Analyzing Multimodal Single-Cell Sequencing Data like an Immunologist with Hierarchical Gating in CellEngine

This week at CYTO, we presented our poster examining the potential to identify cell types in CITE-seq data using hierarchical gating. Compared to clustering, gated populations have clear definitions based on specific marker expression levels. We compared cell type assignment by hierarchical gating vs. unbiased clustering. Clustering did not separate every immune population we would typically gate in a cytometry experiment; however, it did identify distinct subsets within our gated populations that our hierarchy failed to define. Among others, the Vα7.2+ mucosal-associated invariant T (MAIT) cell population could be divided into two subpopulations based on numerous protein and gene features. We propose a hybrid approach to cell type identification for CITE-seq that is driven by hierarchical gating and uses clustering to refine the hierarchy. By leveraging analysis tools that are familiar to immunologists, we highlight how these complex datasets can be analyzed more intuitively than many realize today.

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CellEngine features high-speed versions of multiple unbiased clustering methods, along with classical gating tools, useful for implementing this hybrid approach. Try it out with a free two-month trial at https://cellengine.com.