Analysis of single-cell RNA-seq data: Dimensionality reduction, clustering, and lineage inference

Instructor(s) name(s) and contact information

Diya Das (@diyadas, diyadas@berkeley.edu), Davide Risso (@drisso), and Kelly Street (@kstreet13)

Workshop Description

This workshop will be presented as a lab session (brief introduction followed by hands-on coding) that instructs participants in a Bioconductor workflow for the analysis of single-cell RNA-sequencing data, in three parts:

1. dimensionality reduction that accounts for zero inflation, over-dispersion, and batch effects
2. cell clustering that employs a resampling-based approach resulting in robust and stable clusters
3. lineage trajectory analysis that uncovers continuous, branching developmental processes

We will provide worked examples for lab sessions, and a set of stand-alone notes in this repository.

Note to organizers: A previous version of this workshop was well-attended at BioC 2017, but the tools presented have been significantly updated for interoperability (most notably, through the use of the SingleCellExperiment class), and we have been receiving many requests to provide an updated workflow. We plan to incorporate feedback from this workshop into a revised version of our F1000 Workflow.

Pre-requisites

We expect basic knowledge of R syntax. Some familiarity with S4 objects may be helpful, but not required. More importantly, participants should be familiar with the concept and design of RNA-sequencing experiments. Direct experience with single-cell RNA-seq is not required, and the main challenges of single-cell RNA-seq compared to bulk RNA-seq will be illustrated.

Workshop Participation

This will be a hands-on workshop, in which each student, using their laptop, will analyze a provided example datasets. The workshop will be a mix of example code that the instructors will show to the students (available through this repository) and short exercises.

R / Bioconductor packages used

1. zinbwave : https://bioconductor.org/packages/zinbwave
2. clusterExperiment: https://bioconductor.org/packages/clusterExperiment
3. slingshot: https://github.com/kstreet13/slingshot (will be submitted to Bioconductor shortly)

Time outline

2 hr workshop:

Activity	Time
Intro to single-cell RNA-seq analysis	15m
zinbwave (dimensionality reduction)	30m
clusterExperiment (clustering)	30m
slingshot (lineage trajectory analysis)	30m
Questions / extensions	15m

Workshop goals and objectives

Learning goals

• describe the goals of single-cell RNA-seq analysis
• identify the main steps of a typical single-cell RNA-seq analysis
• evaluate the results of each step in terms of model fit
• synthesize results of successive steps to interpret biological significance and develop biological models
• apply this workflow to carry out a complete analysis of other single-cell RNA-seq datasets

Learning objectives

• compute and interpret low-dimensional representations of single-cell data
• identify and remove sources of technical variation from the data
• identify sub-populations of cells (clusters) and evaluate their robustness
• infer lineage trajectories corresponding to differentiating cells
• order cells by developmental "pseudotime"
• identify genes that play an important role in cell differentiation