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 talk, 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 R/Bioconductor infrastructure. Secondly, I will cover benchmarks of key analysis steps, such as clustering and dimension reduction. Finally, I will touch on how we transferred our DS analysis framework to scRNA-seq, and developed a complex, flexible simulation framework for method comparison, with the ‘muscat’ package.
Institute of Molecular Life Sciences, University of Zurich
Helena earned her undergraduate degree at the Univeristy of Heidelberg in Biochemistry. She then went on to earn her Master’s degree in Computational Biology & Bioinformatics at the ETH Zurich. She is currently a PhD candidate in Statistical Bioinformatics at the University of Zurich.
Helena focuses on developing analysis frameworks for CyTOF data and differential discovery in scRNA-seq data. She is the author of a popular Bioconductor package providing tools for preprocessing and analysis of cytometry data.