Overview
Context
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 Differential Expression Analysis with limma in R course offered by Data Camp, 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.
Programme Structure
Chapters
- Differential Expression Analysis
- Flexible Models for Common Study Designs
- Pre- and post-processing
- Case Study: Effect of Doxorubicin Treatment
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
Language
Delivered
Campus Location
- New York City, United States
Disciplines
Statistics View 109 other Short Courses in Statistics in United StatesWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- This course is primarily aimed at intermediate level learners.
- Professionals in the field of bioinformatics and functional genomics, who are tasked with analyzing genomic data and gaining insight from functional genomics studies, would benefit from this course.
PREREQUISITES
- Introduction to Statistics in R
Tuition Fees
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
- This course can be accessed for free with the Data Camp Premium or Teams subscriptions