DataCamp DataCamp
Interactive Course

Differential Expression Analysis in R with limma

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

  • 4 hours
  • 15 Videos
  • 47 Exercises
  • 897 Participants
  • 3,900 XP

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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

    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.

  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.

  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.

  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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John Blischak
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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Collaborators
  • Richie Cotton

    Richie Cotton

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

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