diff --git a/vignettes/01-theoryBehindHtrfit.Rmd b/vignettes/01-theoryBehindHtrfit.Rmd index a8ac3e4f3da3295eb54fe4300c556fcee05c3888..de20c39bd10bd09d86d06619ffe58a0a64f90048 100644 --- a/vignettes/01-theoryBehindHtrfit.Rmd +++ b/vignettes/01-theoryBehindHtrfit.Rmd @@ -17,8 +17,8 @@ knitr::opts_chunk$set( ``` -In the realm of RNAseq analysis, various key experimental parameters play a crucial role in influencing the statistical power to detect expression changes. Parameters such as sequencing depth, the number of replicates, and others are expected to impact statistical power. -To navigate the selection of optimal values for these experimental parameters, we introduce a comprehensive statistical framework known as HTRfit, underpinned by computational simulation. Moreover, HTRfit offers seamless compatibility with DESeq2 outputs, facilitating a comprehensive evaluation of RNAseq analysis. +In the realm of RNA-seq analysis, various key experimental parameters play a crucial role in influencing the statistical power to detect expression changes. Parameters such as sequencing depth, the number of replicates, and others are expected to impact statistical power. +To navigate the selection of optimal values for these experimental parameters, we introduce a comprehensive statistical framework known as HTRfit, underpinned by computational simulation. Moreover, HTRfit offers seamless compatibility with DESeq2 outputs, facilitating a comprehensive evaluation of RNA-seq analysis. # HTRfit simulation workflow