Monday – Classes from 2 to 8 pm CET

 

Lecture 1 – scRNA-Seq experimental design

• General introduction: cell atlas overviews
• Comparison of Bulk and single cell RNA-Seq
• Overview of available scRNA-seq technologies (10x) and experimental protocols

Lecture 2 - Intro to Data processing: from bcl file to count matrix

•  scRNA-Seq processing workflow starting with choice of sequencer (NextSeq, HiSeq, MiSeq) / barcode swapping and bcl files
• Overview of Popular tools and algorithms
• Common single-cell analyses and interpretation
• Sequencing data: alignment and quality control
• Looking at cool things in alignment like where reads are, mutations, splicing
• Read & UMI counting (Kallisto alignment-free pseudocounts as well), how RSEM works (length dependence, sequencing depth, multimapping reads), CellRanger (dropest), bustools

Lab 1 – Familiarizing yourself with the course AWS instance

• Logging in AWS
• Shell and Unix commands to navigate directories, create folders, open files
• Raw file formats
• Using RStudio
• Get data from 10x website, single cell portal, from GEO (fastqs, counts)

Lab 2 – Processing raw scRNA-Seq data

•  Data outputs from different scRNAseq technologies (10x, Smart-seq2)
• Quality Control reports (CellRanger, dropEst, fastqc)
• Mapping sequencing data with Cellranger

 

 

Tuesday – Classes from 2 to 8 pm CET

 

 

Lecture 3 - Expression QC, normalisation and gene-level batch correction

•  What CellRanger does for quality filtering
• PBMC data
• Normalisation methods https://www.nature.com/articles/nmeth.4292
• Doublets, empty droplets, DropletUtils
• Barcode swapping
• Regression with technical covariates
• What about imputation?

Lab 3 - Introduction to R/Bioconductor

• Installing packages with CRAN and Bioconductor
• Data types
• Data manipulation, slicing

Lab 4 – Data wrangling for scRNAseq data

• Introducing SingleCellExperiment object
• Quality control of cells and genes (doublets, ambient, empty drops)
• Data exploration: violin plots…
• Genes
• Mitochondrial & ribosomal genes
• Filter
• Normalize
• Find variable genes

 

 

 

Wednesday – Classes from 2 to 8 pm CET

 

 

Lecture 4 - Identifying cell populations

• Feature selection
• Dimensionality reduction
• Graph-based clustering
• Assigning cluster identity
• Differential expression tests

Lecture 5 - Batch effects correction

• Batch correction methods (regress out batch, scaling within batch, Seurat v3, MNN, Liger, Harmony, scvi, scgen)
• Evaluation methods for batch correction (ARI, average silhouette width, kBET…)

Lab 5 – Feature selection & Clustering analyses

• Parameters and clustering
• Comparison of feature selection methods
• Annotating clusters

Lab 6 - Correcting batch effects

• Comparison of batch correction methods
• Choosing the optimal batch correction approach

 

 

Thursday – Classes from 2 to 8 pm CET

 

 

Lecture 6 - Advanced topics

• Trajectory inference
• RNA velocity
• Pseudotime inference
• Differential expression through pseudotime

Lecture 7 - Single-cell multi-omic technologies

• Introduction to other omic data types
• Integrating scRNA-seq with other single-cell modalities (CITE, Perturb, ATAC, methylation…)

Lab 7 - Pseudotime analyses

• Popular tools and packages for functional analysis (https://github.com/dynverse/dynmethods#list-of-included-methods)
• Review concepts from papers
• Comparison of pseudotime methods

Lab 8 - Functional analyses

• GO over-representation analyses
• GSEA analyses
• Finding regulatory elements with scATAC-seq and CICERO: https://www.bioconductor.org/packages/devel/bioc/vignettes/cicero/inst/doc/website.html

 

Friday – Classes from 2 to 8 pm CET

 

 

Individual projects: analysing scRNA-seq data by yourself, from A to Z.

•  Small groups
• Pick your favorite scRNA-seq dataset (one you have never looked at before!)
• Work your way through pre-processing / analysis / interpretation of the data
• Support from Jacques & Orr whenever needed: try and solve things by yourself, but don’t hesitate if you are stuck!
• Flash presentation at the end of the day: what/why/where/when/how, conclusions