From 923350d8a5ec9697bb49182da8139432cdf80ff8 Mon Sep 17 00:00:00 2001
From: aduvermy <arnaud.duvermy@ens-lyon.fr>
Date: Fri, 27 Oct 2023 13:52:05 +0200
Subject: [PATCH] update readme

---
 README.md | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 57857bb..fc74cd8 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ Furthermore, by enabling the inclusion of fixed effects, mixed effects, and inte
 
 ## Installation
 
-* method A:  
+#### method A:  
 
 To install the latest version of HTRfit, run the following in your R console :
 ```
@@ -28,7 +28,7 @@ if (!requireNamespace("remotes", quietly = TRUE))
 remotes::install_git("https://gitbio.ens-lyon.fr/aduvermy/HTRfit")
 ```
 
-* method B:
+#### method B:
 
 You also have the option to download a release directly from the [HTRfit release page](https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/releases). Once you've downloaded the release, simply untar the archive. After that, open your R console and execute the following command, where HTRfit-v1.0.0 should be replaced with the path to the untarred folder:
 
@@ -67,6 +67,12 @@ We have developed [Docker images](https://hub.docker.com/repository/docker/ruana
 7. Enjoy HTRfit !
 
 
+## Biosphere virtual machine
+
+A straightforward way to use **HTRfit** is to run it on a Virtual Machine (VM) through [Biosphere](https://biosphere.france-bioinformatique.fr/catalogue/). We recommend utilizing a VM that includes RStudio for an integrated development environment (IDE) experience. Biosphere VM resources can also be scaled according to your simulation needs.  
+**HTRfit** can be installed using the [method A](#method-A).
+
+
 ## HTRfit simulation workflow
 
 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 more have a significant impact. 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. 
-- 
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