Mass cytometry (CyTOF) allows for examination of dozens of proteins at single-cell resolution. By employing heavy metal isotopes rather than fluorescent tags, thereby significantly reducing spectral overlap, CyTOF enables generation of high-throughput high-dimensional cytometry data.
Given the emergence of replicated multi-condition experiments, a primary task in the analysis of any type of single-cell data is to make sample-level inferences, in order to identify i) differentially abundant subpopulations; and, ii) changes in expression at the subpopulation-level, i.e., differential states (DS), across conditions. Preceding such analyses, key challenges lie in data preprocessing (e.g., to remove artefactual signal), clustering (to define subpopulations), and dimension reduction.
In this tutorial, I will present a suite of tools for differential discovery in CyTOF data, including ‘CATALYST’ for preprocessing and visualization, ‘diffcyt’ for differential testing, and a comprehensive analysis pipeline that leverages existing R/Bioconductor infrastructure. Taken together, this framework allows for streamlined and reproducible (R-based) processing of cytometry data from large-scale studies in particular.
Differential discovery: https://doi.org/10.12688/f1000research.11622.3
PhD Epidemiology & Biostatistics, University of Zurich, Switzerland
statistical bioinformatics group, Mark D Robinson
– Swiss National Fund funded exchange: single cell genomics group, Holger Heyn, CNAG-CRG, Barcelona, Spain
BSc Biochemistry, University of Heidelberg, Germany
– research internship, Pennsylvania State University, USA
– Erasmus exchange, Imperial College London, UK
MSc Computation Biology & Bioinformatics, ETH Zurich, Switzerland
– research assistant, Mark D Robinson, University of Zurich, Switzerland
Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland