Estimating the completeness of large-scale single-cell sequencing projects
Abstract
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.
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BibTeX
@article{miihkinen2026estimating,
title = {{Estimating the completeness of large-scale single-cell sequencing projects}},
author = {Mitro Miihkinen and Yidian Chu and Sara Vakkilainen and Yevhen Akimov and Tero Aittokallio},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78221-5},
url = {https://www.nature.com/articles/s41467-026-78221-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Estimating the completeness of large-scale single-cell sequencing projects
AU - Mitro Miihkinen
AU - Yidian Chu
AU - Sara Vakkilainen
AU - Yevhen Akimov
AU - Tero Aittokallio
PY - 2026
DA - 2026/10/08
JO - Nature Communications
DO - 10.1038/s41467-026-78221-5
UR - https://www.nature.com/articles/s41467-026-78221-5
AB - 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.
SN - 2041-1723
ER -