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-# Counts simulations  & DESEQ2 investigations
+# DESEQ2
 
-Purpose: 
-1) Understand how DESEQ2 works <br/>
-2) Understand how maximize statistical power <br/>
-2) Refined biological protocol (seqeuncing effort, ...) <br/>
+- [dispersion inference](results/v3/DESEQ2/2022_02_03_dispersion.html)
+- [sequencing depth](results/v3/DESEQ2/2023_02_05_sequencingDepth.html)
+- [replicates](results/v3/DESEQ2/2023_02_06_replicates.html)
 
 
-## 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 <br/>
-
-Get dispersion estimate for each gene using Maximum Linkelihood Estimatation <br/>
-Fit a curve to wise gene dispersion estimate
-
-### 3) Fit linear model 
-
-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 column of the model matrix X. <br/>
-
-### 4) Wald Test: 
-
-H0: Test if Log(FC) = 0 <br/>
-
-With DESeq2, the Wald test is the default used for hypothesis testing when comparing two groups. The Wald test is a test of hypothesis usually performed on parameters that have been estimated by maximum likelihood. The Wald test is also a standard way to extract a P value from a regression fit.
-
-
-## HTRSIM
+# GLM
 
+- [dispersion inference](results/v3/GLM/2023_02_04_dispersion.html)
+- [sequencing depth](results/v3/GLM/2023_02_05_sequencingDepth.html)
+- [replicates](results/v3/GLM/2023_02_06_replicates.html)