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Microarray Data Analysis

  • Book
  • © 2022

Overview

Editors:
  1. Giuseppe Agapito

    1. Dipartimento di Giurisprudenza, Economia e Sociologia, Università degli Studi “Magna Graecia” di Catanzaro, Catanzaro, Italy

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  • Includes cutting-edge techniques
  • Provides step-by-step detail essential for reproducible results
  • Contain key implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2401)

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Table of contents (19 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Tools in Pharmacogenomics Biomarker Identification for Cancer Patients

    • Francesca Scionti, Maria Teresa Di Martino, Daniele Caracciolo, Licia Pensabene, Pierosandro Tagliaferri, Mariamena Arbitrio
    Pages 1-12

  3. High-Performance Framework to Analyze Microarray Data

    • Fabrizio Marozzo, Loris Belcastro
    Pages 13-27

  4. Web and Cloud Computing to Analyze Microarray Data

    • Barbara Calabrese
    Pages 29-38

  5. A Microarray Analysis Technique Using a Self-Organizing Multiagent Approach

    • Agostino Forestiero, Giuseppe Papuzzo, Rosaria De Simone, Rosa Varchera
    Pages 39-50

  6. Improving Analysis and Annotation of Microarray Data with Protein Interactions

    • Max Kotlyar, Serene W. H. Wong, Chiara Pastrello, Igor Jurisica
    Pages 51-68

  7. Algorithms to Preprocess Microarray Image Data

    • Paolo Zaffino, Maria Francesca Spadea
    Pages 69-78

  8. Microarray Data Preprocessing: From Experimental Design to Differential Analysis

    • Antonio Federico, Laura Aliisa Saarimäki, Angela Serra, Giusy del Giudice, Pia Anneli Sofia Kinaret, Giovanni Scala et al.
    Pages 79-100

  9. Supervised Methods for Biomarker Detection from Microarray Experiments

    • Angela Serra, Luca Cattelani, Michele Fratello, Vittorio Fortino, Pia Anneli Sofia Kinaret, Dario Greco
    Pages 101-120

  10. Unsupervised Algorithms for Microarray Sample Stratification

    • Michele Fratello, Luca Cattelani, Antonio Federico, Alisa Pavel, Giovanni Scala, Angela Serra et al.
    Pages 121-146

  11. Pathway Enrichment Analysis of Microarray Data

    • Chiara Pastrello, Yun Niu, Igor Jurisica
    Pages 147-159

  12. Network Analysis of Microarray Data

    • Alisa Pavel, Angela Serra, Luca Cattelani, Antonio Federico, Dario Greco
    Pages 161-186

  13. geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO)

    • Davide Chicco
    Pages 187-194

  14. Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19–Related Studies

    • Anna Bernasconi, Silvia Cascianelli
    Pages 195-215

  15. Alignment of Microarray Data

    • Francesco Cauteruccio
    Pages 217-237

  16. Integration of DNA Microarray with Clinical and Genomic Data

    • Francesca Scionti, Mariamena Arbitrio, Daniele Caracciolo, Licia Pensabene, Pierfrancesco Tassone, Pierosandro Tagliaferri et al.
    Pages 239-248

  17. Clustering Methods for Microarray Data Sets

    • Giuseppe Agapito, Giuseppe Fedele
    Pages 249-261

  18. Microarray Data Analysis Protocol

    • Giuseppe Agapito, Mariamena Arbitrio
    Pages 263-271

  19. Using Gene Ontology to Annotate and Prioritize Microarray Data

    • Marianna Milano
    Pages 273-287

  20. Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

    • Marzia Settino, Mario Cannataro
    Pages 289-314
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Keywords

About this book

This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility. 

Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets.

Editors and Affiliations

  • Dipartimento di Giurisprudenza, Economia e Sociologia, Università degli Studi “Magna Graecia” di Catanzaro, Catanzaro, Italy

    Giuseppe Agapito

Bibliographic Information

  • Book Title: Microarray Data Analysis

  • Editors: Giuseppe Agapito

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-1839-4

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

  • Hardcover ISBN: 978-1-0716-1838-7Published: 14 December 2021

  • Softcover ISBN: 978-1-0716-1841-7Published: 15 December 2022

  • eBook ISBN: 978-1-0716-1839-4Published: 13 December 2021

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 317

  • Number of Illustrations: 17 b/w illustrations, 54 illustrations in colour

  • Topics: Biological Techniques, Bioinformatics, Statistics, general

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