Christina Azodi, Ruqian Lyu, Jeffrey Pullin

WORKSHOP: A hands on introduction to analyzing single-cell RNA-seq data

We will present a brief introduction to current single-cell RNA sequencing protocols, the data formats they generate and typical questions they can be used to answer. Then, we will provide an instruction document for practicing a workflow of single-cell analysis; from pre-processing and quality control to differential expression analysis. The first half of the workshop will cover key quality control and data visualization, before considering the challenges of clustering and cell type identification using prominent R/Bioconductor packages including scater and scran. The rest of the workshop will focus on the use of single-cell differential expression (DE) tools to answer biological research questions. In particular, the workshop will discuss the rationale of current DE tools, their limitations and the specific biological questions they can and cannot answer. Best practice workflows for DE based on recent benchmarking efforts will be presented.

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: Laptop with R (www.r-project.org), RStudio (http://www.rstudio.com/) and the R packages listed below installed. Some experience with R/Bioconductor is recommended but not required. A selection of Bioconductor packages will need to be installed (details will be provided).

The workflow will be demonstrated using this dataset from 10xGenomics:

Dr Christina Azodi, Ms Ruqian Lyu, Mr Jeffrey Pullin

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.