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RNA-Seq with Bioconductor in R

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

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4 Hours16 Videos44 Exercises14,238 Learners

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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. 1

    Introduction to RNA-Seq theory and workflow

    Free

    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.

    Play Chapter Now
    Introduction to RNA-Seq
    50 xp
    Core Concepts
    50 xp
    RNA-Seq Packages
    100 xp
    RNA-Seq Workflow
    50 xp
    Read Alignments
    50 xp
    Exploring the raw count matrix
    100 xp
    Differential gene expression overview
    50 xp
    DGE Theory
    50 xp
    DGE Theory: Metadata
    100 xp
  2. 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.

    Play Chapter Now
    Introduction to differential expression analysis
    50 xp
    Practice with the DESeq2 vignette
    50 xp
    Organizing the data for DESeq2
    50 xp
    Matching metadata and counts data
    100 xp
    Count normalization
    50 xp
    Normalizing counts with DESeq2
    100 xp
    Hierarchical heatmap
    50 xp
    Hierarchical heatmap by condition
    100 xp
    Hierarchical heatmap analysis
    50 xp
    Principal component analysis
    50 xp
    PCA analysis
    100 xp
    PCA practice: exploring variations
    50 xp
    PCA practice: exploring additional variations
    50 xp
  3. 3

    Differential expression analysis with DESeq2

    In this chapter, we execute the differential expression analysis, generate results and identify the differentially expressed genes.

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    DE analysis
    50 xp
    Creating the DE object
    100 xp
    DESeq2 model - dispersion
    50 xp
    DESeq2 model - exploring dispersions
    100 xp
    Interpreting the dispersion plot
    50 xp
    DESeq2 model - contrasts
    50 xp
    DESeq2 model - extracting results
    100 xp
    DESeq2 results - LFC shrinkage
    100 xp
    DESeq2 results
    50 xp
    DESeq2 results exploration
    100 xp
    Summarizing DESeq2 results
    100 xp
    DESeq2 significant results
    100 xp
  4. 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.

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    Visualization of results
    50 xp
    DESeq2 visualizations - MA and volcano plots
    100 xp
    DESeq2 visualizations - heatmap
    100 xp
    RNA-Seq DE analysis summary - setup
    50 xp
    RNA-Seq DE analysis - experimental planning
    50 xp
    RNA-Seq DE workflow summary
    100 xp
    RNA-Seq DE analysis summary
    50 xp
    DE analysis
    100 xp
    DE analysis results
    100 xp
    RNA-Seq next steps
    50 xp

In the following tracks

Analyzing Genomic Data in R

Datasets

Fibrosis raw counts dataset

Collaborators

David Campos
Shon Inouye
Richie Cotton

Prerequisites

Introduction to Bioconductor in RIntroduction to Data Visualization with ggplot2
Mary Piper HeadshotMary 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.
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