Statistical Methods for Microarray Data Analysis

Methods and Protocols
2013
ISBN: 978-1-60327-336-7
Verlag: Springer

Buch, Englisch, Band 972, 212 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 5709 g

Reihe: Methods in Molecular Biology

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Yakovlev, Andrei Y.

Gaile, Daniel

Klebanov, Lev

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Naturwissenschaften Biowissenschaften Molekularbiologie
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

1. What Statisticians Should Know About Microarray Gene Expression Technology

Stephen Welle

2. Where Statistics and Molecular Microarray Experiments Biology Meet

Diana M. Kelmansky

3. Multiple Hypothesis Testing: A Methodological Overview

Anthony Almudevar

4. Gene Selection with the d-sequence Method

Xing Qiu and Lev B Klebanov

 5. Using of Normalizations for Gene Expression Analysis

Peter Bubelíny

6. Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data

Derick R. Peterson

7. Clustering of Gene-Expression Data via Normal Mixture Models
G.J. McLachlan, L.K. Flack, S.K. Ng, and K. Wang

8. Network-based Analysis of Multivariate Gene Expression Data

Wei Zhi, Jane Minturn, Eric Rappaport, Garrett Brodeur, and Hongzhe Li

9. Genomic Outlier Detection in High-throughput Data Analysis

Debashis Ghosh

10. Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiment

Andreas Scherer, Manhong Dai, and Fan Meng

11. Aggregation Effect in Microarray Data Analysis

Linlin Chen, Anthony Almudevar and Lev Klebanov

12. Test for Normality of the Gene Expression Data

Bobosharif  Shokirov