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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
  2. Altmetric Badge
    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.
  4. Altmetric Badge
    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 3: Multiple hypothesis testing: a methodological overview.
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Chapter title
Multiple hypothesis testing: a methodological overview.
Chapter number 3
Book title
Statistical Methods for Microarray Data Analysis
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-60327-337-4_3
Pubmed ID
23385530
Book ISBNs
978-1-60327-336-7, 978-1-60327-337-4
Authors

Anthony Almudevar

Editors

Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile

Abstract

The process of screening for differentially expressed genes using microarray samples can usually be reduced to a large set of statistical hypothesis tests. In this situation, statistical issues arise which are not encountered in a single hypothesis test, related to the need to identify the specific hypotheses to be rejected, and to report an associated error. As in any complex testing problem, it is rarely the case that a single method is always to be preferred, leaving the analysts with the problem of selecting the most appropriate method for the particular task at hand. In this chapter, an introduction to current multiple testing methodology was presented, with the objective of clarifying the methodological issues involved, and hopefully providing the reader with some basis with which to compare and select methods.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 43%
Student > Ph. D. Student 2 29%
Professor 1 14%
Librarian 1 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 29%
Environmental Science 1 14%
Mathematics 1 14%
Agricultural and Biological Sciences 1 14%
Immunology and Microbiology 1 14%
Other 1 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 February 2013.
All research outputs
#15,263,666
of 22,696,971 outputs
Outputs from Methods in molecular biology
#5,294
of 13,046 outputs
Outputs of similar age
#181,492
of 280,813 outputs
Outputs of similar age from Methods in molecular biology
#176
of 342 outputs
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