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Course

RNA-Seq with Bioconductor in R

IntermediateSkill Level

4.7+

Updated 09/2024

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

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RProbability & Statistics4 hr16 videos44 Exercises3,150 XP21,175Statement of Accomplishment

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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.

Prerequisites

Introduction to Bioconductor in R Introduction to Data Visualization with ggplot2

1

Introduction to RNA-Seq theory and workflow

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.

Introduction to RNA-Seq

Core Concepts

RNA-Seq Packages

RNA-Seq Workflow

Read Alignments

Exploring the raw count matrix

Differential gene expression overview

DGE Theory: Metadata

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.

Introduction to differential expression analysis

Practice with the DESeq2 vignette

Organizing the data for DESeq2

Matching metadata and counts data

Count normalization

Normalizing counts with DESeq2

Hierarchical heatmap

Hierarchical heatmap by condition

Hierarchical heatmap analysis

Principal component analysis

PCA analysis

PCA practice: exploring variations

PCA practice: exploring additional variations

3

Differential expression analysis with DESeq2

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

DE analysis

Creating the DE object

DESeq2 model - dispersion

DESeq2 model - exploring dispersions

Interpreting the dispersion plot

DESeq2 model - contrasts

DESeq2 model - extracting results

DESeq2 results - LFC shrinkage

DESeq2 results

DESeq2 results exploration

Summarizing DESeq2 results

DESeq2 significant results

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.

Visualization of results

DESeq2 visualizations - MA and volcano plots

DESeq2 visualizations - heatmap

RNA-Seq DE analysis summary - setup

RNA-Seq DE analysis - experimental planning

RNA-Seq DE workflow summary

RNA-Seq DE analysis summary

DE analysis

DE analysis results

RNA-Seq next steps

RNA-Seq with Bioconductor in R

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FAQs

What background do I need in bioinformatics before taking this course?

You should have completed Introduction to Bioconductor in R and be comfortable with basic R, ggplot2, and the tidyverse before starting this RNA-Seq analysis course.

Which R package is used for differential expression analysis?

The course uses the DESeq2 package to model count data with a negative binomial distribution and test for differentially expressed genes between conditions.

What visualizations will I create to interpret RNA-Seq results?

You will generate heatmaps and volcano plots to visualize differentially expressed genes, helping you identify significant biological patterns in your data.

Does the course cover quality control steps before running differential expression?

Yes. You will learn how to assess count data quality, identify outliers, and detect major sources of variation before proceeding to differential expression analysis.

Who typically uses RNA-Seq analysis skills in their work?

Bioinformaticians, genomics researchers, and computational biologists use RNA-Seq to identify genes and pathways involved in diseases, drug responses, and developmental biology.

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