Kavli Affiliate: Donald Geman and Laurent Younes
| Authors: Lanlan Ji, An Wang, Shreyash Sonthalia, Daniel Q Naiman, Laurent Younes, Carlo Colantuoni and Donald Geman
| Summary:
Accurate identification of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Such analyses are often based on the existence of highly discriminating “marker genes” for specific cell classes which enables a deeper functional understanding of these classes as well as their identification in new, related datasets. Currently, marker genes are defined by methods that serially assess the level of differential expression (DE) of individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes, that can only be captured by analyzing multiple genes simultaneously. We aim to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing panel selection as a variation of the “minimal set-covering problem” in combinatorial optimization which can be solved with integer programming. In this formulation, the covering elements are genes, and the objects to be covered are cells of a particular class, where a cell is covered by a gene if that gene is expressed in that cell. Our method, CellCover, identifies a panel of marker genes in scRNA-seq data that covers one class of cells within a population. We apply this method to generate covering marker gene panels which characterize cells of the developing mouse neocortex as postmitotic neurons are generated from neural progenitor cells (NPCs). We show that CellCover captures cell-class-specific signals distinct from those defined by DE methods and that CellCover’s compact gene panels can be expanded to explore cell-type-specific function. Transfer learning experiments exploring these covering panels across in vivo mouse, primate, and human scRNA-seq datasets demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as markers of temporal progression in the molecular identity of these cell types across development of the mammalian neocortex. Exploring markers of human outer radial glia (oRG, or basal RG), we show that transcriptomic elements of this key cell type in the expansion of the human cortex appeared in gliogenic precursors of the rodent before the full program emerged in oRG of the primate lineage. We have assembled all the public datasets we used in this report at NeMO analytics [1] where the expression of individual genes can be visualized across all the datasets at NeMO Individual Genes. The gene covering panels we identify across cell types and developmental time can also be freely explored at NeMO: Telley 3 Sets Covering Panels, NeMO: Telley 12 Sets Covering Panels, and NeMO: Sorted Brain Cell Covering Panels. The packages for CellCover are available in CellCover R and CellCover Python.