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 Lanlan Ji, An Wang, and Don Geman our CellCover paper is available on biorxiv.

You can explore all the novel and public datasets we use in this report at NeMO Analytics one gene at a time: HERE, or via the covering set markers we dissected from scRNA-seq data inneocortical development: HERE.