GSE115889
A Rank-based Semblance kernel over Probability Spaces
Purpose: scRNA-seq allows disocvery of novel cell types. Here, we design a kernel method to discover niche cell populations, and show that our kernel provides encoruaging results on a dataset of retinal horizontal cells Methods: Retinal mRNA profiles of Horizontal cells from 10-week-old wild-type (WT) Black 6 mouse were generated by scRNA sequencing. The data was normalized using Seurat. Results: Using an optimized data analysis workflow, we found the Semblance yielded two populations with the retinal horizontal cells, and the second population was found to have unique metabolic properties. Conclusions: Our kernel method providees a unique framework to study cell-to-cell similarity. Overall design: Retinal Horizontal cell mRNA datafrom a 10-week old wild type (WT) aBlack 6 mouse.