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.
In 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.
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.
In this chapter, we execute the differential expression analysis, generate results and identify the differentially expressed genes.
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 tracksAnalyzing Genomic Data
DatasetsFibrosis raw counts dataset
Bioinformatics Consultant and Trainer
“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.”
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Decision Science Analytics, USAA