In this workshop, we provide a quick, hands-on introduction to current computational analysis approaches for studying single-cell RNA sequencing data. We will introduce the data infrastructure for scRNA-seq analysis in R and practice a workflow of scRNAseq analysis; from pre-processing, quality control to dimensionality reduction and clustering, marker gene detection and cell type annotation – with plenty more along the way!
Keywords: single-cell RNA-seq, differential expression
Relevance: This workshop is designed to guide folks through their first experience with handling single-cell expression data. The workshop will be suitable for both wet-lab biologists who want to get started analyzing single cell data or computational biologists who want to transition to single cell analyses.
Requirements: We use the Bioconductor single-cell ecosystem for this workshop. Thus, participants will need a recent version of R (version 4.0+) and a set of specific packages that we use.
The code snippet below will install the necessary packages for you in R (i.e. run the following code at the R prompt in an R or RStudio session). The first line installs the BiocManager package, which is the preferred package for then installing Bioconductor packages. The next (long) line then installs the necessary Bioconductor packages (and any dependencies).
Bioinformatics and Celluar Genomics lab, St Vincent’s Institute
We are from the Bioinformatics and Cellular Genomics lab at St Vincent’s Institute. We are broadly interested in how computational approaches can drive biological discovery. In particular, we are interested in developing statistical and machine learning methods and software tools for the analysis of high-throughput sequencing data, with a focus on single-cell genomic data. We are also interested in the ways in which DNA variation contributes to variation in gene expression at the level of individual cells. We study “single-cell genetics” in this sense by looking at single-cell quantitative trait loci and at the effects of somatic mutations in healthy ageing and cancer. We work closely with biological collaborators to contribute computational expertise to studies motivated by specific biologically focused questions.