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Commit 5af4d96e authored by Arnaud Duvermy's avatar Arnaud Duvermy
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Update README.md

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...@@ -28,18 +28,19 @@ Fit a curve to wise gene dispersion estimate ...@@ -28,18 +28,19 @@ Fit a curve to wise gene dispersion estimate
### 3) Fit linear model ### 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/> Kij ∼ NB(µij , α i )<br/>
µij = s j q ij <br/> µij = s j q ij <br/>
log 2 (q ij ) = x j. β i <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 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 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 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.
of fragments for sample j. The coefficients β i give the log2 fold changes for gene i for each col- x j. β i can be see as the logFC between condition (for treated condition).
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 ### 4) Wald Test H0:
Test if Log(FC) = 0
......
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