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Differential Expression Analysis with limma in R
4.8(54)8k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
Functional genomic technologies generate high-dimensional measurements of gene expression across microarrays, sequencing, and mass spectrometry. This advanced course provides a rigorous foundation in statistical analysis using the Bioconductor package limma, empowering you to process complex genomic experiments and extract biological insights from raw expression data.
Who It's For
Bioinformaticians, computational biologists, and bio-data analysts seeking advanced skills in R to analyze gene expression studies and handle complex experimental designs.
Key Takeaways
- Data Pre-processing: Prepare high-throughput genomic data, perform quality control, and identify and correct for batch effects.
- Limma Workflows: Utilize the R/Bioconductor limma package to model complex experimental setups for differential expression analysis.
- Visualization & Enrichment: Visually assess statistical results and perform downstream gene set enrichment testing to derive functional insights.
About this course
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.
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