Abstract
Despite the increasing amount of available gene expression data, integrative analysis is still hindered by its high susceptibility to microenvironment fluctuations, resulting in inter-experiment variability known as batch effects. Therefore the development of data integration strategy is now more necessary than ever. Although several normalization algorithms have already been proposed, we believe that an effective model must rely on data migration between schemes. In this paper we apply this approach to a set of microarray data from core needle biopsy of breast cancers spanning different microarray platforms, and demonstrate its effectiveness in data preparation for unsupervised analysis and multiclass classification tasks. We propose a custom tool dedicated to defining the model structure. Additionally, we compare several pipelines of data processing, combining data normalization with different batch effect correction methods.
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Acknowledgement
This work was supported by Polish National Centre for Research and Development under Grant Strategmed2/267398/4/NCBR/2015 and Silesian University of Technology Grant 02/010/BK_18/0102 and by Polish Ministry of Science and Higher Education as part of the Implementation Doctorate program at the Silesian University of Technology, Gliwice, Poland (contract No 10/DW/2017/01/1) (AP). Data analysis was partially carried out using the Biotest Platform developed within Project n. PBS3/B3/32/2015 financed by the Polish National Centre of Research and Development (NCBiR). Calculations were performed using the infrastructure supported by the computer cluster Ziemowit (www.ziemowit.hpc.polsl.pl) funded by the Silesian BIO-FARMA project No. POIG.02.01.00-00-166/08 and expanded in the POIG.02.03.01-00- 040/13 in the Computational Biology and Bioinformatics Laboratory of the Biotechnology Centre at the Silesian University of Technology. This work was partially supported by the Polish Ministry of Science and Higher Education as part of the Implementation Doctorate program at the Silesian University of Technology, Gliwice, Poland (contract No 10/DW/2017/01/1).
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Student, S., Płuciennik, A., Łakomiec, K., Wilk, A., Bensz, W., Fujarewicz, K. (2019). Integration Strategies of Cross-Platform Microarray Data Sets in Multiclass Classification Problem. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_48
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