Imaging mass cytometry data: Head and neck squamous cell carcinoma tissue section
Data files
May 10, 2021 version files 2.75 MB
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All_regions.csv
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Region_1.csv
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Region_2.csv
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Region_3.csv
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Region_4.csv
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Region_5.csv
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Region_6.csv
Abstract
High dimensional cytometry is an innovative tool for immune monitoring in health and disease, it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of multiparametric “big data” usually requires specialist computational knowledge. Here we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster) an R package for immune profiling cellular heterogeneity in high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: 1, data import and quality control, 2, dimensionality reduction and unsupervised clustering; and 3, annotation and differential testing, all contained within an R-based open-source framework.