Statistics and informatics in molecular cancer research

Statistics and informatics in molecular cancer research / edited by Carsten Wiuf, Claus L. Andersen.

Molecular understanding of cancer and cancer progression is at the forefront of many research programs. Complex mathematical, statistical and bioinformatics tools are required to extract, handle and process data and this book aims to make these tools available to a wide range of researchers, in a si...

Full description

Saved in:
Bibliographic Details
Other Authors: Wiuf, Carsten, Andersen, Claus L.
Format: eBook
Language:English
Published: Oxford : Oxford University Press, 2009.
Subjects:
Cancer > Research > Statistical methods.
Cancer > Research > Data processing.
Cancer > Molecular aspects > Research > Data processing.
Cancer > Molecular aspects > Research > Statistical methods.
Cancer > Research > Statistical methods
Online Access:Click for online access
Table of Contents:
  • Preface; References; 1 Association studies; 1.1 Introduction; 1.2 Sequence variation and patterns of linkage disequilibrium in the genome; 1.3 Direct and indirect association studies; 1.4 Preliminary analysis and quality control; 1.4.1 Assessment of call rates; 1.4.2 Duplicate samples; 1.4.3 Relatedness between study subjects; 1.4.4 Hardy-Weinberg equilibrium; 1.4.5 Quantile-quantile plots; 1.5 Techniques for detecting association; 1.5.1 Single locus tests; 1.5.2 Incorporating covariates; 1.5.3 Multi-locus tests; 1.5.4 Interactive and additive effects; 1.5.5 Pathway analysis
  • 1.5.6 Subgroup analysis1.5.7 Imputation of genotypes; 1.5.8 Confounding and stratification; 1.6 Statistical power and multiple testing; 1.6.1 Design strategies for increasing power; 1.6.2 The staged design; 1.7 Replication, quantification, and identification of causal variants; 1.8 Discussion; 1.9 URLs; References; 2 Methods for DNA copy number derivations; 2.1 Copy number aberration in cancer; 2.2 Obtaining and analysing copy number data: platforms and initial processing; 2.2.1 Array-CGH; 2.2.2 Oligonucleotide arrays; 2.2.3 Representational methods
  • 2.2.4 Digital karyotyping and sequencing-based approaches2.3 Choosing a platform: array resolution and practical considerations; 2.4 Segmentation; 2.4.1 Artifacts; 2.5 Aberration types; 2.5.1 Regional and focal aberrations; 2.5.2 Copy number variation; 2.5.3 Regional/broad CNA; 2.5.4 Focal CNA; 2.6 Assigning significance to CNA; 2.7 Breakpoints/translocations; 2.8 Clustering approaches; 2.9 Conclusion; References; 3 Methods for derivation of LOH and allelic copy numbers using SNP arrays; 3.1 Introduction; 3.1.1 Overview; 3.1.2 Retinoblastoma; 3.1.3 Identification of TSGs
  • 3.1.4 Mechanisms causing AI (in particular LOH)3.1.5 Genomic alterations and their relation to clinical end-points; 3.2 Experimental determination of LOH; 3.3 SNP genotyping arrays; 3.3.1 Normalization; 3.3.2 Genotyping; 3.4 Simple computational tools to infer LOH; 3.4.1 Classification of genotypes; 3.4.2 Regions with same boundary (RSB); 3.4.3 Nearest Neighbour (NN); 3.5 Advanced statistical tools for LOH inference; 3.5.1 Hidden Markov models; 3.5.2 Example; 3.5.3 Two main problems; 3.5.4 An interpretation of the hidden Markov model; 3.5.5 Limitations to the HMM approach
  • 3.6 Estimation of allele specific copy numbers3.6.1 An allele specific HMM; 3.6.2 Normalization; 3.6.3 The states; 3.6.4 Example; 3.7 Conclusion; References; 4 Bioinformatics of gene expression and copy number data integration; 4.1 Introduction; 4.2 Methods; 4.2.1 Methods to study copy number levels; 4.2.2 Methods to study gene expression; 4.2.3 Microarrays in detection of copy number and gene expression levels; 4.3 Microarray experiment; 4.4 Analysis and integration of gene expression and copy number data; 4.4.1 Preprocessing

Similar Items