Course Identification

Exercises require running and changing R code, therefore familiarity with R is required. We would like to know who has prior R experience and courses they took.

Recent advances in molecular biology, microfluidics, and computation have transformed the growing field of single-cell RNA sequencing (scRNA-seq). In addition, new approaches now encompass diverse characterization of a single cell’s such as: chromatin accessibility, spatial positioning and immunophenotype. The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data.

This course is an introduction to basic approaches in single cell RNA-sequencing (scRNA-seq) data analysis and in combination with additional modalities. It will include hands-on exercises of common bioinformatics analysis workflows.

Experience in R programming and in analysis of bulk RNA-Seq is required.

We will cover the following topics:

1. Introduction to Single cell technologies
2. Analysing gene expression (GEX) from single cells  with Cell Ranger (using Chromium 10X)
3. Quality control measures, normalization, clustering and more using R package Seurat.
4. Downstream analysis of gene expression data: annotating clusters, trajectories.
5. Analysing multiome data i.e. GEX & ATAC-Seq using R package Signac
6. Introduction to spatial transcriptomics

Upon successful completion of the course students will be able to:

1. Demonstrate familiarity with the common bioinformatics workflows, with an emphasis on gene expression of single cell sequencing as well as in combination with other modalities.
2. Familiarity with R-based tools for single cell sequence data analysis.