CytoPheno: Automated descriptive cell type naming in flow and mass cytometry

Abstract

Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4⁺, CCR7⁺, CD45RA⁺, CD19^-) and descriptive cell type names (e.g. naïve CD4⁺ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated tool, CytoPheno, that assigns marker definitions and cell type names to unidentified clusters. First, post-clustered expression data undergoes per-marker calculations to assign markers as positive or negative. Next, marker names undergo a standardization process to match to Protein Ontology identifier terms. Finally, marker descriptions are matched to cell type names within the Cell Ontology. Each part of the tool was tested with benchmark data to demonstrate performance. Additionally, the tool is encompassed in a graphical user interface (R Shiny) to increase user accessibility and interpretability. Overall, CytoPheno can aid researchers in timely and unbiased phenotyping of post-clustered cytometry data.

Figure 1.

Simplified schematic depicting the inputs and outputs for each part of the CytoPheno tool. Created in BioRender.com.

Figure 2:

Part 2 workflow from initial marker name to final standardized PRO or GO linked term.

Figure 3.

A 2×2 confusion matrix depicting the number (percentage) of actual and predicted markers categorized as positive or negative for each of the three (Samusik, Kimmey, and spectral) benchmark datasets.

Figure 4.

Simplified depiction of the three parts and six substeps that form the R shiny GUI. Each step represents a separate section the GUI takes the user through. The boxes contain a brief description and an example screenshot from that part of the application. The screenshots depict certain sections, but not the entirety, of the user interface.