Contents

Tutorials on some of the common steps during scRNAseq data analysis using three popular toolkits: Seurat, Bioconductor and Scanpy.

You can run the labs either in a Singularity container on Uppmax (recommended during course) or using Docker locally on your system. Instructions on running the labs are provided here

A short description of the data used in the tutorials is provided here.

Tip

We perform the same steps with all three toolkits, but there are some small differences as all methods are not implemented everywhere. It’s up to you which toolkit you want to work with. To download file, Right click > Save Link As….

Topic Seurat Bioconductor Scanpy
1 Quality Control
2 Dimensionality reduction
3 Data integration
4 Clustering
5 Differential expression
6 Celltype prediction
7 Trajectory inference
Caution

This topics below are not covered in the workshop and are therefore optional. These topics also require separate containers. See instructions on running labs for more info.

Optional Topic Seurat Bioconductor Scanpy
8 Spatial transcriptomics

Toolkit comparisons

Below are a few reports where the results from the 3 toolkits and multiple methods in the toolkits are analysed. OBS! Several of these scripts requires additional packages that are not available in the Docker containers used for the exercises.

  • General comparison of the main steps of the pipelines:
  • Comparison of Variable gene selection methods:
  • Differential gene expression detection:
  • Normalization methods:

Useful resources

  • The github repository for this course
  • Pre-recorded videos of lectures (from 2022) are available on Youtube
  • Single Cell Glossary
  • Single cell RNA-seq course at from Hemberg lab
  • Single cell RNA-seq course in Python
  • Single cell RNA-seq course at Broad
  • Repository listing many scRNA-seq tools
  • SingleCellExperiment objects for many datasets
  • Conquer datasets - many different datasets based on a salmon pipeline
  • The Human Cell Atlas project