Principles of statistical genomics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer
2013
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| Subjects: | |
| Online Access: | FAN01 UBT01 Volltext Inhaltsverzeichnis Abstract |
| Physical Description: | 1 Online-Ressource |
| ISBN: | 0387708065 9780387708065 9780387708072 |
| DOI: | 10.1007/978-0-387-70807-2 |
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Record in the Search Index
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| adam_text | PRINCIPLES OF STATISTICAL GENOMICS
/ XU, SHIZHONG
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
GENETIC LINKAGE MAP
MAP FUNCTIONS
PHYSICAL MAP AND GENETIC MAP
DERIVATION OF MAP FUNCTIONS
HALDANE MAP FUNCTION
KOSAMBI MAP FUNCTION
RECOMBINATION FRACTION
MATING DESIGNS
MAXIMUM LIKELIHOOD ESTIMATION OF RECOMBINATION FRACTION
STANDARD ERROR AND SIGNIFICANCE TEST
FISHER’S SCORING ALGORITHM FOR ESTIMATING
EM ALGORITHM FOR ESTIMATING
GENETIC MAP CONSTRUCTION
CRITERIA OF OPTIMALITY
SEARCH ALGORITHMS
EXHAUSTIVE SEARCH
HEURISTIC SEARCH
SIMULATED ANNEALING
BRANCH AND BOUND
BOOTSTRAP CONFIDENCE OF A MAP
MULTIPOINT ANALYSIS OF MENDELIAN LOCI
JOINT DISTRIBUTION OF MULTIPLE LOCUS GENOTYPE
BC DESIGN
F2 DESIGN
FOUR-WAY CROSS DESIGN
INCOMPLETE GENOTYPE INFORMATION
PARTIALLY INFORMATIVE GENOTYPE
BC AND F2 ARE SPECIAL CASES OF FW
DOMINANCE AND MISSING MARKERS
CONDITIONAL PROBABILITY OF A MISSING MARKER GENOTYPE
JOINT ESTIMATION OF RECOMBINATION FRACTIONS
MULTIPOINT ANALYSIS FOR M MARKERS
MAP CONSTRUCTION WITH UNKNOWN RECOMBINATION FRACTIONS
BASIC CONCEPTS OF QUANTITATIVE GENETICS
GENE FREQUENCY AND GENOTYPE FREQUENCY
GENETIC EFFECTS AND GENETIC VARIANCE
AVERAGE EFFECT OF ALLELIC SUBSTITUTION
GENETIC VARIANCE COMPONENTS
HERITABILITY
AN F2 FAMILY IS IN HARDY-WEINBERG EQUILIBRIUM
MAJOR GENE DETECTION
ESTIMATION OF MAJOR GENE EFFECT
BC DESIGN
F2 DESIGN
HYPOTHESIS TESTS
BC DESIGN
F2 DESIGN
SCALE OF THE GENOTYPE INDICATOR VARIABLE
STATISTICAL POWER
TYPE I ERROR AND STATISTICAL POWER
WALD-TEST STATISTIC
SIZE OF A MAJOR GENE
RELATIONSHIP BETWEEN W-TEST AND Z-TEST
EXTENSION TO DOMINANCE EFFECT
SEGREGATION ANALYSIS
GAUSSIAN MIXTURE DISTRIBUTION
EM ALGORITHM
CLOSED FORM SOLUTION
EM STEPS
DERIVATION OF THE EM ALGORITHM
PROOF OF THE EM ALGORITHM
HYPOTHESIS TESTS
VARIANCES OF ESTIMATED PARAMETERS
ESTIMATION OF THE MIXING PROPORTIONS
GENOME SCANNING FOR QUANTITATIVE TRAIT LOCI
THE MOUSE DATA
GENOME SCANNING
MISSING GENOTYPES
TEST STATISTICS
BONFERRONI CORRECTION
PERMUTATION TEST
PIEPHO’S APPROXIMATE CRITICAL VALUE
THEORETICAL CONSIDERATION
INTERVAL MAPPING
LEAST SQUARES METHOD
WEIGHTED LEAST SQUARES
FISHER SCORING
MAXIMUM LIKELIHOOD METHOD
EM ALGORITHM
VARIANCE-COVARIANCE MATRIX OF ˆΘ
HYPOTHESIS TEST
REMARKS ON THE FOUR METHODS OF INTERVAL MAPPING
INTERVAL MAPPING FOR ORDINAL TRAITS
GENERALIZED LINEAR MODEL
ML UNDER HOMOGENEOUS VARIANCE
ML UNDER HETEROGENEOUS VARIANCE
ML UNDER MIXTURE DISTRIBUTION
ML VIA THE EM ALGORITHM
LOGISTIC ANALYSIS
EXAMPLE
MAPPING SEGREGATION DISTORTION LOCI
PROBABILISTIC MODEL
THE EM ALGORITHM
HYPOTHESIS TEST
VARIANCE MATRIX OF THE ESTIMATED PARAMETERS
SELECTION COEFFICIENT AND DOMINANCE
LIABILITY MODEL
EM ALGORITHM
VARIANCE MATRIX OF ESTIMATED PARAMETERS
HYPOTHESIS TEST
