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.

单细胞 RNA 测序数据显着推进了细胞类型多样性和组成的表征。然而，细胞类型的定义因数据和分析流程而异，引发了人们对细胞类型有效性和普遍性的担忧。通过 MetaNeighbor，我们提出了一种有效且稳健的细胞类型可复制性量化方法，可保持数据集独立性，并且与数据集集成相比具有高度可扩展性。在此协议中，我们展示了如何使用 MetaNeighbor 通过遵循简单的三步程序来表征细胞类型的可复制性：基因过滤、邻居投票和可视化。我们展示了如何定制这些步骤来量化细胞类型的可复制性，确定有助于细胞类型识别的基因集，并根据参考分类法预训练模型以快速评估新生成的数据。该协议基于 Bioconductor 和 GitHub 上提供的开源 R 软件包，需要基本熟悉 Rstudio 或 R 命令行，通常可以在 <5 分钟内运行数百万个细胞。