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Valection: design optimization for validation and verification studies

BACKGROUND: Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testin...

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Detalles Bibliográficos
Autores principales: Cooper, Christopher I, Yao, Delia, Sendorek, Dorota H, Yamaguchi, Takafumi N, P’ng, Christine, Houlahan, Kathleen E, Caloian, Cristian, Fraser, Michael, Ellrott, Kyle, Margolin, Adam A, Bristow, Robert G, Stuart, Joshua M, Boutros, Paul C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157051/
https://www.ncbi.nlm.nih.gov/pubmed/30253747
http://dx.doi.org/10.1186/s12859-018-2391-z
Descripción
Sumario:BACKGROUND: Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. RESULTS: To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. CONCLUSIONS: Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2391-z) contains supplementary material, which is available to authorized users.