Digital PCR Partition Classification.

BACKGROUND: Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative p...

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Publicado en:Clinical Chemistry Vol. 69; no. 9; pp. 976 - 991
Autores principales: Vynck, Matthijs, Yao Chen, Gleerup, David, Vandesompele, Jo, Trypsteen, Wim, Lievens, Antoon, Thas, Olivier, De Spiegelaere, Ward
Formato: Journal Article
Publicado: Oxford University Press / USA Sep2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2023
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      pub: Oxford University Press / USA
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          Vynck, Matthijs
          Yao Chen
          Gleerup, David
          Vandesompele, Jo
          Trypsteen, Wim
          Lievens, Antoon
          Thas, Olivier
          De Spiegelaere, Ward
        affil: Digital PCR Consortium, Ghent University, Ghent, Belgium
      sug:
      ab: BACKGROUND: Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods. CONTENT: This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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