data, which can be either single cell or bulk data. The plot shows the mean-dispersion estimated, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration.", echo=F, eval=T, include=T}
+```{r schematic, fig.cap="PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data are estimated , which can be either single cell or bulk data. The plot shows the mean-dispersion relation, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration.", echo=F, eval=T, include=T}
 knitr::include_graphics("powsimr_workflow.png")
 ```