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The Resource Statistical methods for microarray data analysis : methods and protocols, edited by Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile, (electronic book)

Statistical methods for microarray data analysis : methods and protocols, edited by Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile, (electronic book)

Contributor
Summary
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
Language
eng
Work
Publication
Extent
1 online resource (xi, 212 pages)
Contents
  • Using of normalizations for gene expression analysis
  • Peter Bubeliny
  • Constructing multivariate prognostic gene signatures with censored survival data
  • Derick R. Peterson
  • Clustering of gene expression data via normal mixture models
  • G.J. McLachlan [and others]
  • Network-based analysis of multivariate gene expression data
  • Wei Zhi [and others]
  • Genomic outlier detection in high-throughput data analysis
  • Debashis Ghosh
  • What statisticians should know about microarray gene expression technology
  • Impact of experimental noise and annotation imprecision on data quality in microarray experiments
  • Andreas Scherer, Manhong Dai, and Fan Meng
  • Aggregation effect in microarray data analysis
  • Linlin Chen, Anthony Almudevar, and Lev Klebanov
  • Test for normality of the gene expression data
  • Bobosharif Shokirov
  • Stephen Welle
  • Where statistics and molecular microarray experiments biology meet
  • Diana M. Kelmansky
  • Multiple hypothesis testing : a methodological overview
  • Anthony Almudevar
  • Gene selection with the [delta]-sequence method
  • Xing Qiu and Lev Klebanov
Isbn
9781603273367
Instance
Label
Statistical methods for microarray data analysis : methods and protocols
Title
Statistical methods for microarray data analysis
Title remainder
methods and protocols
Statement of responsibility
edited by Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile
Contributor
Subject
Language
eng
Summary
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
Member of
Cataloging source
GW5XE
Dewey number
572.8/636
Illustrations
illustrations
Index
index present
LC call number
QP624.5.D726
LC item number
S73 2013
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorDate
  • 1944-
  • 1946-
  • 1968-
http://library.link/vocab/relatedWorkOrContributorName
  • Yakovlev, Andrej Yu.
  • Klebanov, L. B.
  • Gaile, Daniel
Series statement
Methods in molecular biology, 1940-6029
Series volume
972
http://library.link/vocab/subjectName
  • DNA microarrays
  • Oligonucleotide Array Sequence Analysis
  • Statistics as Topic
  • Microarray Analysis
Label
Statistical methods for microarray data analysis : methods and protocols, edited by Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile, (electronic book)
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Using of normalizations for gene expression analysis
  • Peter Bubeliny
  • Constructing multivariate prognostic gene signatures with censored survival data
  • Derick R. Peterson
  • Clustering of gene expression data via normal mixture models
  • G.J. McLachlan [and others]
  • Network-based analysis of multivariate gene expression data
  • Wei Zhi [and others]
  • Genomic outlier detection in high-throughput data analysis
  • Debashis Ghosh
  • What statisticians should know about microarray gene expression technology
  • Impact of experimental noise and annotation imprecision on data quality in microarray experiments
  • Andreas Scherer, Manhong Dai, and Fan Meng
  • Aggregation effect in microarray data analysis
  • Linlin Chen, Anthony Almudevar, and Lev Klebanov
  • Test for normality of the gene expression data
  • Bobosharif Shokirov
  • Stephen Welle
  • Where statistics and molecular microarray experiments biology meet
  • Diana M. Kelmansky
  • Multiple hypothesis testing : a methodological overview
  • Anthony Almudevar
  • Gene selection with the [delta]-sequence method
  • Xing Qiu and Lev Klebanov
Control code
SPR828148223
Dimensions
unknown
Extent
1 online resource (xi, 212 pages)
File format
unknown
Form of item
online
Isbn
9781603273367
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
Statistical methods for microarray data analysis : methods and protocols, edited by Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile, (electronic book)
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Using of normalizations for gene expression analysis
  • Peter Bubeliny
  • Constructing multivariate prognostic gene signatures with censored survival data
  • Derick R. Peterson
  • Clustering of gene expression data via normal mixture models
  • G.J. McLachlan [and others]
  • Network-based analysis of multivariate gene expression data
  • Wei Zhi [and others]
  • Genomic outlier detection in high-throughput data analysis
  • Debashis Ghosh
  • What statisticians should know about microarray gene expression technology
  • Impact of experimental noise and annotation imprecision on data quality in microarray experiments
  • Andreas Scherer, Manhong Dai, and Fan Meng
  • Aggregation effect in microarray data analysis
  • Linlin Chen, Anthony Almudevar, and Lev Klebanov
  • Test for normality of the gene expression data
  • Bobosharif Shokirov
  • Stephen Welle
  • Where statistics and molecular microarray experiments biology meet
  • Diana M. Kelmansky
  • Multiple hypothesis testing : a methodological overview
  • Anthony Almudevar
  • Gene selection with the [delta]-sequence method
  • Xing Qiu and Lev Klebanov
Control code
SPR828148223
Dimensions
unknown
Extent
1 online resource (xi, 212 pages)
File format
unknown
Form of item
online
Isbn
9781603273367
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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