Abstract
<jats:p>Single cell gene expression profiling provides a rich and systematic characterization of complex systems which revolutionized the way biologists can interrogate transcriptomes. However, the amount and complexity of data generated by such methods poses new challenges for biologists who are trying to extract detailed insights on the genetic programs that drive cellular functions and differentiation. We propose a radically different approach to the analysis of scRNAseq data that provides much more intuitive understanding of cell specific gene expression programs. These high coverage cells, were found in all five scRNA-seq datasets we investigated and constitute direct quantitative experimental observations of the mRNA content of individual cells. We focussed on ~4500 individual cells with coverage ranging from 15,000 to hundreds of thousands of Unique Molecular Identifiers. Clustering these cells using a supervised strategy we devised allowed to describe cell specific mRNA contents with unprecedented resolution. We identified genes that are dominating cell specific transcriptomes as well as low expression genes that are restricted to particular cell types but are all but undetectable at lower coverage threshold. For each cell type (or subtype) we characterized, we identified a set of genes with expression restricted to those cells that were not previously associated with the corresponding tissue.</jats:p>