CuBlock : a cross-platform normalization method for gene-expression microarrays
Junet, Valentin (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Farrés, Judith (Anaxomics Biotech SL)
Mas, José M. (Anaxomics Biotech SL)
Daura i Ribera, Xavier (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")

Fecha:	2021
Resumen:	Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (), UPC (), YuGene (), DBNorm (), Shambhala () and a simple log transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. CuBlock can be downloaded from . are available at Bioinformatics online.
Ayudas:	European Commission 765158
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Bioinformatics, Vol. 37, Issue 16 (August 2021) , p. 2365-2373, ISSN 1367-4811

DOI: 10.1093/bioinformatics/btab105
PMID: 33609102

9 p, 748.7 KB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Instituto de Biotecnología y de Biomedicina (IBB)
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