RT Journal Article
SR Electronic
T1 Optimal marker gene selection for cell type discrimination in single cell analyses
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 599654
DO 10.1101/599654
A1 Bianca Dumitrascu
A1 Soledad Villar
A1 Dustin G. Mixon
A1 Barbara E. Engelhardt
YR 2019
UL http://biorxiv.org/content/early/2019/04/04/599654.abstract
AB Single-cell technologies characterize complex cell populations across multiple data modalities at un-precedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers to identify and differentiate specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGene-Fit selects gene transcript markers that jointly optimize cell label recovery using label-aware compressive classification methods, resulting in a substantially more robust and less redundant set of markers than existing methods. When applied to a data set given a hierarchy of cell type labels, the markers found by our method enable the recovery of the label hierarchy through a computationally efficient and principled optimization.