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