Eswar, S. | bioRxiv Science

bioRxiv2025

cytoFlagR: A comprehensive framework to objectively assess high-parameter cytometry data for batch effects

MotivationHigh-parameter cytometry is widely used in longitudinal studies, but technical variation across batches can confound biological signals. However, tools that objectively identify problematic batches and markers are limited. ResultsWe introduce cytoFlagR, a comprehensive tool to flag batch-related problems at the marker and cell cluster level based on robust statistical evaluations. Batch and marker variations are assessed based on median signal intensities of negative and positive cell populations and positive cell frequencies, along with Earth Movers Distance (EMD) of signal intensity distributions. Additionally, cytoFlagR identifies cell type specific batch problems via unsupervised clustering and is suitable for mass and spectral cytometry datasets where it objectively detects distinct types of batch issues. We demonstrated cytoFlagRs utility for assessing datasets that include or lack reference controls. Thus, cytoFlagR improves quality control by objective identification of technical variations that may impact downstream analysis. Availability and ImplementationcytoFlagR is freely available as R scripts with documentation and an example at https://github.com/AndorfLab/cytoFlagR. Contactandorfsa@ucmail.uc.edu

bioinformatics↗

bioRxiv2025

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

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. naive 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.

bioinformatics↗