Skip to main content
HomeR

Differential Expression Analysis with limma in R

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

Start Course for Free
4 hours15 videos47 exercises7,107 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

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.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Analyzing Genomic Data in R

Go To Track
  1. 1

    Differential Expression Analysis

    Free

    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.

    Play Chapter Now
    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. 2

    Flexible Models for Common Study Designs

    In this chapter, you'll learn how to construct linear models to test for differential expression for common experimental designs.

    Play Chapter Now
    Flexible linear models
    50 xp
    Design matrix for group-means model
    100 xp
    Contrasts matrix for group-means
    100 xp
    Test for differential expression for group-means
    100 xp
    Studies with more than two groups
    50 xp
    Design matrix for 3 groups
    100 xp
    Contrasts matrix for 3 groups
    100 xp
    Test for differential expression for 3 groups
    100 xp
    Factorial experimental design
    50 xp
    Design matrix for 2x2 factorial
    100 xp
    Contrasts matrix for 2x2 factorial
    100 xp
    Test for differential expression for 2x2 factorial
    100 xp
  3. 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.

    Play Chapter Now
    Normalizing and filtering
    50 xp
    Normalize
    100 xp
    Filter genes
    100 xp
    Accounting for technical batch effects
    50 xp
    Visualize batch effects
    100 xp
    Remove batch effects
    100 xp
    Visualizing the results
    50 xp
    Histogram of p-values
    100 xp
    Volcano plot
    100 xp
    Enrichment testing
    50 xp
    KEGG pathways
    100 xp
    Gene ontology categories
    100 xp
  4. 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.

    Play Chapter Now
    Pre-process the data
    50 xp
    Pre-process features
    100 xp
    Boxplot of Top2b
    100 xp
    Check sources of variation
    100 xp
    Model the data
    50 xp
    Design matrix
    100 xp
    Contrasts matrix
    100 xp
    Test for differential expression
    100 xp
    Inspect the results
    50 xp
    Histogram of p-values
    100 xp
    Volcano plot
    100 xp
    Pathway enrichment
    100 xp
    Conclusion
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Analyzing Genomic Data in R

Go To Track

datasets

Doxorubicin datasetLeukemia datasetHypoxia dataset

collaborators

Collaborator's avatar
David Campos
Collaborator's avatar
Shon Inouye
Collaborator's avatar
Richie Cotton

prerequisites

Introduction to Statistics in R
John Blischak HeadshotJohn 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.
See More

What do other learners have to say?

    FAQs

    Join over 15 million learners and start Differential Expression Analysis with limma in R today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.