Merkzettel Warenkorb
  • Produktbild: Principles of Statistical Genomics
  • Produktbild: Principles of Statistical Genomics

Principles of Statistical Genomics

48,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Lieferung nach Hause

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

10.09.2012

Verlag

Springer Us

Seitenzahl

428

Maße (L/B/H)

24,1/16/2,7 cm

Gewicht

828 g

Auflage

2013

Sprache

Englisch

ISBN

978-0-387-70806-5

Beschreibung

Rezension

From the reviews:

“The book was compiled from a collection of lecture notes for a statistical genomics course offered to University California Riverside graduate students by the author. It can be used as a textbook for graduate students in statistical genomics, but also by researchers as a reference book. … For advanced readers of this very modern book in a new field of biometrics, they can choose to read any particular chapters as they desire in this multidisciplinary area.” (T. Postelnicu, zbMATH, Vol. 1276, 2014)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

10.09.2012

Verlag

Springer Us

Seitenzahl

428

Maße (L/B/H)

24,1/16/2,7 cm

Gewicht

828 g

Auflage

2013

Sprache

Englisch

ISBN

978-0-387-70806-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Kundinnen und Kunden meinen

0 Bewertungen

Informationen zu Bewertungen

Zur Abgabe einer Bewertung ist eine Anmeldung im Konto notwendig. Die Authentizität der Bewertungen wird von uns nicht überprüft. Wir behalten uns vor, Bewertungstexte, die unseren Richtlinien widersprechen, entsprechend zu kürzen oder zu löschen.

Die Bewertungen sind nach Format, Anzahl Sterne und Datum sortiert.

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kund*innen durch Ihre Meinung

Kundinnen und Kunden meinen

0 Bewertungen filtern
  • Produktbild: Principles of Statistical Genomics
  • Produktbild: Principles of Statistical Genomics
  • Part I Genetic Linkage Map

