Differential Expression Analysis with limma in R

Name: Differential Expression Analysis with limma in R
Brand: Careervira
Price: 62 GBP
Availability: InStock
Rating: 1 (1 reviews)

via

DataCamp

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Visit Course Report

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

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

Skills You Will Gain

Prerequisites/Requirements

Introduction to Statistics in R

What You Will Learn

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

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

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

Course Content

Expand all sections

Module 1: Differential Expression Analysis

Module 2: Flexible Models for Common Study Designs

Module 3: Pre- and post-processing

Module 4: Case Study: Effect of Doxorubicin Treatment

Course Instructors

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...