Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor

Single-cell RNA-sequencing data have significantly advanced the characterization of cell-type diversity and composition. However, cell-type definitions vary across data and analysis pipelines, raising concerns about cell-type validity and generalizability. With MetaNeighbor, we proposed an efficient and robust quantification of cell-type replicability that preserves dataset independence and is highly scalable compared to dataset integration. In this protocol, we show how MetaNeighbor can be used to characterize cell-type replicability by following a simple three-step procedure: gene filtering, neighbor voting and visualization. We show how these steps can be tailored to quantify cell-type replicability, determine gene sets that contribute to cell-type identity and pretrain a model on a reference taxonomy to rapidly assess newly generated data. The protocol is based on an open-source R package available from Bioconductor and GitHub, requires basic familiarity with Rstudio or the R command line and can typically be run in <5 min for millions of cells. This protocol provides step-by-step instructions for using MetaNeighbor, a computational tool that allows quantification of cell-type replicability across single-cell transcriptomic datasets and identifies the gene sets that contribute to cell identity.

Automated summary

The protocol introduces an efficient and robust quantification method for cell-type replicability using MetaNeighbor, which allows for identification of gene sets contributing to cell identity. The method is based on an open-source R package and can typically be run in less than 5 minutes for millions of cells.