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E-raamat: Statistical Methods for Microarray Data Analysis: Methods and Protocols

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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.

Arvustused

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)

Preface v
Contributors xi
1 What Statisticians Should Know About Microarray Gene Expression Technology
1(14)
Stephen Welle
2 Where Statistics and Molecular Microarray Experiments Biology Meet
15(22)
Diana M. Kelmansky
3 Multiple Hypothesis Testing: A Methodological Overview
37(20)
Anthony Almudevar
4 Gene Selection with the δ-Sequence Method
57(16)
Xing Qiu
Lev Klebanov
5 Using of Normalizations for Gene Expression Analysis
73(12)
Peter Bubeliny
6 Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data
85(18)
Derick R. Peterson
7 Clustering of Gene Expression Data Via Normal Mixture Models
103(18)
G.J. McLachlan
L.K. Flack
S.K. Ng
K. Wang
8 Network-Based Analysis of Multivariate Gene Expression Data
121(20)
Wei Zhi
Jane Minturn
Eric Rappaport
Garrett Brodeur
Hongzhe Li
9 Genomic Outlier Detection in High-Throughput Data Analysis
141(14)
Debashis Ghosh
10 Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments
155(22)
Andreas Scherer
Manhong Dai
Fan Meng
11 Aggregation Effect in Microarray Data Analysis
177(16)
Linlin Chen
Anthony Almudevar
Lev Klebanov
12 Test for Normality of the Gene Expression Data
193(16)
Bobosharif Shokirov
Index 209