Update README.md

README.md

-# counts_simulation
+% Counts simulations  & DESEQ2 investigations
+
+Purpose: 
+    1) Understand how DESEQ2 works
+    2) Understand how maximize statistical power 
+    2) Refined biological protocol (seqeuncing effort, ...)
+
+
+# About DESEQ2
+
+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
+
+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.
+
+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 
+
+<img src="./img/fig_mu_effect">
+
+# Investigation of µ effect 
+
+<img src="./img/fig_size_effect">
+
+