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Addressing confounding artifacts in reconstruction of gene co-expression networks

Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We de...

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Detalles Bibliográficos
Autores principales: Parsana, Princy, Ruberman, Claire, Jaffe, Andrew E., Schatz, Michael C., Battle, Alexis, Leek, Jeffrey T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521369/
https://www.ncbi.nlm.nih.gov/pubmed/31097038
http://dx.doi.org/10.1186/s13059-019-1700-9
Descripción
Sumario:Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1700-9) contains supplementary material, which is available to authorized users.