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El. knyga: Statistics and Informatics in Molecular Cancer Research

Edited by (, Bioinformatics Research Center, University of Aarhus), Edited by (, Molecular Diagnostic Laboratory, University of Aarhus)
  • Formatas: PDF+DRM
  • Išleidimo metai: 18-Jun-2009
  • Leidėjas: Oxford University Press
  • Kalba: eng
  • ISBN-13: 9780191559778
Kitos knygos pagal šią temą:
Statistics and Informatics in Molecular Cancer ResearchDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 18-Jun-2009
  • Leidėjas: Oxford University Press
  • Kalba: eng
  • ISBN-13: 9780191559778
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Molecular understanding of cancer and cancer progression is at the forefront of many research programs today. High-throughput array technologies and other modern molecular techniques produce a wealth of molecular data about the structure, and function of cells, tissues, and organisms. Correctly analyzed and interpreted these data hold the promise of bringing new markers for prognostic and diagnostic use, for new treatment schemes, and of gaining new biological insight into the evolution of cancer and its molecular, pathological, and clinical consequences.

Aimed at graduates and researchers, this book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters from carefully chosen experts it brings the reader up to date with specific topics in cancer research, how the topics give rise to development of new informatics and statistics tools, and how the tools can be applied. The focus of the book is to give the reader an understanding of key concepts and tools, rather than focusing on technical issues.

