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

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 (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/JingAnyaSun/ImmCellTyper">https://github.com/JingAnyaSun/ImmCellTyper</ext-link>), 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.