ImmCellTyper: an integrated computational pipeline for systematic mining of Mass Cytometry data to assist deep immune profiling

Author:

Sun Jing1ORCID,Choy Desmond2ORCID,Sompairac Nicolas2,Jamshidi Shirin2,Mishto Michele13ORCID,Kordasti Shahram245ORCID

Affiliation:

1. Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King’s College London

2. School of Cancer and Pharmaceutical Sciences, King’s College London

3. Research group of Molecular Immunology, Francis Crick Institute

4. Haematology Department, Guy’s Hospital

5. Department of Clinical and Molecular Sciences, Università Politecnica delle Marche

Abstract

Mass cytometry, also known as Cytometry by time-of-flight (CyTOF), is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level. This technology significantly advances clinical research in immune monitoring and the interrogation of immune cell populations. Nevertheless, the vast amount of data generated by CyTOF poses a daunting challenge for analysis. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel and robust toolkit designed for CyTOF data analysis. The analytical framework incorporates an in-house developed semi-supervised clustering tool named BinaryClust, which first characterises main cell lineages, followed by in-depth interrogation for population of interest using unsupervised methods.BinaryClust was benchmarked with existing clustering tools and demonstrated superior accuracy and speed across two datasets comprising around 4 million cells, performing as good as manual gating by human experts. Furthermore, this computational pipeline provides a variety of visualization and analytical tools spanning from quality control to differential analysis, which can be tailored to user’s specific needs, aiming to provide a one-stop solution for CyTOF data analysis. The general workflow consists of five key steps: 1) Batch effect evaluation and correction, 2) Data quality control and pre-processing, 3) Main cell lineage characterisation and quantification, 4) Extraction and in-depth investigation of cell type of interest; 5) Differential analysis of cell abundance and functional marker expression (supporting multiple study groups). Overall, ImmCellTyper integrates expert’s biological knowledge in a semi-supervised fashion to accurately deconvolute well-defined main cell lineages, while also preserving the potential of unsupervised approaches to discover novel cell subsets and providing a user-friendly toolset to remove the analytical barrier for high-dimensional immune profiling.

Publisher

eLife Sciences Publications, Ltd

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