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Course Description

Single cell sequencing of RNA libraries (scRNA-seq) is a major scientific breakthrough that enables the transcriptomic study of cellular and tissue heterogeneity, in contrast to traditional bulk tissue transcriptomics methods. While many concepts are shared between scRNA-seq and bulk RNA-seq computational analysis workflows, there are many new scRNA-seq analysis concepts and methods. The CBW has developed a 2-day course that provides an introduction to scRNA-seq data analysis with integrated tutorials demonstrating the use of current scRNA-seq analysis tools. The tutorials are designed as self-contained units that include example data and detailed instructions for installation of all required bioinformatics tools.

Course Objectives

Participants will gain practical experience and skills to be able to:

  • Perform basic bioinformatics tasks such as tool installation
  • Perform read alignment and transcript quantification
  • Perform quality control
  • Visualize and interpret scRNA-seq data
  • Perform clustering and differential expression analysis 
  • Annotate cell clusters
  • Integrate scRNA-seq data sets
Target Audience

Graduates, postgraduates, and PIs working or about to embark on an analysis of scRNA-seq data. Attendees should be familiar with mapping and analysis of bulk RNA-seq data, and aware of the general workflow for scRNA-seq analysis, likely through familiarity with methods sections in publications.

Note that the focus of this course is on single cell RNA-seq analysis. This course is designed to pair well with the bulk RNA-seq analysis course that precedes it (July 17-19, 2023) and students are encouraged to enroll in both.

Prerequisites

Basic familiarity with Unix commands and the R scripting language. Previous attendance at the CBW RNA-seq Analysis workshop is recommended. This workshop requires participants to complete pre-workshop tasks and readings.

You will also require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 1.5GHz CPU, 8GB RAM, 10GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements). 

Course Outline

Module 1: Introduction to single cell RNA-seq (Trevor Pugh)

  • Overview of single cell RNA-seq compared to other transcriptomics approaches
  • Experimental design
  • Sample preparation and cell capture techniques
  • Library preparation and transcript quantification
  • scRNA-seq technology options
  • Introduction to the analysis workflow
  • Introduction to different tool kits and online data sharing portals
  • Recommended computer specifications for running analysis on real data sets

Lab Practical: Log into AWS and set up instance with scRNA-seq analysis tools and data sets, load a single-cell dataset & explore parts of the object.

Module 2: Read Quantification (Tallulah Andrews)

  • Structure of scRNAseq reads
  • Assigning reads to genes
    • What kind of reference do you need?
    • What does scRNAseq capture / not capture?
    • Introns vs exons
  • Assigning reads to cells: Cell barcode demultiplexing
  • Counting the number of unique RNA molecules: UMI deduplication
    • Manual UMI counting interactive activity.
  • Cell filtering
    • Cellranger, EmptyDrops
  • Doublets

Lab Practical: Using EmptyDrops to identify valid single-cells. Using DoubletFinder to identify doublets.

Module 3: scRNA-seq Data Structures and Quality Control (Tallulah Andrews)

  • Per cell and per gene metrics: total number of counts (UMIs), total number of detected genes, total number of mitochondrial counts, percent of mitochondrial counts
  • Batch effects and experimental artifacts

Lab Practical: Creating SingleCellExperiment Objects with R and using the scater package in Bioconductor to perform quality control.

Module 4: Normalization and Confounders (Tallulah Andrews)

  • Normalization approaches
  • Data visualization with PCA and tSNE and UMAP plots

Lab Practical: Using the scater package in Bioconductor to normalize and visualize data before and after QC & normalization.

Module 5: Biological Analyses (Tallulah Andrews)

  • Dimensionality reduction
  • Clustering
  • Differential expression between clusters and identifying cell “marker” genes
  • Statistical tests
  • Cell type annotation

Lab Practical: Use the Seurat package to cluster scRNA-seq data

Module 6: Data Set integration (Gary Bader)

  • Label centric integration
  • Cross data set normalization
  • mnnCorrect
  • Canonical Correlation Analysis (CCA) & RPCA in Seurat
  • Data sharing resources (e.g., HCA (cellxgene), Broad Portal, CReSCENT, GEO etc.)

Lab Practical: Use the Seurat package to integrate two scRNA-seq datasets using CCA and RPCA.

Workshop Details:

Duration: 2 days

Start: Jun 20, 2024

End: Jun 21, 2024

Location: Canada

Course Mode: Onsite

Status: Application Open

Apply
Offers:
CAD $495 for applications received between February 7, 2024 to April 20, 2024
CAD $695 for applications received between April 21, 2024 to June 6, 2024
Limited to: 26 participants
Open Access Content:

Canadian Bioinformatics Workshops promotes open access. Past workshop content is available under a Creative Commons License.

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