Updated DE figure

lessons/04a_design_formulas.md

@@ -17,10 +17,9 @@ Approximate time: 30 minutes
 The final step in the differential expression analysis workflow is **fitting the raw counts to the NB model and performing the statistical test** for differentially expressed genes. In this step we essentially want to determine whether the mean expression levels of different sample groups are significantly different.
 
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-<img src="../img/de_theory.png" width="600">
+<img src="../img/Differential_expression.png" width="900">
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-*Image credit:  Paul  Pavlidis,  UBC*
+
 
 
 The [DESeq2 paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8) was published in 2014, but the package is continually updated and available for use in R through Bioconductor. It builds on good ideas for dispersion estimation and use of Generalized Linear Models from the [DSS](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4005660/) and [edgeR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/) methods.