Automatically generate two‐dimensional gating hierarchy from clustered cytometry data

Abstract

Cytometry is an important technique widely used in medicine and biological research. Biologists traditionally analyze single-cell cytometry data by manual gating, which can be subjective and labor intensive. To address this issue, many automated and semiautomated methods have been developed. These advanced methods are designed to speed up and standardize the analysis of cytometry data, but their popularity is limited by their visualizations which are not intuitive to biologists who are accustomed to the conventional biaxial gating plots. In this article, we present a new method called Cluster-to-Gate (C2G) that can take clustering results as input, and automatically generate a nested two-dimensional gating hierarchy, which is a visualization representation that biologists are familiar with. This method can generate gating sequences for multiple target populations simultaneously and summarize them in one hierarchical tree that represents the gating hierarchy. We have tested this method on target populations defined by manual gating, automated clustering algorithms (k-means for example), and visualization-assisted methods (SPADE and tSNE). We have demonstrated that C2G is able to generate gating sequences that capture cell populations defined by the various clustering strategies, and robust to over-clustered and overlapping target populations. © 2018 International Society for Advancement of Cytometry.

Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization

“…Both viSNE/tSNE 18 and SPADE 19 are prone to suffering from the curse of dimensionality (see Supplementary Notes 1 and 2, Supplementary Figs 14–16) and most importantly they require user-defined input parameters that seriously affect clustering and visualization outcomes. In addition, as noted by Yang et al 20 “…[viSNE and SPADE] are not intuitive to biologists who are accustomed to the two-dimensional nested gating representations.”…”

“…Aiming to make clustering outcomes more intuitive to biologists, Yang et al 20 have recently developed the C2G data visualization method. This method is able to generate a gating hierarchy that captures the target populations (identified by any clustering method) and present the hierarchy in nested two-dimensional gating sequences that resemble the conventional manual gating analysis.…”

“…Presenting Hi-D data in a two-dimensional nested tree structure may indeed help biologists who are accustomed to the two-dimensional nested gating representations. However, conventional (domain knowledge-driven) gating strategies do not always follow the best separation path that underlies the gating strategy principle applied by the C2G method 20 . Thus, a gating strategy that is built according to the best separation principle may not be readily interpretable to biologists.…”

Flow Cytometry: The Next Revolution

“…However, this process is labor intensive, subjective, and prone to inter-operator variability. Expectedly, the initial use of machine learning algorithms in cytometry was for gating purposes [221][222][223][224][225][226][227][228][229]. As a result, multiple approaches have been developed that directly or indirectly learn from expert-defined gates and consistently apply gating criteria across large datasets.…”