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.
This exercise is part of the course
Differential Expression Analysis with limma in R
Exercise 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.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create design matrix with no intercept
design <- ___(~___ + ___, data = ___(eset))
# Count the number of samples modeled by each coefficient
colSums(design)