CellCover for
cell type markers
CellCover implements a version of the minimal covering problem in binarized data to overcome the inherent sparsity in scRNA-seq data to define groups of genes that together identify cell classes with high precision. This process often includes many genes at lower expression levels that are ignored in differnetial expression based marker gene identification. Because low expressed genes have low sensitivity in distinguishing cell classes when used alone, they are not often used in differential expression based methods. However, together, panels of such genes achieve both high sensitivity and high specificity.
Lead by An Wang, Lanlan Ji, Larent Younes, and Don Geman our CellCover paper is available on biorxiv. Where we use this new method to explore the major cell types and developmental progression of neural progenitor cells in neocortical neurogenesis, with a specific focus on the evolution of outer radial glial cells across the mammalian lineage.
Links for the exploration of public data that we use in this report via nd which we have assembled at NeMO Analytics:
2] Projection of CellCover marker gene panels derived from radial glial cells 1hr, 24hr, and 96hr after final cell division (3 CellCover panels from Telley 2019).
3] Projection of CellCover marker gene panels derived from radial glial cells 1hr, 24hr, and 96hr after final cell division (12 CellCover panels from Telley 2019), further divided into cells labeled at E12-15.
4] Projection of differential expression marker genes derived from radial glial cells 1hr, 24hr, and 96hr after final cell division (12 DEG panels from Telley 2019), further divided into cells labeled at E12-15.
5] Projection of CellCover marker gene panels derived from sorted cell types in the human fetal brain (CellCover panels from Lui 2022).