Analysis of single cell RNA-seq data

Episodes

1. Alignment and feature counting with Cell Ranger
2. QC and Exploratory Analysis
3. Normalisation
4. sctransform: Variance Stabilising Transformation
5. Feature Selection and Dimensionality Reduction
6. Batch correction and data set integration
7. Clustering
8. Identification of cluster marker genes
9. Differential expression analysis
10. Differential Abundance

Prerequisites

• Inermediate level knowledge on R (programming language)
• Familiarity with terminal and basic linux commands
• Some knowledge on shell environment variables and for loops
• Ability to use a terminal based text editor such as nano
  • This is not much of an issue as we are using JupyterHub which has a more friendlier text editor.
• Intermediate level knowledge on Molecular Biology and Genetics

Recommended but not required

• Attend Genomics Data Carpentry, RNA-Seq Data Analysis, Introduction to R and/or Intermediate R

Data set

• Data used in this workshop is based on CaronBourque2020 relating to pediatric leukemia, with four sample types, including:
  • pediatric Bone Marrow Mononuclear Cells (PBMMCs)
  • three tumour types: ETV6-RUNX1, HHD, PRE-T

Attribution notice

• Material used in this workshop is based on folloowing repositories

  • Introduction to single-cell RNA-seq data analysis - University of Cambridge https://bioinformatics-core-shared-training.github.io/SingleCell_RNASeq_Jan23/
  • Analysis of single cell RNA-seq data - Welcome Sanger Institute https://www.singlecellcourse.org/

References

• https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0947-7
• https://www.nature.com/articles/nbt.4091
• https://arxiv.org/abs/physics/0512106
• https://www.nature.com/articles/s41598-019-41695-z
• https://www.nature.com/articles/s41598-019-41695-z#Fig3