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

Differential Expression Analysis

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

Flexible Models for Common Study Designs

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

Normalize

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

Pre- and post-processing

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

Pathway enrichment

Conclusion

Case Study: Effect of Doxorubicin Treatment

Doxorubicin dataset

Leukemia dataset

Hypoxia dataset

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.

Introduction to Statistics in R

Discover how you can use the versatile R/Bioconductor package limma to perform a differential expression analysis on the most common experimental designs.

Differential Expression Analysis with limma in R

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

Análise de dados genômicos em R

Instructor

Models for complicated study designs

Group-means parametrization

Design matrix for group-means

Testing the group-means parametrization

The parametrization does not change the results

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_6456/subtitles/course_6456_0827d5445f999f64e4c5a45150ce36e2.vtt

Flexible Models for Common Study Designs - Flexible linear models

Flexible linear models

Create Your Free Account