Abstract
The amount of biological data is increasing and their analysis is becoming one of the most challenging topics in the information sciences. Before starting the analysis it is important to remove unwanted variability due to some factors such as: year of sequencing, laboratory conditions and use of different protocols. This is a crucial step because if the variability is not evaluated before starting the analysis of interest, the results may be undesirable and the conclusion can not be true. The literature suggests to use some valid mathematical models, but experience shows that applying these to high-throughput data with a non-uniform study design is not straightforward and in many cases it may introduce a false signal. Therefore it is necessary to develop models that allow to remove the effects that can negatively influence the study preserving biological meaning. In this paper we report a new case study related lymphoma methylation data and we propose a suitable pipeline for its analysis.
G. Pontali and L. Cascione—Equal contribution.
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Notes
- 1.
Bioconductor repository provides tools for analysis and comprehension of high-throughput genomic data. It has 1560 software packages. The current release of Bioconductor is version 3.7.
- 2.
Single-stranded fragments of DNA that are complementary to a gene.
- 3.
Pertaining to a chromosome that is not a sex chromosome.
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Acknowledgments
This work has been partially supported by the following projects: GNCS-INDAM, Fondo Sociale Europeo, and National Research Council Flagship Projects Interomics. This work has been partially supported by the project of the Italian Ministry of education, Universities and Research (MIUR) “Dipartimenti di Eccellenza 2018-2022".
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Pontali, G., Cascione, L., Arribas, A.J., Rinaldi, A., Bertoni, F., Giugno, R. (2019). A Reliable Method to Remove Batch Effects Maintaining Group Differences in Lymphoma Methylation Case Study. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_3
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