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Statistical Methods for Microarray Data Analysis

Overview of attention for book
Cover of 'Statistical Methods for Microarray Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 What statisticians should know about microarray gene expression technology.
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    Chapter 2 Where statistics and molecular microarray experiments biology meet.
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    Chapter 3 Multiple hypothesis testing: a methodological overview.
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    Chapter 4 Gene Selection with the δ-Sequence Method.
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    Chapter 5 Using of normalizations for gene expression analysis.
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    Chapter 6 Constructing multivariate prognostic gene signatures with censored survival data.
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    Chapter 7 Clustering of gene expression data via normal mixture models.
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    Chapter 8 Network-based analysis of multivariate gene expression data.
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    Chapter 9 Genomic outlier detection in high-throughput data analysis.
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    Chapter 10 Impact of experimental noise and annotation imprecision on data quality in microarray experiments.
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    Chapter 11 Aggregation Effect in Microarray Data Analysis
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    Chapter 12 Test for normality of the gene expression data.
Attention for Chapter 5: Using of normalizations for gene expression analysis.
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Citations

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Chapter title
Using of normalizations for gene expression analysis.
Chapter number 5
Book title
Statistical Methods for Microarray Data Analysis
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-60327-337-4_5
Pubmed ID
23385532
Book ISBNs
978-1-60327-336-7, 978-1-60327-337-4
Authors

Peter Bubelíny

Editors

Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 100%

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