CellCover Captures Neural Stem Cell Progression in Mammalian Neocortical Development

Abstract

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 several genes at the same time. We wish 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.

CellBiAge: Improved single-cell age classification using data binarization

“…In methylation profiling, binarized single-cell methylomes, when coupled with bulk methylomes as references, have demonstrated the ability to predict cellular age with high accuracy. 23 Furthermore, binarization can preserve biological heterogeneity while alleviating noise embedded in RNA-seq data at the bulk 48 , 49 and single-cell resolutions, 50 , 51 yet its potential for age group prediction has not been explored. In our workflow, binarization significantly enhanced performance in the classification task when combined with HVG feature selection.…”