@inbook{1509b2c47ed44e3ea9497404ccb6d136,
title = "Microarray Data Preprocessing: From Experimental Design to Differential Analysis",
abstract = "DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.",
keywords = "Batch effect, Differential analysis, DNA methylation, Experimental design, Gene expression, Microarray, Normalization, Omics data analysis, Preprocessing",
author = "Antonio Federico and Saarim{\"a}ki, \{Laura Aliisa\} and Angela Serra and \{del Giudice\}, Giusy and Kinaret, \{Pia Anneli Sofia\} and Giovanni Scala and Dario Greco",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2022",
doi = "10.1007/978-1-0716-1839-4\_7",
language = "English",
isbn = "978-1-0716-1841-7",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "79--100",
editor = "Giuseppe Agapito",
booktitle = "Microarray Data Analysis",
address = "United States",
}