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2019 | OriginalPaper | Buchkapitel

A Reliable Method to Remove Batch Effects Maintaining Group Differences in Lymphoma Methylation Case Study

verfasst von : Giulia Pontali, Luciano Cascione, Alberto J. Arribas, Andrea Rinaldi, Francesco Bertoni, Rosalba Giugno

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer International Publishing

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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.

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Fußnoten
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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Metadaten
Titel
A Reliable Method to Remove Batch Effects Maintaining Group Differences in Lymphoma Methylation Case Study
verfasst von
Giulia Pontali
Luciano Cascione
Alberto J. Arribas
Andrea Rinaldi
Francesco Bertoni
Rosalba Giugno
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-17935-9_3