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reComBat: batch-effect removal in large-scale multi-source gene-expression data integration.
Adamer, Michael F; Brüningk, Sarah C; Tejada-Arranz, Alejandro; Estermann, Fabienne; Basler, Marek; Borgwardt, Karsten.
Afiliação
  • Adamer MF; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
  • Brüningk SC; Swiss Institute for Bioinformatics (SIB), Lausanne 1015, Switzerland.
  • Tejada-Arranz A; Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
  • Estermann F; Swiss Institute for Bioinformatics (SIB), Lausanne 1015, Switzerland.
  • Basler M; Biozentrum, University of Basel, Basel 4056, Switzerland.
  • Borgwardt K; Biozentrum, University of Basel, Basel 4056, Switzerland.
Bioinform Adv ; 2(1): vbac071, 2022.
Article em En | MEDLINE | ID: mdl-36699372
Motivation: With the steadily increasing abundance of omics data produced all over the world under vastly different experimental conditions residing in public databases, a crucial step in many data-driven bioinformatics applications is that of data integration. The challenge of batch-effect removal for entire databases lies in the large number of batches and biological variation, which can result in design matrix singularity. This problem can currently not be solved satisfactorily by any common batch-correction algorithm. Results: We present reComBat, a regularized version of the empirical Bayes method to overcome this limitation and benchmark it against popular approaches for the harmonization of public gene-expression data (both microarray and bulkRNAsq) of the human opportunistic pathogen Pseudomonas aeruginosa. Batch-effects are successfully mitigated while biologically meaningful gene-expression variation is retained. reComBat fills the gap in batch-correction approaches applicable to large-scale, public omics databases and opens up new avenues for data-driven analysis of complex biological processes beyond the scope of a single study. Availability and implementation: The code is available at https://github.com/BorgwardtLab/reComBat, all data and evaluation code can be found at https://github.com/BorgwardtLab/batchCorrectionPublicData. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article