Statistical methods for microarray data analysis : methods and protocols
Author(s)
Bibliographic Information
Statistical methods for microarray data analysis : methods and protocols
（Methods in molecular biology / John M. Walker, series editor, v. 972）（Springer protocols）
Humana Press, c2013
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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 (TM) 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.
Table of Contents
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 MethodXing Qiu and Lev B Klebanov
5. Using of Normalizations for Gene Expression Analysis Peter Bubeliny
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
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