Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.

SUMMARY: Causal mediation analysis investigates the role of mediators in the relationship between exposure and outcome. In the analysis of omics or imaging data, mediators are often high-dimensional, presenting challenges such as multicollinearity and interpretability. Existing methods either compromise interpretability or fail to effectively prioritize mediators. To address these challenges and advance causal mediation analysis in high-dimensional contexts, we propose the Partial Sum Statistic and Sample Splitting Strategy (PS5) framework. Through extensive simulations, we demonstrate that PS5 offers superior type I error control, higher statistical power, reduced bias in mediation effect estimation, and more accurate mediator selection. We apply PS5 to an imaging genetics dataset of chronic obstructive pulmonary disease (COPD) patients from the COPDGene study. The results show successful estimation of the global indirect effect and identification of mediating image regions. Notably, we identify a region in the lower lobe of the lung that exhibits a strong and concordant mediation effect for both genetic and environmental exposures, suggesting potential targets for treatment to mitigate COPD severity caused by genetic and smoking effects. AVAILABILITY AND IMPLEMENTATION: PS5 is publicly available at https://github.com/hung-ching-chang/PS5Med.