BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data
Abstract Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentati...
I tiakina i:
| Ngā kaituhi matua: | , , , , , , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Nature Portfolio
2024-01-01
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| Rangatū: | Nature Communications |
| Urunga tuihono: | https://doi.org/10.1038/s41467-023-44560-w |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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