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.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Differential Expression Analysis with limma in R

ดูคอร์ส

คำแนะนำการฝึกหัด

The ExpressionSet object eset with the leukemia data has been loaded in your workspace.

Use model.matrix to create a design matrix with no intercept. Recall that the variable of interest for this study (progressive vs. stable cancers) is in the column Disease of the phenotype data frame.

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# Create design matrix with no intercept
design <- ___(~___ + ___, data = ___(eset))

# Count the number of samples modeled by each coefficient
colSums(design)

แก้ไขและรันโค้ด