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SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection
Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, pa...
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| Publié dans: | Brief Bioinform |
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| Auteurs principaux: | , , , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Oxford University Press
2020
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8324985/ https://ncbi.nlm.nih.gov/pubmed/32591778 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bib/bbaa097 |
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