MAPPING QTL UNDER SEGREGATION DISTORTION
JOINT LIKELIHOOD FUNCTION
EM ALGORITHM
VARIANCE-COVARIANCE MATRIX OF ESTIMATED PARAMETERS
HYPOTHESIS TESTS
EXAMPLE
QTL MAPPING IN OTHER POPULATIONS
RECOMBINANT INBRED LINES
DOUBLE HAPLOIDS
FOUR-WAY CROSSES
FULL-SIB FAMILY
F2 POPULATION DERIVED FROM OUTBREDS
EXAMPLE
RANDOM MODEL APPROACH TO QTL MAPPING
IDENTITY-BY-DESCENT (IBD)
RANDOM EFFECT GENETIC MODEL
SIB-PAIR REGRESSION.- MAXIMUM LIKELIHOOD ESTIMATION
EM ALGORITHM
EM ALGORITHM UNDER SINGULAR VALUE DECOMPOSITION
MULTIPLE SIBLINGS
ESTIMATING THE IBD VALUE FOR A MARKER
MULTIPOINT METHOD FOR ESTIMATING THE IBD VALUE
GENOME SCANNING AND HYPOTHESIS TESTS
MULTIPLE QTL MODEL
COMPLEX PEDIGREE ANALYSIS
MAPPING QTL FOR MULTIPLE TRAITS
MULTIVARIATE MODEL
EM ALGORITHM FOR PARAMETER ESTIMATION
HYPOTHESIS TESTS
VARIANCE MATRIX OF ESTIMATED PARAMETERS
DERIVATION OF THE EM ALGORITHM
EXAMPLE
BAYESIAN MULTIPLE QTL MAPPING
BAYESIAN REGRESSION ANALYSIS
MARKOV CHAIN MONTE CARLO
MAPPING MULTIPLE QTL
MULTIPLE QTL MODEL
PRIOR, LIKELIHOOD AND POSTERIOR
SUMMARY OF THE MCMC PROCESS
POST MCMC ANALYSIS
ALTERNATIVE METHODS OF BAYESIAN MAPPING
REVERSIBLE JUMP MCMC
STOCHASTIC SEARCH VARIABLE SELECTION
LASSO AND BAYESIAN LASSO
EXAMPLE: ARABIDOPSIS DATA
EMPIRICAL BAYESIAN QTL MAPPING
CLASSICAL MIXED MODEL
SIMULTANEOUS UPDATING FOR MATRIX G
COORDINATE DESCENT METHOD
BLOCK COORDINATE DESCENT METHOD
BAYESIAN ESTIMATES OF QTL EFFECTS
HIERARCHICAL MIXED MODEL
INVERSE CHI-SQUARE PRIOR
EXPONENTIAL PRIOR
DEALING WITH SPARSE MODELS
INFINITESIMAL MODEL FOR WHOLE GENOME SEQUENCE DATA
DATA TRIMMING
CONCEPT OF CONTINUOUS GENOME
EXAMPLE: SIMULATED DATA
MICROARRAY DIFFERENTIAL EXPRESSION ANALYSIS
DATA PREPARATION
DATA TRANSFORMATION
DATA NORMALIZATION
F-TEST AND T-TEST
TYPE I ERROR AND FALSE DISCOVERY RATE
SELECTION OF DIFFERENTIALLY EXPRESSED GENES
PERMUTATION TEST
SELECTING GENES BY CONTROLLING FDR
PROBLEMS OF THE PREVIOUS METHODS
REGULARIZED T-TEST
GENERAL LINEAR MODEL
FIXED MODEL APPROACH
RANDOM MODEL APPROACH
HIERARCHICAL CLUSTERING OF MICROARRAY DATA
DISTANCE MATRIX
UPGMA
NEIGHBOR JOINING
PRINCIPLE OF NEIGHBOR JOINING
COMPUTATIONAL ALGORITHM
OTHER METHODS
BOOTSTRAP CONFIDENCE
MODEL-BASED CLUSTERING OF MICROARRAY DATA
CLUSTER ANALYSIS WITH THE K-MEANS METHOD
CLUSTER ANALYSIS UNDER GAUSSIAN MIXTURE
MULTIVARIATE GAUSSIAN DISTRIBUTION
MIXTURE DISTRIBUTION
THE EM ALGORITHM
SUPERVISED CLUSTER ANALYSIS
SEMI-SUPERVISED CLUSTER ANALYSIS
INFERRING THE NUMBER OF CLUSTERS
MICROARRAY EXPERIMENTS WITH REPLICATIONS
GENE SPECIFIC ANALYSIS OF VARIANCES
GENERAL LINEAR MODEL
THE SEM ALGORITHM
HYPOTHESIS TESTING
FACTOR ANALYSIS OF MICROARRAY DATA
BACKGROUND OF FACTOR ANALYSIS
LINEAR MODEL OF LATENT FACTORS
EM ALGORITHM
NUMBER OF FACTORS
CLUSTER ANALYSIS
DIFFERENTIAL EXPRESSION ANALYSIS
MCMC ALGORITHM
CLASSIFICATION OF TISSUE SAMPLES USING MICROARRAYS
LOGISTIC REGRESSION
PENALIZED LOGISTIC REGRESSION
THE COORDINATE DESCENT ALGORITHM
CROSS VALIDATION
PREDICTION OF DISEASE OUTCOME
MULTIPLE CATEGORY CLASSIFICATION
TIME-COURSE MICROARRAY DATA ANALYSIS
GENE EXPRESSION PROFILES
ORTHOGONAL POLYNOMIAL
B-SPLINE
MIXED EFFECT MODEL
MIXTURE MIXED MODEL
EM ALGORITHM
BEST LINEAR UNBIASED PREDICTION
SEM ALGORITHM
MONTE CARLO SAMPLING
SEM STEPS
QUANTITATIVE TRAIT ASSOCIATED MICROARRAY DATA ANALYSIS
