Create an ExpressionSet object
Managing 3 different data sets for one experiment is tedious and error-prone, especially if you need to perform any filtering. Combine the 3 data sets from the leukemia experiment into a unified object using the Bioconductor class ExpressionSet.
This is a part of the course
“Differential Expression Analysis with limma in R”
Exercise instructions
The expression matrix (x), feature data (f), and phenotype data (p) are loaded in your workspace.
Create a new ExpressionSet object using the function
ExpressionSet.Pass the expression matrix to the
assayDataargument.Pass the phenotype data frame to the
phenoDataargument.Pass the feature data frame to the
featureDataargument.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load package
library(Biobase)
# Create ExpressionSet object
eset <- ___(assayData = ___,
phenoData = AnnotatedDataFrame(___),
featureData = AnnotatedDataFrame(___))
# View the number of features (rows) and samples (columns)
dim(eset)This exercise is part of the course
Differential Expression Analysis with limma in R
Learn to use the Bioconductor package limma for differential gene expression analysis.
To begin, you'll review the goals of differential expression analysis, manage gene expression data using R and Bioconductor, and run your first differential expression analysis with limma.
Exercise 1: Differential expression analysisExercise 2: Applications of differential expression analysisExercise 3: Differential expression dataExercise 4: Create a boxplotExercise 5: The ExpressionSet classExercise 6: Create an ExpressionSet objectExercise 7: Create a boxplot with an ExpressionSet objectExercise 8: The limma packageExercise 9: Specify a linear model to compare 2 groupsExercise 10: Test for differential expression between 2 groupsWhat is DataCamp?
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