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SciBet as a portable and fast single cell type identifier

Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advanta...

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Detalles Bibliográficos
Autores principales: Li, Chenwei, Liu, Baolin, Kang, Boxi, Liu, Zedao, Liu, Yedan, Chen, Changya, Ren, Xianwen, Zhang, Zemin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156687/
https://www.ncbi.nlm.nih.gov/pubmed/32286268
http://dx.doi.org/10.1038/s41467-020-15523-2
Descripción
Sumario:Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell type identification.