Design matrix for group-means model
In the previous chapter, you tested the leukemia data for differential expression using the traditional treatment-contrasts parametrization. As a first step to learning the more flexible group-means parametrization, you will re-test the leukemia data to confirm you obtain the same results.
Cet exercice fait partie du cours
Differential Expression Analysis with limma in R
Instructions
The ExpressionSet object eset with the leukemia data has been loaded in your workspace.
- Use
model.matrixto create a design matrix with no intercept. Recall that the variable of interest for this study (progressive vs. stable cancers) is in the columnDiseaseof the phenotype data frame.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create design matrix with no intercept
design <- ___(~___ + ___, data = ___(eset))
# Count the number of samples modeled by each coefficient
colSums(design)