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Differential Expression Analysis with limma in R

Learn to use the Bioconductor package limma for differential gene expression analysis.

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4 Hours15 Videos47 Exercises5,075 Learners3900 XPAnalyzing Genomic Data Track

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Course Description

Functional genomic technologies like microarrays, sequencing, and mass spectrometry enable scientists to gather unbiased measurements of gene expression levels on a genome-wide scale. Whether you are generating your own data or want to explore the large number of publicly available data sets, you will first need to learn how to analyze these types of experiments. In this course, you will be taught how to use the versatile R/Bioconductor package limma to perform a differential expression analysis on the most common experimental designs. Furthermore, you will learn how to pre-process the data, identify and correct for batch effects, visually assess the results, and perform enrichment testing. After completing this course, you will have general analysis strategies for gaining insight from any functional genomics study.

  1. 1

    Differential 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.

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    Differential expression analysis
    50 xp
    Applications of differential expression analysis
    50 xp
    Differential expression data
    50 xp
    Create a boxplot
    100 xp
    The ExpressionSet class
    50 xp
    Create an ExpressionSet object
    100 xp
    Create a boxplot with an ExpressionSet object
    100 xp
    The limma package
    50 xp
    Specify a linear model to compare 2 groups
    100 xp
    Test for differential expression between 2 groups
    100 xp
  2. 3

    Pre- and post-processing

    Now that you've learned how to perform differential expression tests, next you'll learn how to normalize and filter the feature data, check for technical batch effects, and assess the results.

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  3. 4

    Case Study: Effect of Doxorubicin Treatment

    In this final chapter, you'll use your new skills to perform an end-to-end differential expression analysis of a study that uses a factorial design to assess the impact of the cancer drug doxorubicin on the hearts of mice with different genetic backgrounds.

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In the following tracks

Analyzing Genomic Data


dcamposlizDavid CamposrichieRichie CottonshoninouyeShon Inouye
John Blischak Headshot

John Blischak

Postdoctoral Scholar at University of Chicago

John Blischak is a researcher in the Department of Human Genetics at the University of Chicago. He has years of experience using linear models to generate insight from functional genomics experiments, and he is excited to teach you how you can do the same.
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