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

Megan Crow, Anirban Paul, Sara Ballouz, Z. Josh Huang, Jesse Gillis

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number884
JournalNature communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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