Estimating the completeness of large-scale single-cell sequencing projects

During embryonic development, cells undergo differentiation into highly specialized cell types. Capitalizing on single-cell RNA sequencing, many initiatives and substantial resources are cataloguing these differentiated cell types by their transcriptomic profiles. Despite the extensive efforts to profile various organs and their cellular compositions, we lack metrics to assess the completeness of the sequencing projects. In this cellular biodiversity analysis, we leverage the increasingly available single-cell data together with statistical methods, originally developed for assessing the species richness of ecological communities, to estimate dataset completeness based on current data from single-cell profiling technologies. Deriving from such cellular richness estimates, we establish a practical statistical framework that enables reliable assessment of the completeness of any large-scale single-cell profiling project, after which additional sequencing efforts do not anymore reveal new cellular clusters. Such estimates can serve as stoppage-points for the ongoing sequencing projects, hence guiding a more cost-efficient completion of the profiling of various human tissues. Single-cell RNA sequencing is being widely used to catalogue differentiated cell types across tissues, but methods to evaluate how completely these cellular populations have been sampled remain limited. Here, the authors adapt statistical approaches from ecological species-richness estimation to single-cell datasets, developing a framework to assess profiling completeness and showing how it can identify points at which additional sequencing is unlikely to reveal new cellular clusters.