Species-Agnostic Transfer Learning for Cross-species Transcriptomics Data Integration without Gene Orthology

Abstract

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role, particularly in studying disease mechanisms and treatment responses. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete, particularly for non-model organisms, and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We built on the cross-domain structure-preserving projection and extended the algorithm toward out-of-sample prediction, a common challenge in biomedical sequencing data. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO biological processes amongst the most influential genes composing the latent space for species integration. Subsequently, this enables the identification and functional annotation of genes missing from public orthology databases. Finally, we evaluated our approach with four different single-cell sequencing datasets focusing on out-of-sample prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms all related methods working without prior knowledge when predicting unseen cell types based on other species data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.