Session content

Monday – Classes from 09:30 to 17:30

 

Lecture 1 – scRNA-Seq experimental design

 

  • General introduction
  • Comparison of Bulk and single cell RNA-Seq
  • Overview of available scRNA-seq technologies and experimental protocols
  • scRNA-Seq experimental design

 

 

Lab 1 –  Understanding sequencing raw data

 

  • Shell and Unix commands
  • Raw file formats
  • Public data repositories

 

 

Lecture 2 - Intro to Data processing

 

  • scRNA-Seq processing workflow
  • Overview of Popular tools and algorithms
  • Common single-cell analyses and interpretation

 

 

Lab 2 – Processing raw scRNA-Seq data

 

  • Data outputs from different scRNAseq technologies
  • Demultiplexing sequencing data
  • Read Quality Control
  • Read alignment and visualization

 

 

 

Tuesday – Classes from 09:30 to 17:30

 

 

Lecture 3 – Transcriptome quantification

 

  • Read & UMI counting
  • Gene length & coverage
  • Gene expression units

 

 

Lab 3 - Introduction to R - part 1

 

  • Installing packages
  • Data-types
  • Data manipulation

 

 

Lab 4 – Introduction to R - part 2

 

  • Visualization tools
  • Data structures and file formats for single-cell data

 

 

 

Wednesday – Classes from 09:30 to 17:30

 

 

 Interactive Lecture 4 - Expression QC, normalisation and batch correction

 

  • Different normalisation methods

 

 

Lab 5 – Data wrangling for scRNAseq data

 

  • Quality control of cells and genes
  • Data exploration

 

 

Lecture 5 - Identifying cell populations

 

  • Feature selection
  • Dimensionality reduction
  • Clustering and assigning identity
  • Differential expression

 

Lab 6 – Feature selection & Clustering analysis

 

  • Parameters and clustering
  • Comparison of feature selection methods

 

 

 

Thursday – Classes from 09:30 to 17:30

 

 

Lecture 6 - Introduction to batch effects

 

  • Batch correction methods
  • Evaluation methods for batch correction

 

 

Lab 7 - Correcting batch effects

 

  • Applying Batch correction methods

 

 

Lecture 7 - Functional analysis of cell sub-populations

 

  • Gene sets and signatures
  • Pathway analysis

 

Lecture 8 - Pseudotime cell trajectories

 

  • Waddington Landscape
  • Pseudotime inference
  • Differential expression through pseudotime

 

 

Lab 8 - Functional and Pseudotime analysis

 

  • Popular tools and packages for functional analysis
  • Comparison of pseudotime methods

 

 

Friday – Classes from 09:30 to 17:30

 

 

Lecture 9 - Single-cell multiomic technologies

 

  • Introduction to other omic data types
  • Integrating scRNA-seq with other single-cell modalities

 

Lab 9 - Analysis of CITE-seq, scATAC-seq

 

 

Lecture 10 - Open discussion

 

  • Review, Questions and Answers