Your browser doesn't support javascript.
loading
Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.
Steen, Chloé B; Luca, Bogdan A; Alizadeh, Ash A; Gentles, Andrew J; Newman, Aaron M.
Afiliación
  • Steen CB; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Luca BA; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
  • Alizadeh AA; Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
  • Gentles AJ; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Newman AM; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
Methods Mol Biol ; 2629: 43-71, 2023.
Article en En | MEDLINE | ID: mdl-36929073
Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estado de Salud / Ecosistema Aspecto: Determinantes_sociais_saude / Patient_preference Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estado de Salud / Ecosistema Aspecto: Determinantes_sociais_saude / Patient_preference Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
...