Cytocipher determines significantly different populations of cells in single-cell RNA-seq data.

Abstract

Identification of cell types using single-cell RNA-seq is revolutionizing the study of multicellular organisms. However, typical single-cell RNA-seq analysis often involves post hoc manual curation to ensure clusters are transcriptionally distinct, which is time-consuming, error-prone, and irreproducible. To overcome these obstacles, we developed Cytocipher, a bioinformatics method and scverse compatible software package that statistically determines significant clusters. Application of Cytocipher to normal tissue, development, disease, and large-scale atlas data reveals the broad applicability and power of Cytocipher to generate biological insights in numerous contexts. This included the identification of cell types not previously described in the datasets analysed, such as CD8+ T cell subtypes in human peripheral blood mononuclear cells; cell lineage intermediate states during mouse pancreas development; and subpopulations of luminal epithelial cells over-represented in prostate cancer. Cytocipher also scales to large datasets with high-test performance, as shown by application to the Tabula Sapiens Atlas representing >480 000 cells. Cytocipher is a novel and generalizable method that statistically determines transcriptionally distinct and programmatically reproducible clusters from single-cell data. The software version used for this manuscript has been deposited on Zenodo (https://doi.org/10.5281/zenodo.8089546), and is also available via github (https://github.com/BradBalderson/Cytocipher).