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

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.

Main functionalities

scQUEST has been published as a computational protocol in STAR Protocols (Martinelli et al, 2022). If you find scQUEST useful in your research, please consider citing:

@article{10.1016/j.xpro.2022.101578,
    title={scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data},
    author={Martinelli, Adriano Luca and Wagner, Johanna and Bodenmiller, Bernd and Rapsomaniki, Maria Anna},
    journal = {STAR Protocols},
    volume = {3},
    number = {3},
    pages = {101578},
    year = {2022},
    doi = {10.1016/j.xpro.2022.101578},
}

Installation and Tutorials

In our detailed Online Documentation you'll find:

• Installation instructions.
• An overview of scQUEST's main components and API.
• An end-to-end tutorial using the breast cancer mass cytometry data from (Wagner at al., 2019).
• A tutorial using publicly available flow cytometry data from (Levine et al., 2015)
• Tutorials on how to load .fcs files to AnnData objects and how to customize scQUEST's PyTorch models and datasets.