From 307c12525efae0d7acb2688f64dbdec28819c29a Mon Sep 17 00:00:00 2001 From: fduveau <102-fduveau@users.noreply.gitbio.ens-lyon.fr> Date: Thu, 16 May 2024 15:58:53 +0200 Subject: [PATCH] Update 01-theoryBehindHtrfit.Rmd Correction RNAseq -> RNA-seq --- vignettes/01-theoryBehindHtrfit.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/01-theoryBehindHtrfit.Rmd b/vignettes/01-theoryBehindHtrfit.Rmd index a8ac3e4..de20c39 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 -- GitLab