Literature DB >> 34902124

Microarray Data Preprocessing: From Experimental Design to Differential Analysis.

Antonio Federico1,2,3, Laura Aliisa Saarimäki1,2,3, Angela Serra1,2,3, Giusy Del Giudice1,2,3, Pia Anneli Sofia Kinaret1,2,3,4, Giovanni Scala5, Dario Greco6,7,8,9.   

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.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Batch effect; DNA methylation; Differential analysis; Experimental design; Gene expression; Microarray; Normalization; Omics data analysis; Preprocessing

Mesh:

Year:  2022        PMID: 34902124     DOI: 10.1007/978-1-0716-1839-4_7

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  42 in total

Review 1.  Microarray data normalization and transformation.

Authors:  John Quackenbush
Journal:  Nat Genet       Date:  2002-12       Impact factor: 38.330

2.  Normalization of RNA-seq data using factor analysis of control genes or samples.

Authors:  Davide Risso; John Ngai; Terence P Speed; Sandrine Dudoit
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

Review 3.  Applications of DNA microarrays in biology.

Authors:  Roland B Stoughton
Journal:  Annu Rev Biochem       Date:  2005       Impact factor: 23.643

Review 4.  Microarray data quality - review of current developments.

Authors:  Timothy Wilkes; Holger Laux; Carole A Foy
Journal:  OMICS       Date:  2007

5.  User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org.

Authors:  Lars M T Eijssen; Magali Jaillard; Michiel E Adriaens; Stan Gaj; Philip J de Groot; Michael Müller; Chris T Evelo
Journal:  Nucleic Acids Res       Date:  2013-04-24       Impact factor: 16.971

6.  Empirical comparison of cross-platform normalization methods for gene expression data.

Authors:  Jason Rudy; Faramarz Valafar
Journal:  BMC Bioinformatics       Date:  2011-12-07       Impact factor: 3.169

7.  arrayQualityMetrics--a bioconductor package for quality assessment of microarray data.

Authors:  Audrey Kauffmann; Robert Gentleman; Wolfgang Huber
Journal:  Bioinformatics       Date:  2008-12-23       Impact factor: 6.937

8.  eUTOPIA: solUTion for Omics data PreprocessIng and Analysis.

Authors:  Veer Singh Marwah; Giovanni Scala; Pia Anneli Sofia Kinaret; Angela Serra; Harri Alenius; Vittorio Fortino; Dario Greco
Journal:  Source Code Biol Med       Date:  2019-01-29

9.  Quality control in microarray assessment of gene expression in human airway epithelium.

Authors:  Tina Raman; Timothy P O'Connor; Neil R Hackett; Wei Wang; Ben-Gary Harvey; Marc A Attiyeh; David T Dang; Matthew Teater; Ronald G Crystal
Journal:  BMC Genomics       Date:  2009-10-24       Impact factor: 3.969

10.  Exploratory methods for checking quality of microarray data.

Authors:  Eun-Kyung Lee; Taesung Park
Journal:  Bioinformation       Date:  2007-04-10
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  2 in total

Review 1.  Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome's Dark Matter.

Authors:  Ahmad Almatroudi
Journal:  Int J Mol Sci       Date:  2022-04-17       Impact factor: 6.208

2.  Ten quick tips for biomarker discovery and validation analyses using machine learning.

Authors:  Ramon Diaz-Uriarte; Elisa Gómez de Lope; Rosalba Giugno; Holger Fröhlich; Petr V Nazarov; Isabel A Nepomuceno-Chamorro; Armin Rauschenberger; Enrico Glaab
Journal:  PLoS Comput Biol       Date:  2022-08-11       Impact factor: 4.779

  2 in total

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