RNA-Seq with Bioconductor in R
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Start Course for Free4 Hours16 Videos44 Exercises6,515 Learners
3150 XPCreate Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.Loved by learners at thousands of companies
Course Description
RNA-Seq is an exciting next-generation sequencing method used for identifying genes and pathways underlying particular diseases or conditions. As high-throughput sequencing becomes more affordable and accessible to a wider community of researchers, the knowledge to analyze this data is becoming an increasingly valuable skill. Join us in learning about the RNA-Seq workflow and discovering how to identify which genes and biological processes may be important for your condition of interest! We will start the course with a brief overview of the RNA-Seq workflow with an emphasis on differential expression (DE) analysis. Starting with the counts for each gene, the course will cover how to prepare data for DE analysis, assess the quality of the count data, and identify outliers and detect major sources of variation in the data. The DESeq2 R package will be used to model the count data using a negative binomial model and test for differentially expressed genes. Visualization of the results with heatmaps and volcano plots will be performed and the significant differentially expressed genes will be identified and saved.
- 1
Introduction to RNA-Seq theory and workflow
FreeIn this chapter we explore what we can do with RNA-Seq data and why it is exciting. We learn about the different steps and considerations involved in an RNA-Seq workflow. - 2
Exploratory data analysis
In this chapter, we perform quality control on the RNA-Seq count data using heatmaps and principal component analysis. We explore the similarity of the samples to each other and determine whether there are any sample outliers. - 3
Differential expression analysis with DESeq2
In this chapter, we execute the differential expression analysis, generate results and identify the differentially expressed genes. - 4
Exploration of differential expression results
In this final chapter we explore the differential expression results using visualizations, such as heatmaps and volcano plots. We also review the steps in the analysis and summarize the differential expression workflow with DESeq2.
In the following tracks
Analyzing Genomic DataDatasets
Fibrosis raw counts dataset
David Campos
Richie Cotton
Shon Inouye
Mary Piper
Bioinformatics Consultant and Trainer
Mary Piper serves dual roles as research analyst and bioinformatics trainer in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. However, her primary role is the development and instruction of bioinformatics workshops focused on the analysis of next-generation sequencing data. She has a PhD in cellular and molecular biology from the University of Michigan and a background in science education. Her passion for bioinformatics research and teaching led to her desire to pursue bioinformatics as a career and to share that knowledge with the community.
What do other learners have to say?
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics, USAA
Join over 7 million learners and start RNA-Seq with Bioconductor in R today!
Create Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.