Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor.

ABSTRACT: 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 ref

SUBMITTER: Fischer S 

PROVIDER: S-EPMC8826496 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature