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.
Differential Expression AnalysisFree
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 analysis50 xpApplications of differential expression analysis50 xpDifferential expression data50 xpCreate a boxplot100 xpThe ExpressionSet class50 xpCreate an ExpressionSet object100 xpCreate a boxplot with an ExpressionSet object100 xpThe limma package50 xpSpecify a linear model to compare 2 groups100 xpTest for differential expression between 2 groups100 xp
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 models50 xpDesign matrix for group-means model100 xpContrasts matrix for group-means100 xpTest for differential expression for group-means100 xpStudies with more than two groups50 xpDesign matrix for 3 groups100 xpContrasts matrix for 3 groups100 xpTest for differential expression for 3 groups100 xpFactorial experimental design50 xpDesign matrix for 2x2 factorial100 xpContrasts matrix for 2x2 factorial100 xpTest for differential expression for 2x2 factorial100 xp
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 filtering50 xpNormalize100 xpFilter genes100 xpAccounting for technical batch effects50 xpVisualize batch effects100 xpRemove batch effects100 xpVisualizing the results50 xpHistogram of p-values100 xpVolcano plot100 xpEnrichment testing50 xpKEGG pathways100 xpGene ontology categories100 xp
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 data50 xpPre-process features100 xpBoxplot of Top2b100 xpCheck sources of variation100 xpModel the data50 xpDesign matrix100 xpContrasts matrix100 xpTest for differential expression100 xpInspect the results50 xpHistogram of p-values100 xpVolcano plot100 xpPathway enrichment100 xpConclusion50 xp
In the following tracksAnalyzing Genomic Data in R
PrerequisitesIntroduction to Statistics in R
John BlischakSee More
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.