Workshop: Single-cell RNA-seq analysis with R/Bioconductor

22/11/2021 | News Events

2022: For biologists and bioinformaticians with prior knowledge of R/ Bioconductor

Physalia Course: scRNAseq data analysis with R / Bioconductor

Dates: 24.01/ 26.01/ 31.01./ 2.02/ 7.02.2022

This course will introduce biologists and bioinformaticians to the field of single-cell RNA sequencing. We will cover a range of software and analysis workflows that extend over the spectrum from the best practices in the filtering scRNA-seq data to the downstream analysis of cell clusters and temporal ordering. This course will help the attendees gain accurate insights in pre-processing, analysis and interpretation of scRNA-seq data.

We will start by introducing general concepts about single-cell RNA-sequencing. From there, we will then continue to describe the main analysis steps to go from raw sequencing data to processed and usable data. We will present classical analysis workflows, their output and the possible paths to investigate for downstream analysis.

Throughout this workshop, we will put an emphasis on R/Bioconductor ecosystem and the different packages which will be used to analyse datasets and learn new approaches.

The course is structured in modules over five days.

During the first 1/3 of the day, formal lectures will cover the key concepts required to understand the principles scRNA-seq analysis (~2h).

Following these lectures, practical examples will be shown to illustrate how to translate the acquired knowledge into functional R code (~1h). At this stage, trainees will get acquainted with state-of-the-art packages for scRNAseq analysis as well as the best practices in bioinformatics.

During the second half of the day (3h), trainees will work by themselves, following guided exercises to improve their understanding of scRNAseq analysis workflow. A “solution” for each exercise will be provided once the exercises are finished. The exercises will mainly focus on specific concepts introducted earlier that day. However, analytical steps studied throughout the previous days will also be integrated so that towards the end of the week, trainees are fully able to perform fundamental scRNAseq analyses from beginning to end.

Office hours will take place during the last hour of the exercises. An instructor will be available to answer individual questions related to daily exercises. A Slack channel will also be available so that Q&A are available for everybody.

Learning Outcomes:
At the end of this course, you should be able to:

• Understand the pros/cons of different single-cell RNA-seq methods
• Process and QC of scRNA-seq data
• Normalize scRNA-seq data
• Correct for batch effects
• Visualise the data and applying dimensionality reduction
• Perform cell clustering and annotation
• Perform differential gene expression analysis
• Infer cell trajectory and pseudotime, and perform temporal differential expression

Session 1 

  • scRNA-Seq experimental design (lecture - 2 hours)
  • From raw data to counts ((demonstration - 1 hour)
  • Processing raw scRNA-Seq data (homework – 3 hours)

Session 2

  • Expression QC and normalisation (lecture - 2 hours)
  • scRNAseq in R/Bioconductor -part1 (demonstration - 1 hour)
  • Processing scRNAseq counts and data wrangling (homework – 3 hours)

Session 3

  • Identifying cell populations (lecture - 2 hours)
  • scRNAseq in R/Bioconductor- part2 (demonstration - 1 hour)
  • Clustering analysis of scRNAseq data (homework – 3 hours)

Session 4

  • Cell type annotation & batch effects (lecture - 2 hours)
  • scRNAseq in R/Bioconductor- part3 (demonstration - 1 hour)
  • Investigation of scRNAseq cell composition (homework – 3 hours)

Session 5

  • Cell type annotation & batch effects (lecture - 2 hours)
  • scRNAseq in R/Bioconductor- part3 (demonstration - 1 hour)
  • Investigation of scRNAseq cell composition (homework – 3 hours)

Session 6

  • Trajectories and pseudotimes (lecture - 2 hours)
  • Advanced scRNAseq analysis in R (demonstration - 1 hour)
  • Investigation of scRNAseq cell composition (homework – 3 hours)