A main theme is the extensive use of array technologies in modern cancer research - gene expression and exon arrays, SNP and copy number arrays, and methylation arrays - to derive quantitative and qualitative statements about cancer, its progression and aetiology, and to understand how these technologies on one hand allow us learn about cancer tissue as a complex system and on the other hand allow us to pinpoint key genes and events as crucial for the development of the disease.
Preface x
References xii
Association studies
1(24)
Emily Webb
Richard Houlston
Introduction
1(1)
Sequence variation and patterns of linkage disequilibrium in the genome
2(2)
Direct and indirect association studies
4(1)
Preliminary analysis and quality control
5(2)
Assessment of call rates
5(1)
Duplicate samples
6(1)
Relatedness between study subjects
6(1)
Hardy-Weinberg equilibrium
6(1)
Quantile-quantile plots
7(1)
Techniques for detecting association
7(7)
Single locus tests
7(2)
Incorporating covariates
9(1)
Multi-locus tests
10(1)
Interactive and additive effects
11(1)
Pathway analysis
12(1)
Subgroup analysis
13(1)
Imputation of genotypes
13(1)
Confounding and stratification
13(1)
Statistical power and multiple testing
14(3)
Design strategies for increasing power
16(1)
The staged design
17(1)
Replication, quantification, and identification of causal variants
17(1)
Discussion
18(1)
URLs
19(6)
References
20(5)
Methods for DNA copy number derivations
25(27)
Cameron Brennan
Copy number aberration in cancer
25(1)
Obtaining and analysing copy number data: platforms and initial processing
25(4)
Array-CGH
26(1)
Oligonucleotide arrays
26(2)
Representational methods
28(1)
Digital karyotyping and sequencing-based approaches
28(1)
Choosing a platform: array resolution and practical considerations
29(2)
Segmentation
31(3)
Artifacts
33(1)
Aberration types
34(5)
Regional and focal aberrations
34(2)
Copy number variation
36(1)
Regional/broad CNA
37(1)
Focal CNA
37(2)
Assigning significance to CNA
39(5)
Breakpoints/translocations
44(2)
Clustering approaches
46(2)
Conclusion
48(4)
References
48(4)
Methods for derivation of LOH and allelic copy numbers using SNP arrays
52(26)
Carsten Wiuf
Philippe Lamy
Claus L. Andersen
Introduction
52(4)
Overview
53(1)
Retinoblastoma
53(1)
Identification of TSGs
54(1)
Mechanisms causing AI (in particular LOH)
54(1)
Genomic alterations and their relation to clinical end-points
55(1)
Experimental determination of LOH
56(1)
SNP genotyping arrays
57(3)
Normalization
57(1)
Genotyping
58(2)
Simple computational tools to infer LOH
60(1)
Classification of genotypes
60(1)
Regions with same boundary (RSB)
60(1)
Nearest Neighbour (NN)
61(1)
Advanced statistical tools for LOH inference
61(6)
Hidden Markov models
61(2)
Example
63(2)
Two main problems
65(1)
An interpretation of the hidden Markov model
65(1)
Limitations to the HMM approach
65(2)
Estimation of allele specific copy numbers
67(7)
An allele specific HMM
68(1)
Normalization
68(2)
The states
70(1)
Example
70(4)
Conclusion
74(4)
References
74(4)
Bioinformatics of gene expression and copy number data integration
78(24)
Outi Monni
Sampsa Hautaniemi
Introduction
78(1)
Methods
79(2)
Methods to study copy number levels
79(1)
Methods to study gene expression
80(1)
Microarrays in detection of copy number and gene expression levels
81(1)
Microarray experiment
81(6)
Analysis and integration of gene expression and copy number data
87(10)
Preprocessing
87(2)
Identifying amplified and deleted regions from array-CGH data
89(1)
Statistical approach to integrate gene expression and array-CGH data
90(4)
Data reduction model approach to integrate gene expression and array-CGH data
94(2)
Interpolation
96(1)
Gene annotation
97(1)
Conclusions
97(5)
References
98(4)
Analysis of DNA methylation in cancer
102(30)
Fabian Model
Jorn Lewin
Catherine Lofton-Day
Gunter Weiss
Introduction
102(3)
DNA methylation biology
102(1)
DNA methylation in cancer
103(2)
Overview
105(1)
Measuring DNA methylation
105(4)
Measurement technologies
105(3)
Quantification of DNA methylation
108(1)
Data preprocessing
109(9)
Direct bisulphite sequencing
110(4)
DNA microarrays
114(4)
Data analysis
118(10)
Tissue classification using DNA microarrays
118(5)
Plasma based cancer detection
123(3)
Cancer recurrence prediction
126(2)
Conclusion
128(4)
References
128(4)
Pathway analysis: Pathway signatures and classification
132(28)
Ming Yi
Robert M. Stephens
Overview of pathway analysis
132(6)
Pathway and network visualization methods
132(4)
Gene-set based methods
136(2)
From gene signatures/classifiers to pathway signatures/classifiers
138(9)
Gene signature and classifiers
138(2)
Pathway signatures/classifiers as an alternative?
140(2)
Current advances in pathway-level signatures and pathway classification
142(5)
Potentials of pathway-based analysis for integrative discovery
147(4)
Conclusions
151(9)
References
152(8)
Two methods for comparing genomic data across independent studies in cancer research: Meta-analysis and oncomine concepts map
160(17)
Wendy Lockwood Banka
Matthew J. Anstett
Daniel R. Rhodes
Introduction
160(1)
Single-study gene expression analyses in oncomine
161(3)
Differential expression analysis
161(2)
Co-expression analysis
163(1)
Meta-analysis
164(1)
Application
164(3)
Oncomine concepts map
167(2)
Assembling gene signatures
167(1)
Association analysis
168(1)
Application
169(5)
Direct comparison of oncomine concepts results to meta-analysis results
169(5)
Conclusion
174(3)
References
174(3)
Bioinformatic approaches to the analysis of alternative splicing variants in cancer biology
177(16)
Lue Ping Zhao
Jessica Andriesen
Wenhong Fan
Introduction to alternative splicing
177(2)
Traditional methods for splicing analysis
177(2)
Current estimates of alternative splicing in humans
179(1)
Alternative splicing and cancer
179(1)
Oligonucleotide arrays for detecting alternative splicing variants
179(3)
cDNA arrays
180(1)
GeneChip arrays
180(1)
GeneChip exon arrays
181(1)
Tiling arrays
181(1)
Bioinformatic approaches
182(5)
Two group design
182(1)
Functional alternative splicing variants utilizing exon arrays
183(1)
A general framework
184(2)
Relative versus absolute abundance
186(1)
Detection limits
187(1)
An example
187(2)
Future directions
189(4)
References
190(3)
Index 193
Carsten Wiuf obtained his PhD in mathematical biology from the University of Aarhus in 1998. Afterwards he spent 4 years in Oxford at the Department of Statistics before joining a biotech company in Boston. In 2003 he became Professor of Bioinformatics at the University of Aarhus. He has co-authored the book Gene Genealogies, Variation and Evolution (OUP).

Claus L. Andersen earned his PhD in cancer biology from the University of Aarhus in 2002. In 2002 he became an assistant professor at the University of Aarhus and later in 2005 an associated professor. Today he is heading the colorectal cancer research group at the Molecular Diagnostic Laboratory, Aarhus University Hospital.

Both have worked on informatics approaches to the analysis of molecular cancer data and have practical as well as theoretical experience with development of bioinformatics and statistical methods for analysis of molecular data.
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