    1 Map Functions

    1.1 Physical map and genetic map

    1.2 Derivation of map functions

    1.3 Haldane map function

    1.4 Kosambi map function

    2 Recombination Fraction

    2.1 Mating designs

    2.2 Maximum likelihood estimation of recombination fraction

    2.3 Standard error and significance test

    2.4 Fisher’s scoring algorithm for estimating

    2.5 EM algorithm for estimating

    3 Genetic Map Construction

    3.1 Criteria of optimality

    3.2 Search algorithms

    3.2.1 Exhaustive search

    3.2.2 Heuristic search

    3.2.3 Simulated annealing

    3.2.4 Branch and bound

    3.3 Bootstrap confidence of a map

    4 Multipoint Analysis of Mendelian Loci

    4.1 Joint distribution of multiple locus genotype

    4.1.1 BC design

    4.1.2 F2 design

    4.1.3 Four-way cross design

    4.2 Incomplete genotype information

    4.2.1 Partially informative genotype

    4.2.2 BC and F2 are special cases of FW

    4.2.3 Dominance and missing markers

    4.3 Conditional probability of a missing marker genotype

    4.4 Joint estimation of recombination fractions

    4.5 Multipoint analysis for m markers

    4.6 Map construction with unknown recombination fractions

    Part II Analysis of Quantitative Traits

    5 Basic Concepts of Quantitative Genetics

    5.1 Gene frequency and genotype frequency

    5.2 Genetic effects and genetic variance

    5.3 Average effect of allelic substitution

    5.4 Genetic variance components

    5.5 Heritability

    5.6 An F2 family is in Hardy-Weinberg equilibrium

    6 Major Gene Detection

    6.1 Estimation of major gene effect

    6.1.1 BC design

    6.1.2 F2 design

    6.2 Hypothesis tests

    6.2.1 BC design

    6.2.2 F2 design

    6.3 Scale of the genotype indicator variable

    6.4 Statistical power

    6.4.1 Type I error and statistical power

    6.4.2 Wald-test statistic

    6.4.3 Size of a major gene

    6.4.4 Relationship between W-test and Z-test

    6.4.5 Extension to dominance effect

    7 Segregation Analysis

    7.1 Gaussian mixture distribution

    7.2 EM algorithm

    7.2.1 Closed form solution

    7.2.2 EM steps

    7.2.3 Derivation of the EM algorithm

    7.2.4 Proof of the EM algorithm

    7.3 Hypothesis tests

    7.4 Variances of estimated parameters

    7.5 Estimation of the mixing proportions

    8 Genome Scanning for Quantitative Trait Loci

    8.1 The mouse data

    8.2 Genome scanning

    8.3 Missing genotypes

    8.4 Test statistics

    8.5 Bonferroni correction

    8.6 Permutation test

    8.7 Piepho’s approximate critical value

    8.8 Theoretical consideration

    9 Interval Mapping

    9.1 Least squares method

    9.2 Weighted least squares

    9.3 Fisher scoring

    9.4 Maximum likelihood method

    9.4.1 EM algorithm

    9.4.2 Variance-covariance matrix of ˆθ

    9.4.3 Hypothesis test

    9.5 Remarks on the four methods of interval mapping

    10 Interval Mapping for Ordinal Traits

    10.1 Generalized linear model

    10.2 ML under homogeneous variance

    10.3 ML under heterogeneous variance

    10.4 ML under mixture distribution

    10.5 ML via the EM algorithm

    10.6 Logistic analysis

    10.7 Example

    11 Mapping Segregation Distortion Loci

    11.1 Probabilistic model

    11.1.1 The EM Algorithm

    11.1.2 Hypothesis test

    11.1.3 Variance matrix of the estimated parameters

    11.1.4 Selection coefficient and dominance

    11.2 Liability model

    11.2.1 EM algorithm

    11.2.2 Variance matrix of estimated parameters

    11.2.3 Hypothesis test

    11.3 Mapping QTL under segregation distortion

    11.3.1 Joint likelihood function

    11.3.2 EM algorithm

    11.3.3 Variance-covariance matrix of estimated parameters

    11.3.4 Hypothesis tests

    11.3.5 Example

    12 QTL Mapping in Other Populations

    12.1 Recombinant inbred lines

    12.2 Double haploids

    12.3 Four-way crosses

    12.4 Full-sib family

    12.5 F2 population derived from outbreds

    12.6 Example

    13 Random Model Approach to QTL Mapping

    13.1 Identity-by-descent (IBD)

    13.2 Random effect genetic model

    13.3 Sib-pair regression

    13.4 Maximum likelihood estimation

    13.4.1 EM algorithm

    13.4.2 EM algorithm under singular value decomposition

    13.4.3 Multiple siblings

    13.5 Estimating the IBD value for a marker

    13.6 Multipoint method for estimating the IBD value

    13.7 Genome scanning and hypothesis tests

    13.8 Multiple QTL model

    13.9 Complex pedigree analysis

    14 Mapping QTL for Multiple Traits

    14.1 Multivariate model

    14.2 EM algorithm for parameter estimation

    14.3 Hypothesis tests

    14.4 Variance matrix of estimated parameters

    14.5 Derivation of the EM algorithm

    14.6 Example

    15 Bayesian Multiple QTL Mapping

    15.1 Bayesian regression analysis

    15.2 Markov chain Monte Carlo

    15.3 Mapping multiple QTL

    15.3.1 Multiple QTL model

    15.3.2 Prior, likelihood and posterior

    15.3.3 Summary of the MCMC process

    15.3.4 Post MCMC analysis

    15.4 Alternative methods of Bayesian mapping

    15.4.1 Reversible jump MCMC

    15.4.2 Stochastic search variable selection

    15.4.3 Lasso and Bayesian Lasso

    15.5 Example: Arabidopsis data

    16 Empirical Bayesian QTL Mapping

    16.1 Classical mixed model

    16.1.1 Simultaneous updating for matrix G

    16.1.2 Coordinate descent method

    16.1.3 Block coordinate descent method

    16.1.4 Bayesian estimates of QTL effects

    16.2 Hierarchical mixed model

    16.2.1 Inverse chi-square prior

    16.2.2 Exponential prior

    16.2.3 Dealing with sparse models

    16.3 Infinitesimal model for whole genome sequence data

    16.3.1 Data trimming

    16.3.2 Concept of continuous genome

    16.4 Example: Simulated data

    Part III Microarray Data Analysis

    17 Microarray Differential Expression Analysis

    17.1 Data preparation

    17.1.1 Data transformation

    17.1.2 Data normalization

    17.2 F-test and t-test

    17.3 Type I error and false discovery rate

    17.4 Selection of differentially expressed genes

    17.4.1 Permutation test

    17.4.2 Selecting genes by controlling FDR

    17.4.3 Problems of the previous methods

    17.4.4 Regularized t-test

    17.5 General linear model

    17.5.1 Fixed model approach

    17.5.2 Random model approach

    18 Hierarchical Clustering of Microarray Data

    18.1 Distance matrix

    18.2 UPGMA

    18.3 Neighbor joining

    18.3.1 Principle of neighbor joining

    18.3.2 Computational algorithm

    18.4 Other methods

    18.5 Bootstrap confidence

    19 Model-Based Clustering of Microarray Data

    19.1 Cluster analysis with the K-means method

    19.2 Cluster analysis under Gaussian mixture

    19.2.1 Multivariate Gaussian distribution

    19.2.2 Mixture distribution

    19.2.3 The EM algorithm

    19.2.4 Supervised cluster analysis

    19.2.5 Semi-supervised cluster analysis

    19.3 Inferring the number of clusters

    19.4 Microarray experiments with replications

    20 Gene Specific Analysis of Variances

    20.1 General linear model

    20.2 The SEM algorithm

    20.3 Hypothesis testing

    21 Factor Analysis of Microarray Data

    21.1 Background of factor analysis

    21.1.1 Linear model of latent factors

    21.1.2 EM algorithm

    21.1.3 Number of factors

    21.2 Cluster analysis

    21.3 Differential expression analysis

    21.4 MCMC algorithm

    22 Classification of Tissue Samples Using Microarrays

    22.1 Logistic regression

    22.2 Penalized logistic regression

    22.3 The coordinate descent algorithm

    22.4 Cross validation

    22.5 Prediction of disease outcome

    22.6 Multiple category classification

    23 Time-Course Microarray Data Analysis

    23.1 Gene expression profiles

    23.2 Orthogonal polynomial

    23.3 B-spline

    23.4 Mixed effect model

    23.5 Mixture mixed model

    23.6 EM algorithm

    23.7 Best linear unbiased prediction

    23.8 SEM algorithm

    23.8.1 Monte Carlo sampling

    23.8.2 SEM steps

    24 Quantitative Trait Associated Microarray Data Analysis

    24.1 Linear association

    24.1.1 Linear model

    24.1.2 Cluster analysis

    24.1.3 Three-cluster analysis

    24.1.4 Differential expression analysis

    24.2 Polynomial and B-spline

    24.3 Multiple trait association

    25 Mapping Expression Quantitative Trait Loci

    25.1 Individual marker analysis

    25.1.1 SEM algorithm

    25.1.2 MCMC algorithm

    25.2 Joint analysis of all markers

    25.2.1 Multiple eQTL model

    25.2.2 SEM algorithm

    25.2.3 MCMC algorithm

    25.2.4 Hierarchical evolutionary stochastic search (HESS)