scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data

Abstract

Summary: With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses.For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Keywords

• Bioinformatics
• Single Cell
• Flow Cytometry/Mass Cytometry
• Cancer