Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples

The advent of mass cytometry has resulted in the generation of high-dimensional, single-cell expression data sets from clinical samples. These data sets cannot be effectively analyzed using traditional approaches. Instead, new approaches using dimensionality reduction and network analysis techniques have been implemented to assess these data. Here, detailed methods are described for analyzing immune cell expression from clinical samples using network analyses. Specifically, details are given for performing SCAFFoLD and CITRUS analyses. The methods described will use immune cell tumor infiltrate as an example.