LINEAR ASSOCIATION
LINEAR MODEL
CLUSTER ANALYSIS
THREE-CLUSTER ANALYSIS
DIFFERENTIAL EXPRES
SEM ALGORITHM
MCMC ALGORITHM
JOINT ANALYSIS OF ALL MARKERS
MULTIPLE EQTL MODEL
SEM ALGORITHM
MCMC ALGORITHM
HIERARCHICAL EVOLUTIONARY STOCHASTIC SEARCH (HESS)
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
PRINCIPLES OF STATISTICAL GENOMICS
/ XU, SHIZHONG
: 2013
ABSTRACT / INHALTSTEXT
STATISTICAL GENOMICS IS A RAPIDLY DEVELOPING FIELD, WITH MORE AND MORE
PEOPLE INVOLVED IN THIS AREA. HOWEVER, A LACK OF SYNTHETIC REFERENCE
BOOKS AND TEXTBOOKS IN STATISTICAL GENOMICS HAS BECOME A MAJOR
HURDLETO THE DEVELOPMENT OF THE FIELD. ALTHOUGH MANY BOOKS HAVE BEEN
PUBLISHED RECENTLY IN BIOINFORMATICS, MOST OF THEM EMPHASIZE DNA
SEQUENCE ANALYSIS UNDER A DETERMINISTIC APPROACH. PRINCIPLES OF
STATISTICAL GENOMICS SYNTHESIZES THE STATE-OF-THE-ART STATISTICAL
METHODOLOGIES (STOCHASTIC APPROACHES) APPLIED TO GENOME STUDY. IT
FACILITATES UNDERSTANDING OF THE STATISTICAL MODELS AND METHODS BEHIND
THE MAJOR BIOINFORMATICS SOFTWARE PACKAGES, WHICH WILL HELP RESEARCHERS
CHOOSE THE OPTIMAL ALGORITHM TO ANALYZE THEIR DATA AND BETTER INTERPRET
THE RESULTS OF THEIR ANALYSES. UNDERSTANDING EXISTING STATISTICAL MODELS
AND ALGORITHMS ASSISTS RESEARCHERS TO DEVELOP IMPROVED STATISTICAL
METHODS TO EXTRACT MAXIMUM INFORMATION FROM THEIR DATA. RESOURCEFUL AND
EASY TO USE, PRINCIPLES OF STATISTICAL GENOMICS ISA COMPREHENSIVE
REFERENCE FOR RESEARCHERS AND GRADUATE STUDENTS STUDYING STATISTICAL
GENOMICS.
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| discipline | Biologie |
| doi_str_mv | 10.1007/978-0-387-70807-2 |
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| spelling | Xu, Shizhong Verfasser aut Principles of statistical genomics Shizhong Xu New York, NY Springer 2013 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Genomik (DE-588)4776397-8 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Biostatistik (DE-588)4729990-3 s Genomik (DE-588)4776397-8 s 1\p DE-604 https://doi.org/10.1007/978-0-387-70807-2 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025715546&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025715546&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Xu, Shizhong Principles of statistical genomics Genomik (DE-588)4776397-8 gnd Biostatistik (DE-588)4729990-3 gnd |
| subject_GND | (DE-588)4776397-8 (DE-588)4729990-3 |
| title | Principles of statistical genomics |
| title_auth | Principles of statistical genomics |
| title_exact_search | Principles of statistical genomics |
| title_full | Principles of statistical genomics Shizhong Xu |
| title_fullStr | Principles of statistical genomics Shizhong Xu |
| title_full_unstemmed | Principles of statistical genomics Shizhong Xu |
| title_short | Principles of statistical genomics |
| title_sort | principles of statistical genomics |
| topic | Genomik (DE-588)4776397-8 gnd Biostatistik (DE-588)4729990-3 gnd |
| topic_facet | Genomik Biostatistik |
| url | https://doi.org/10.1007/978-0-387-70807-2 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025715546&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025715546&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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