Background: With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.Result: To address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.Conclusion: All the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.

Cyclophosphamide is a commonly used chemotherapeutic drug to treat cancer with side effects that trigger bladder injury and hemorrhagic cystitis. Although previous studies have demonstrated that certain cell subsets and communications are activated to drive the repair and regeneration of bladder, it is not well understood how distinct bladder cell subsets function synergistically in this process. Here, we used droplet-based single-cell RNA sequencing (scRNA-seq) to profile the cell types within the murine bladder mucous layer under normal and injured conditions. Our analysis showed that superficial cells are directly repaired by cycling intermediate cells. We further identified two resident mesenchymal lineages (Acta2⁺ myofibroblasts and Cd34⁺ fibroblasts). The delineation of cell-cell communications revealed that Acta2⁺ myofibroblasts upregulated Fgf7 expression during acute injury, which activated Fgfr signaling in progenitor cells within the basal/intermediate layers to promote urothelial cell growth and repair. Overall, our study contributes to a more comprehensive understanding of the cellular dynamics during cyclophosphamide-induced bladder injury and may help identify important niche factors contributing to the regeneration of injured bladders.

Trio-based whole-genome sequencing identified the role of chromatin modification in OCD pathology.