scICER: clustering stability assessment for integrated single-cell analyses

Motivation Clustering is essential in single-cell and spatial omics for identifying cell types and states, but stochastic algorithms such as Leiden can reduce cluster stability and compromise annotation reliability. We present scICER, an R/Seurat implementation of single-cell Inconsistency Clustering Estimator (scICE)-style inconsistency analysis that evaluate clustering stability on user-provided graphs stored in Seurat objects from standard or integrated workflows. Results Compared with the original Julia implementation, the scICER adds ecosystem-native input/output, fixed-resolution evaluation, optional handling of very small clusters, and practical workflows for large objects. Across 48 benchmark datasets and integrated analyses, scICER reproduced reference scICE behavior while enabling stability assessment after Harmony or scVI correction. Availability and implementation scICER is implemented in R and is freely available at https://github.com/ATPs/scICER. A companion Python implementation with similar functionality, scICEpy, is available at https://github.com/ATPs/scICEpy.