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Large-scale correlation network construction for unraveling the coordination of complex biological systems.
Becker, Martin; Nassar, Huda; Espinosa, Camilo; Stelzer, Ina A; Feyaerts, Dorien; Berson, Eloise; Bidoki, Neda H; Chang, Alan L; Saarunya, Geetha; Culos, Anthony; De Francesco, Davide; Fallahzadeh, Ramin; Liu, Qun; Kim, Yeasul; Maric, Ivana; Mataraso, Samson J; Payrovnaziri, Seyedeh Neelufar; Phongpreecha, Thanaphong; Ravindra, Neal G; Stanley, Natalie; Shome, Sayane; Tan, Yuqi; Thuraiappah, Melan; Xenochristou, Maria; Xue, Lei; Shaw, Gary; Stevenson, David; Angst, Martin S; Gaudilliere, Brice; Aghaeepour, Nima.
Afiliación
  • Becker M; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Nassar H; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Espinosa C; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Stelzer IA; Department of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany.
  • Feyaerts D; These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa.
  • Berson E; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Bidoki NH; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Chang AL; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Saarunya G; These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa.
  • Culos A; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • De Francesco D; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Fallahzadeh R; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Liu Q; These authors contributed equally: Martin Becker, Huda Nassar, Camilo Espinosa.
  • Kim Y; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Maric I; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Mataraso SJ; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Payrovnaziri SN; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Phongpreecha T; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Ravindra NG; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Stanley N; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Shome S; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Tan Y; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Thuraiappah M; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Xenochristou M; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Xue L; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Shaw G; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Stevenson D; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Angst MS; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Gaudilliere B; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
  • Aghaeepour N; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
Nat Comput Sci ; 3(4): 346-359, 2023 Apr.
Article en En | MEDLINE | ID: mdl-38116462
ABSTRACT
Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Comput Sci Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nat Comput Sci Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos