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.
Diese Übung ist Teil des Kurses
Differential Expression Analysis with limma in R
Anleitung zur Übung
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.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create design matrix with no intercept
design <- ___(~___ + ___, data = ___(eset))
# Count the number of samples modeled by each coefficient
colSums(design)