LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data
LSH-GAN is a locality-sensitive hashing based generative adversarial model that can produce realistic cell samples from small sample single-cell scRNA-seq data. The generated cells can be utilized for downstream analysis, like gene selection and cell clustering.
I tiakina i:
| Ngā kaituhi matua: | , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Nature Portfolio
2022-06-01
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| Rangatū: | Communications Biology |
| Urunga tuihono: | https://doi.org/10.1038/s42003-022-03473-y |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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