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Course

Differential Expression Analysis with limma in R

AdvancedSkill Level

4.7+

Updated 08/2024

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

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RProbability & Statistics

4 hr

15 videos

47 Exercises

3,900 XP

8,117

Statement of Accomplishment

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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.

Prerequisites

Introduction to Statistics in R

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.

Differential expression analysis

Applications of differential expression analysis

Differential expression data

Create a boxplot

The ExpressionSet class

Create an ExpressionSet object

Create a boxplot with an ExpressionSet object

The limma package

Specify a linear model to compare 2 groups

Test for differential expression between 2 groups

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.

Flexible linear models

Design matrix for group-means model

Contrasts matrix for group-means

Test for differential expression for group-means

Studies with more than two groups

Design matrix for 3 groups

Contrasts matrix for 3 groups

Test for differential expression for 3 groups

Factorial experimental design

Design matrix for 2x2 factorial

Contrasts matrix for 2x2 factorial

Test for differential expression for 2x2 factorial

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.

Normalizing and filtering

Filter genes

Accounting for technical batch effects

Visualize batch effects

Remove batch effects

Visualizing the results

Histogram of p-values

Volcano plot

Enrichment testing

KEGG pathways

Gene ontology categories

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.

Pre-process the data

Pre-process features

Boxplot of Top2b

Check sources of variation

Model the data

Design matrix

Contrasts matrix

Test for differential expression

Inspect the results

Histogram of p-values

Volcano plot

Pathway enrichment

Differential Expression Analysis with limma in R

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FAQs

Is this course suitable for beginners?

No. This course is primarily aimed at intermediate level learners.

Will I receive a certificate at the end of the course?

Yes, upon successful completion, all course participants will receive a certificate verifying their achievement.

Who will benefit from this course?

Professionals in the field of bioinformatics and functional genomics, who are tasked with analyzing genomic data and gaining insight from functional genomics studies, would benefit from this course.

What topics does this course cover?

This course covers topics such as goals of differential expression analysis, managing gene expression data in R and Bioconductor, running differential expression analysis with limma, constructing linear models to test for differential expression, normalizing and filtering the feature data, checking for technical batch effects, and performing enrichment testing.

What is the teaching method of this course?

The teaching method of this course includes step-by-step instruction and hands-on applications with videos, quizzes, exercises and expert feedback. At the end of the course, a case study will be used to test and confirm new-found skills.

Are there any prerequisites before taking this course?

Yes, prior programming experience in R is recommended before taking this course.

Is there a discussion or forum in this course?

Yes, this course provides both in-text discussions and an online forum where you can ask for feedback from the course instructors and engage in conversations about the course material with fellow participants.

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