Cell type matching across species using protein embeddings and transfer learning
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.30.525343v1?rss=1

Authors: Biharie, K., Michielsen, L., Reinders, M. J. T., Mahfouz, A.

Abstract: Motivation: Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. Results: In this work, we present a model to Transfer and Align Cell Types in Cross-Species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterwards, TACTiCS uses transfer learning to propa-gate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, at a high resolution, our model outperforms the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better matches than BLAST, both in our model and SAMap. Availability: https://github.com/kbiharie/TACTiCS

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