From 5af4d96e3bffc06ab3f59aa0a808ac0ec408298b Mon Sep 17 00:00:00 2001
From: Arnaud Duvermy <arnaud.duvermy@ens-lyon.fr>
Date: Thu, 10 Feb 2022 13:48:39 +0100
Subject: [PATCH] Update README.md

---
 README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index aeb7303..d72f0e4 100644
--- a/README.md
+++ b/README.md
@@ -28,18 +28,19 @@ Fit a curve to wise gene dispersion estimate
 
 ### 3) Fit linear model 
 
-The differential expression analysis uses a generalized linear model of the form:
+The differential expression analysis uses a generalized linear model of the form: <br/>
 Kij ∼ NB(µij , α i )<br/>
 µij = s j q ij <br/>
 log 2 (q ij ) = x j. β i <br/>
 
 where counts K ij for gene i, sample j are modeled using a Negative Binomial distribution with
 fitted mean µ ij and a gene-specific dispersion parameter α i . The fitted mean is composed of a
-sample-specific size factor s j and a parameter q ij proportional to the expected true concentration
-of fragments for sample j. The coefficients β i give the log2 fold changes for gene i for each col-
-umn of the model matrix X. The sample-specific size factors can be see as the logFC between condition.
+sample-specific size factor s j and a parameter q ij proportional to the expected true concentration of fragments for sample j. The coefficients β i give the log2 fold changes for gene i for each column of the model matrix X. 
+x j. β i can be see as the logFC between condition (for treated condition).
 
-4) Wald Test H0: Log(FC) = 0
+### 4) Wald Test H0: 
+
+Test if Log(FC) = 0
 
 
 
-- 
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