Overview

This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. You will also learn integrated analysis of two samples in order to detect cell type -specific responses. Both DropSeq and 10X Genomics data are used in the exercises. 

The course is kindly sponsored by the ELIXIR EXCELERATE project.

This course is part of CSCs Bioweek.

Audience

The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required, and the course is thus suitable for everybody who is planning to use single cell RNA-seq.

Syllabus

You will learn how to:

• check the quality of reads with FastQC
• tag reads with molecular and cellular barcodes
• trim reads
• align reads to the reference genome with HISAT2 and STAR
• tag reads with gene names
• visualize aligned reads in genomic context using the Chipster genome browser
• estimate the number of usable cells by checking the inflection point
• detect bead synthesis errors
• create and filter DGE
• regress out unwanted variability
• detect variable genes and perform principle component analysis
• cluster cells and find marker genes for a cluster
• run canonical correlation analysis (CCA) to identify common sources of variation between two datasets
• align two samples for integrated analysis
• find conserved cluster markers within two samples
• find differentially expressed genes in a cluster between two samples
• visualize genes with cell type specific responses in two samples

Learning objectives

After this course you should be able to:

• use a range of bioinformatics tools to undertake basic analysis of single cell RNA-seq data
• discuss a variety of aspects of single cell RNA-seq data analysis
• understand the advantages and limitations of single cell RNA-seq data analysis