Program

Classes are from:

Monday - Classes from 14:00 to 20:00 (Paris time)

Lecture 1 - Introduction to scRNA-Seq analysis [Jacques]

  • 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 - From sequencing reads to expression matrices [Jacques]

  • 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
  • IGV: Looking at cool things in alignment like where reads are, mutations, splicing

Lab 1 - Familiarizing yourself with the course AWS instance [Jacques]

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

Lab 2 - From sequencing reads to expression matrices [Fabricio]

  • Mapping sequencing data with Cellranger
  • Quality Control reports (CellRanger, dropEst, fastqc)

Tuesday - Classes from 14:00 to 20:00 (Paris time)

Flash talks [Everybody]

Lecture 3 - Quality control for scRNA-Seq data [Jacques]

  • What CellRanger does for quality filtering
  • Normalisation methods
  • Doublets, empty droplets, DropletUtils
  • Barcode swapping
  • Regression with technical covariates

Lab 3 - Introduction to R/Bioconductor [Fabricio]

  • Installing packages with CRAN and Bioconductor
  • Data types, data manipulation, slicing
  • I/O for scRNAseq analysis in R

Lab 4 - scRNA-Seq data wrangling [Fabricio]

  • Data structure
  • Data filtering
  • Exploratory data analysis

Wednesday - Classes from 14:00 to 20:00 (Paris time)

Lecture 4 - Identifying cell populations [Jacques]

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

Lab 5 - Identifying Cell Populations: dimensionality reduction, clustering and annotation [Jacques]

  • Feature selection
  • Dimensional reduction
  • Graph-based clustering
  • Marker gene detection
  • Cell type annotation
  • Data visualization

Lecture 5 - Data integration and batch effect correction [Orr]

  • 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 6 - Data integration and batch effect correction [Orr]

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

Thursday - Classes from 14:00 to 20:00 (Paris time)

Lecture 6 - Advances in single-cell genomics: the epigenome [Orr]

Lab 7 - Single-cell ATAC-Seq analysis [Orr]

Lecture 7 - Trajectories and pseudotimes [Orr]

  • Trajectory inference
  • Popular tools and packages for trajectory analysis (https://github.com/dynverse/dynmethods#list-of-included-methods)
  • Pseudotime inference
  • RNA velocity
  • Differential expression through pseudotime

Lab 8 - Inferring differentiation trajectories and pseudotime [Fabricio]

  • Infering trajectory in sperm cell lineage
  • Orientating a trajectory with RNA veloctiy
  • DE analysis along a trajectory

Friday - Classes from 14:00 to 20:00 (Paris time)

Lecture 8 - Advances in single-cell genomics: spatial transcriptomics [Orr]

Friday will then be divided in two parts:

  • Morning & afternoon (1h + 1h30): Group projects: analysing scRNA-seq data by yourself, from A to Z
  • Afternoon (1h): Group presentations (10’ each group, max 5 slides: what/why/where/when/how, conclusions)

Happy hour time!!