Matrix Inversion and Subset Selection (MISS): A pipeline for mapping of diverse cell types across the murine brain

Abstract

Significance The current state-of-the-art mappings of cell types fall short regarding finely resolved subtypes of neural cells, especially γ-aminobutyric acidergic and glutamatergic subtypes. Most such maps compromise on either the number or specificity of unique cell types quantified in each study. Others only use qualitative validation for their maps and fail to address whether gene subset selection is necessary for optimal maps. The Matrix Inversion and Subset Selection pipeline uses publicly available in situ hybridization and single-cell RNA sequencing gene expression data to infer cell-type distributions to map diverse cell types across the murine brain. Most importantly, we demonstrate that data-driven feature selection is necessary to arrive at quantitatively optimal cell-type maps using inversion-, deconvolution-, and correlation-based mapping approaches.