Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity.

ABSTRACT: Defining cell types requires integrating diverse single-cell measurements from multiple experiments and biological contexts. To flexibly model single-cell datasets, we developed LIGER, an algorithm that delineates shared and dataset-specific features of cell identity. We applied it to four diverse and challenging analyses of human and mouse brain cells. First, we defined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis. Second, we analyzed expression in the human substantia nigra, comparing cell states in specific donors and relating cell types to those in the mouse. Third, we integrated in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we jointly defined mo

SUBMITTER: Welch JD 

PROVIDER: S-EPMC6716797 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature