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Differential Expression Analysis with limma in R

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$12

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49% OFF

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Functional genomic technologies, such as sequencing and microarrays, can be used by scientists to get unbiased measurements of gene expression at large scale. No matter if you're creating your data from publicly available data sets or creating it, you will need to be able to analyze it. This course will show you how to use R/Bioconductor's flexible package limma to perform differential expression analysis on the most popular experimental designs. Additionally, you will learn how to prepare data, correct batch effects and visual assess results. You can also conduct enrichment testing. After completing this course, you will be able use these strategies for insight into any functional genomes study.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Introduction to Statistics in R

What You Will Learn

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

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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

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You will learn how to pre-process the data, identify and correct for batch effects, visually assess the results, and perform enrichment testing

Course Instructors

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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...

Course Reviews

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Course Cover

$12

$6

49% OFF

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