Print Email Facebook Twitter Cell type matching across species using protein embeddings and transfer learning Title Cell type matching across species using protein embeddings and transfer learning Author Biharie, K. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Michielsen, L.C.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2023 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. Afterward, TACTiCS uses transfer learning to propagate 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, our model outperforms Seurat and the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better cell type matches than BLAST in our model. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:4bc32597-2b55-4cbe-8c44-09bcdf4738b6 DOI https://doi.org/10.1093/bioinformatics/btad248 ISSN 1367-4803 Source Bioinformatics, 39 (1 S), i404-i412 Part of collection Institutional Repository Document type journal article Rights © 2023 K. Biharie, L.C.M. Michielsen, M.J.T. Reinders, A.M.E.T.A. Mahfouz Files PDF btad248.pdf 1.01 MB Close viewer /islandora/object/uuid:4bc32597-2b55-4cbe-8c44-09bcdf4738b6/datastream/OBJ/view