PT  - JOURNAL ARTICLE
AU  - Megan Crow
AU  - Anirban Paul
AU  - Sara Ballouz
AU  - Z. Josh Huang
AU  - Jesse Gillis
TI  - Addressing the looming identity crisis in single cell RNA-seq
AID  - 10.1101/150524
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 150524
4099  - http://biorxiv.org/content/early/2017/06/16/150524.short
4100  - http://biorxiv.org/content/early/2017/06/16/150524.full
AB  - Single cell RNA-sequencing technology (scRNA-seq) provides a new avenue to discover and characterize cell types, but the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine the replicability of these studies. Meta-analysis of rapidly accumulating data is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that allows researchers to quantify the degree to which cell types replicate across datasets, and to rapidly identify clusters with high similarity for further testing. We first measure the replicability of neuronal identity by comparing more than 13 thousand individual scRNA-seq transcriptomes, then assess cross-dataset evidence for novel pyramidal neuron and cortical interneuron subtypes identified by scRNA-seq. We find that 24/45 cortical interneuron subtypes and 10/48 pyramidal neuron subtypes have evidence of replication in at least one other study. Identifying these putative replicates allows us to re-analyze the data for differential expression and provide lists of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types and subtypes with high accuracy, indicating many of the transcriptional changes characterizing cell identity are pervasive and easily detected.