The ability to predict gene-expression landscapes at single-cell resolution has long been a challenge in the field of genomics. We mapped whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila, and earthworm using Microwell-seq. We propose the first sequence-based model, Nvwa, that can predict gene expression at single-cell resolution directly from genomic sequences.
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This is a summary of: Li, J. et al. Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nat. Genet. https://doi.org/10.1038/s41588-022-01197-7 (2022).
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Deciphering single-cell transcriptional programs across species. Nat Genet 54, 1595–1596 (2022). https://doi.org/10.1038/s41588-022-01198-6
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DOI: https://doi.org/10.1038/s41588-022-01198-6
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