Skip to main content
SpringerLink
Log in

Statistical Methods for Microarray Data Analysis

Methods and Protocols

  • Book
  • © 2013

Overview

Editors:
  1. Andrei Y. Yakovlev

    1. School of Medicine & Dentistry, Dept. Biostatistics & Computational, University of Rochester, Rochester, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Lev Klebanov

    1. , Department of Probability and Statistics, Charles University, Prague, Czech Republic

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Daniel Gaile

    1. State University of New York at Buffalo, Buffalo, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Aids scientists in continuing to study microarrays and the most current statistical methods
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 972)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. What Statisticians Should Know About Microarray Gene Expression Technology

    • Stephen Welle
    Pages 1-13

  3. Where Statistics and Molecular Microarray Experiments Biology Meet

    • Diana M. Kelmansky
    Pages 15-35

  4. Multiple Hypothesis Testing: A Methodological Overview

    • Anthony Almudevar
    Pages 37-55

  5. Gene Selection with the δ-Sequence Method

    • Xing Qiu, Lev Klebanov
    Pages 57-71

  6. Using of Normalizations for Gene Expression Analysis

    • Peter Bubelíny
    Pages 73-83

  7. Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data

    • Derick R. Peterson
    Pages 85-101

  8. Clustering of Gene Expression Data Via Normal Mixture Models

    • G. J. McLachlan, L. K. Flack, S. K. Ng, K. Wang
    Pages 103-119

  9. Network-Based Analysis of Multivariate Gene Expression Data

    • Wei Zhi, Jane Minturn, Eric Rappaport, Garrett Brodeur, Hongzhe Li
    Pages 121-139

  10. Genomic Outlier Detection in High-Throughput Data Analysis

    • Debashis Ghosh
    Pages 141-153

  11. Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments

    • Andreas Scherer, Manhong Dai, Fan Meng
    Pages 155-176

  12. Aggregation Effect in Microarray Data Analysis

    • Linlin Chen, Anthony Almudevar, Lev Klebanov
    Pages 177-191

  13. Test for Normality of the Gene Expression Data

    • Bobosharif Shokirov
    Pages 193-208

  14. Back Matter

    Pages 209-212
    Download chapter PDF
Back to top

Keywords

About this book

Microarrays for simultaneous measurement of redundancy  of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular Biology™ series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory.

 

Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study  microarrays and the most current statistical methods.

Reviews

“This book covers a broad range of topics, from the normalization of expression levels to the evaluation of experimental noise or the identification of putative networks through either multivariate analysis approach or clustering. … It is therefore appropriate for research students and post-docs as well as lecturers looking for handson examples.” (Irina Ioana Mohorianu, zbMATH 1312.92006, 2015)

Editors and Affiliations

  • School of Medicine & Dentistry, Dept. Biostatistics & Computational, University of Rochester, Rochester, USA

    Andrei Y. Yakovlev

  • , Department of Probability and Statistics, Charles University, Prague, Czech Republic

    Lev Klebanov

  • State University of New York at Buffalo, Buffalo, USA

    Daniel Gaile

Bibliographic Information

  • Book Title: Statistical Methods for Microarray Data Analysis

  • Book Subtitle: Methods and Protocols

  • Editors: Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-60327-337-4

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media New York 2013

  • Hardcover ISBN: 978-1-60327-336-7Published: 06 February 2013

  • Softcover ISBN: 978-1-4939-5079-9Published: 23 August 2016

  • eBook ISBN: 978-1-60327-337-4Published: 11 February 2013

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 212

  • Topics: Human Genetics, Microarrays

Publish with us

Policies and ethics

Back to top

Search

Navigation