Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.Single cell RNA-sequencing analysis poses challenges in replication due to technical biases and analytic variability among bioinformatics pipelines. Here, Crow et al develop MetaNeighbor for measuring cell-type replication across datasets, and use it to identify marker genes for neuron subtypes with evidence of replication.

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