Context-aware comparison of cell type gene expression across species

Abstract

AbstractComputational comparison of single cell expression profiles cross-species uncovers functional similarities and differences between cell types. Importantly, it offers the potential to refine evolutionary relationships based on gene expression. Current analysis strategies are limited by the strong hypothesis of ortholog conjecture, and lose expression information given by non-orthologs. To address this, we devise a novel analytical framework that redefines the analysis paradigm. This framework robustly classifies genes by expression specificity and distribution across cell types, allowing for a dataset-specific reassessment of the ortholog conjecture by evaluating the degree of ortholog class conservation. We utilise the gene classes to decode species effects on cross-species transcriptomics space, and compare sequence conservation with expression specificity similarity across different types of orthologs. We develop contextualised cell type similarity measurements while considering species-unique genes and non-one-to-one orthologs. Finally, we consolidate gene classification results into a knowledge graph, allowing hierarchical depiction of cell types and orthologous groups, and continuous integration of new data.