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@@ -8,20 +8,39 @@ Purpose:
 
 ## About DESEQ2
 
+Main step:
+
+1) Estimate size factor
+
+Median ratio method is used to estimate the size factor per sample.
+
+The size factor is used for normalizing counts (per gene per sample).
+Normalized counts allow minimizing biais linked to library size.
+By normalizing the counts DESEQ2 aims to make sure differential expression are based on factors study and not to sequencing depth
+/!\ gene length is not take into account !
+
+2) Estimate dispersion
+
+Purpose: Estimate the variability between replicates
+
+Get dispersion estimate for each gene using Maximum Linkelihood Estimatation
+Fit a curve to wise gene dispersion estimate
+
+3) Fit linear model 
+
 The differential expression analysis uses a generalized linear model of the form:
-Kij ∼ NB(µij , α i )
-µij = s j q ij
-log 2 (q ij ) = x j. β i
+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 replaced by gene-specific
-normalization factors for each sample using normalizationFactors.
+umn of the model matrix X. The sample-specific size factors can be see as the logFC between condition.
+
+4) Wald Test H0: Log(FC) = 0
 
-Experiments without replicates do not allow for estimation of the dispersion of counts around the
-expected value for each group, which is critical for differential expression analysis. Analysis with-out replicates is no longer supported since v1.22.
 
 
 ## Investigation of µ effect