Single-cell RNAseq analysis with R/Bioconductor Single-cell RNAseq analysis with R/Bioconductor 3 Lab 1: Familiarizing yourself with the course AWS instance 1 Lecture 1 - Introduction to scRNAseq analysis Welcome Program RStudio Prerequisites Day 1 1 Lecture 1 - Introduction to scRNAseq analysis 2 Lecture 2 - From sequencing reads to expression matrices 3 Lab 1: Familiarizing yourself with the course AWS instance 4 Lab 2: From .bcl to count matrix Day 2 5 Lecture 3 - Quality control for scRNA-Seq data 6 Lab 3: Introduction to R/Bioconductor 7 Lab 4 - Single-cell RNA-seq data wrangling Day 3 8 Lecture 4 - Identifying cell populations 9 Lab 5: Dimension reduction, clustering and annotation 10 Lecture 5 - Data integration and batch effect correction 11 Lab 6: Batch correction Day 4 12 Lecture 6 - Advances in single-cell genomics: the epigenome 13 Lab 7: Single-cell ATAC-seq analysis workflow 14 Lecture 7 - Trajectories and pseudotimes 15 Lab 8: Pseudotime analyses Day 5 16 Lecture 8 - Advances in single-cell genomics: spatial transcriptomics Extra resources Single-cell RNAseq analysis with R/Bioconductor |
J. Serizay, O. Ashenberg, F. Almeida-Silva This book was built with Quarto. Day 1 2 Lecture 2 - From sequencing reads to expression matrices