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SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.
Gao, Qi; Ji, Zhicheng; Wang, Liuyang; Owzar, Kouros; Li, Qi-Jing; Chan, Cliburn; Xie, Jichun.
Afiliación
  • Gao Q; Department of Biostatistics and Bioinformatics, Duke University, USA.
  • Ji Z; Department of Biostatistics and Bioinformatics, Duke University, USA.
  • Wang L; Department of Molecular Genetics and Microbiology, Duke University, USA.
  • Owzar K; Department of Biostatistics and Bioinformatics, Duke University, USA.
  • Li QJ; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore.
  • Chan C; Singapore Immunology Network, Agency for Science, Technology and Research, Singapore.
  • Xie J; Department of Biostatistics and Bioinformatics, Duke University, USA.
Nucleic Acids Res ; 52(9): e46, 2024 May 22.
Article en En | MEDLINE | ID: mdl-38647069
ABSTRACT
SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Nucleic Acids Res Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido