top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
Pubbl/distr/stampa Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008
Descrizione fisica 1 online resource (440 p.)
Disciplina 572.8636
Soggetto topico DNA microarrays
Soggetto genere / forma Electronic books.
ISBN 1-281-94703-2
9786611947033
3-527-62281-0
3-527-62282-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analysis of Microarray Data; Contents; Preface; List of Contributors; 1 Introduction to DNA Microarrays; 1.1 Introduction; 1.1.1 The Genome is an Information Scaffold; 1.1.2 Gene Expression is Detected by Hybridization; 1.1.2.1 Hybridization is Used to Measure Gene Expression; 1.1.2.2 Microarrays Provide a New Twist to an Old Technique; 1.2 Types of Arrays; 1.2.1 Spotted Microarrays; 1.2.2 Affymetrix GeneChips; 1.2.2.1 Other In Situ Synthesis Platforms; 1.2.2.2 Uses of Microarrays; 1.3 Array Content; 1.3.1 ESTs Are the First View; 1.3.1.1 Probe Design; 1.4 Normalization and Scaling
1.4.1 Be Unbiased, Be Complete1.4.2 Sequence Counts; References; 2 Comparative Analysis of Clustering Methods for Microarray Data; 2.1 Introduction; 2.2 Measuring Distance Between Genes or Clusters; 2.3 Network Models; 2.3.1 Boolean Network; 2.3.2 Coexpression Network; 2.3.3 Bayesian Network; 2.3.4 Co-Occurrence Network; 2.4 Network Constrained Clustering Method; 2.4.1 Extract the Giant Connected Component; 2.4.2 Compute "Network Constrained Distance Matrix"; 2.5 Network Constrained Clustering Results; 2.5.1 Yeast Galactose Metabolism Pathway; 2.5.2 Retinal Gene Expression Data
2.5.3 Mouse Segmentation Clock Data2.6 Discussion and Conclusion; References; 3 Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders; 3.1 Introduction: Gene and Genetic Networks; 3.2 Background and Prior Theory; 3.2.1 Motivation; 3.2.2 Bayesian Networks Theory; 3.2.2.1 d-Separation at Colliders; 3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework; 3.2.3 Learning Network Structure from Observed Conditional Independencies; 3.2.4 Prior Work: The PC Algorithm; 3.2.4.1 PC Algorithm
3.5 Results and Further Application3.5.1 Estimating α False-Positive Rates for the v-Structure Test; 3.5.2 Learning an Aortic Lesion Network; 3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes; 3.5.4 Future Work; References; 4 Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects; 4.1 Introduction; 4.2 Basic Theory of Bayesian Networks; 4.2.1 Bayesian Scoring Metrics; 4.2.2 Heuristic Search Methods; 4.2.3 Inference Score; 4.3 Methods; 4.3.1 Experimental Design; 4.3.2 Tissue Contamination; 4.3.3 Gene List Prefiltering
4.3.4 Outlier Removal
Record Nr. UNINA-9910144107303321
Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
Pubbl/distr/stampa Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008
Descrizione fisica 1 online resource (440 p.)
Disciplina 572.8636
Soggetto topico DNA microarrays
ISBN 1-281-94703-2
9786611947033
3-527-62281-0
3-527-62282-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analysis of Microarray Data; Contents; Preface; List of Contributors; 1 Introduction to DNA Microarrays; 1.1 Introduction; 1.1.1 The Genome is an Information Scaffold; 1.1.2 Gene Expression is Detected by Hybridization; 1.1.2.1 Hybridization is Used to Measure Gene Expression; 1.1.2.2 Microarrays Provide a New Twist to an Old Technique; 1.2 Types of Arrays; 1.2.1 Spotted Microarrays; 1.2.2 Affymetrix GeneChips; 1.2.2.1 Other In Situ Synthesis Platforms; 1.2.2.2 Uses of Microarrays; 1.3 Array Content; 1.3.1 ESTs Are the First View; 1.3.1.1 Probe Design; 1.4 Normalization and Scaling
1.4.1 Be Unbiased, Be Complete1.4.2 Sequence Counts; References; 2 Comparative Analysis of Clustering Methods for Microarray Data; 2.1 Introduction; 2.2 Measuring Distance Between Genes or Clusters; 2.3 Network Models; 2.3.1 Boolean Network; 2.3.2 Coexpression Network; 2.3.3 Bayesian Network; 2.3.4 Co-Occurrence Network; 2.4 Network Constrained Clustering Method; 2.4.1 Extract the Giant Connected Component; 2.4.2 Compute "Network Constrained Distance Matrix"; 2.5 Network Constrained Clustering Results; 2.5.1 Yeast Galactose Metabolism Pathway; 2.5.2 Retinal Gene Expression Data
2.5.3 Mouse Segmentation Clock Data2.6 Discussion and Conclusion; References; 3 Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders; 3.1 Introduction: Gene and Genetic Networks; 3.2 Background and Prior Theory; 3.2.1 Motivation; 3.2.2 Bayesian Networks Theory; 3.2.2.1 d-Separation at Colliders; 3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework; 3.2.3 Learning Network Structure from Observed Conditional Independencies; 3.2.4 Prior Work: The PC Algorithm; 3.2.4.1 PC Algorithm
3.5 Results and Further Application3.5.1 Estimating α False-Positive Rates for the v-Structure Test; 3.5.2 Learning an Aortic Lesion Network; 3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes; 3.5.4 Future Work; References; 4 Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects; 4.1 Introduction; 4.2 Basic Theory of Bayesian Networks; 4.2.1 Bayesian Scoring Metrics; 4.2.2 Heuristic Search Methods; 4.2.3 Inference Score; 4.3 Methods; 4.3.1 Experimental Design; 4.3.2 Tissue Contamination; 4.3.3 Gene List Prefiltering
4.3.4 Outlier Removal
Record Nr. UNINA-9910830082603321
Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8636
572.865
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-280-25332-0
9786610253326
0-470-35030-X
0-471-72612-5
0-471-72842-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9910146082203321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8636
572.865
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
ISBN 1-280-25332-0
9786610253326
0-470-35030-X
0-471-72612-5
0-471-72842-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9910830637803321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Analyzing microarray gene expression data [[electronic resource] /] / Geoffrey J. McLachlan, Kim-Anh Do, Christopher Ambroise
Autore McLachlan Geoffrey J. <1946->
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2004
Descrizione fisica 1 online resource (366 p.)
Disciplina 572.8636
572.865
Altri autori (Persone) DoKim-Anh <1960->
AmbroiseChristophe <1969->
Collana Wiley series in probability and statistics
Soggetto topico DNA microarrays - Statistical methods
Gene expression - Statistical methods
ISBN 1-280-25332-0
9786610253326
0-470-35030-X
0-471-72612-5
0-471-72842-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays
1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays
2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space
3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions
3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic
3.17.2 The Clest Method for the Number of Clusters
Record Nr. UNINA-9910840946603321
McLachlan Geoffrey J. <1946->  
Hoboken, N.J., : Wiley-Interscience, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Pubbl/distr/stampa Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Descrizione fisica 1 online resource (282 p.)
Disciplina 572.8
572.8636
611.01816
Altri autori (Persone) SchererAndreas <1966->
Collana Wiley Series in Probability and Statistics
Soggetto topico DNA microarrays
DNA microarrays - Experiments
Soggetto genere / forma Electronic books.
ISBN 1-282-37950-X
9786612379505
0-470-68598-0
0-470-68599-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Batch Effects and Noise in Microarray Experiments; Contents; List of Contributors; Foreword; Preface; 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction; 2 Microarray Platforms and Aspects of Experimental Variation; 2.1 Introduction; 2.2 Microarray Platforms; 2.2.1 Affymetrix; 2.2.2 Agilent; 2.2.3 Illumina; 2.2.4 Nimblegen; 2.2.5 Spotted Microarrays; 2.3 Experimental Considerations; 2.3.1 Experimental Design; 2.3.2 Sample and RNA Extraction; 2.3.3 Amplification; 2.3.4 Labeling; 2.3.5 Hybridization; 2.3.6 Washing; 2.3.7 Scanning
2.3.8 Image Analysis and Data Extraction2.3.9 Clinical Diagnosis; 2.3.10 Interpretation of the Data; 2.4 Conclusions; 3 Experimental Design; 3.1 Introduction; 3.2 Principles of Experimental Design; 3.2.1 Definitions; 3.2.2 Technical Variation; 3.2.3 Biological Variation; 3.2.4 Systematic Variation; 3.2.5 Population, Random Sample, Experimental and Observational Units; 3.2.6 Experimental Factors; 3.2.7 Statistical Errors; 3.3 Measures to Increase Precision and Accuracy; 3.3.1 Randomization; 3.3.2 Blocking; 3.3.3 Replication; 3.3.4 Further Measures to Optimize Study Design
3.4 Systematic Errors in Microarray Studies3.4.1 Selection Bias; 3.4.2 Observational Bias; 3.4.3 Bias at Specimen/Tissue Collection; 3.4.4 Bias at mRNA Extraction and Hybridization; 3.5 Conclusion; 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies; 4.1 Introduction; 4.1.1 Batch Effects; 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments; 4.2.1 Using the Linear Model for Design; 4.2.2 Examples of Design Guided by the Linear Model; 4.3 Blocks and Batches; 4.3.1 Complete Block Designs; 4.3.2 Incomplete Block Designs
4.3.3 Multiple Batch Effects4.4 Reducing Batch Effects by Normalization and Statistical Adjustment; 4.4.1 Between and Within Batch Normalization with Multi-array Methods; 4.4.2 Statistical Adjustment; 4.5 Sample Pooling and Sample Splitting; 4.5.1 Sample Pooling; 4.5.2 Sample Splitting: Technical Replicates; 4.6 Pilot Experiments; 4.7 Conclusions; Acknowledgements; 5 Aspects of Technical Bias; 5.1 Introduction; 5.2 Observational Studies; 5.2.1 Same Protocol, Different Times of Processing; 5.2.2 Same Protocol, Different Sites (Study 1); 5.2.3 Same Protocol, Different Sites (Study 2)
5.2.4 Batch Effect Characteristics at the Probe Level5.3 Conclusion; 6 Bioinformatic Strategies for cDNA-Microarray Data Processing; 6.1 Introduction; 6.1.1 Spike-in Experiments; 6.1.2 Key Measures - Sensitivity and Bias; 6.1.3 The IC Curve and MA Plot; 6.2 Pre-processing; 6.2.1 Scanning Procedures; 6.2.2 Background Correction; 6.2.3 Saturation; 6.2.4 Normalization; 6.2.5 Filtering; 6.3 Downstream Analysis; 6.3.1 Gene Selection; 6.3.2 Cluster Analysis; 6.4 Conclusion; 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance; 7.1 Introduction
7.1.1 Microarray Gene Expression Data
Record Nr. UNINA-9910139958503321
Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Pubbl/distr/stampa Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Descrizione fisica 1 online resource (282 p.)
Disciplina 572.8
572.8636
611.01816
Altri autori (Persone) SchererAndreas <1966->
Collana Wiley Series in Probability and Statistics
Soggetto topico DNA microarrays
DNA microarrays - Experiments
ISBN 1-282-37950-X
9786612379505
0-470-68598-0
0-470-68599-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Batch Effects and Noise in Microarray Experiments; Contents; List of Contributors; Foreword; Preface; 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction; 2 Microarray Platforms and Aspects of Experimental Variation; 2.1 Introduction; 2.2 Microarray Platforms; 2.2.1 Affymetrix; 2.2.2 Agilent; 2.2.3 Illumina; 2.2.4 Nimblegen; 2.2.5 Spotted Microarrays; 2.3 Experimental Considerations; 2.3.1 Experimental Design; 2.3.2 Sample and RNA Extraction; 2.3.3 Amplification; 2.3.4 Labeling; 2.3.5 Hybridization; 2.3.6 Washing; 2.3.7 Scanning
2.3.8 Image Analysis and Data Extraction2.3.9 Clinical Diagnosis; 2.3.10 Interpretation of the Data; 2.4 Conclusions; 3 Experimental Design; 3.1 Introduction; 3.2 Principles of Experimental Design; 3.2.1 Definitions; 3.2.2 Technical Variation; 3.2.3 Biological Variation; 3.2.4 Systematic Variation; 3.2.5 Population, Random Sample, Experimental and Observational Units; 3.2.6 Experimental Factors; 3.2.7 Statistical Errors; 3.3 Measures to Increase Precision and Accuracy; 3.3.1 Randomization; 3.3.2 Blocking; 3.3.3 Replication; 3.3.4 Further Measures to Optimize Study Design
3.4 Systematic Errors in Microarray Studies3.4.1 Selection Bias; 3.4.2 Observational Bias; 3.4.3 Bias at Specimen/Tissue Collection; 3.4.4 Bias at mRNA Extraction and Hybridization; 3.5 Conclusion; 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies; 4.1 Introduction; 4.1.1 Batch Effects; 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments; 4.2.1 Using the Linear Model for Design; 4.2.2 Examples of Design Guided by the Linear Model; 4.3 Blocks and Batches; 4.3.1 Complete Block Designs; 4.3.2 Incomplete Block Designs
4.3.3 Multiple Batch Effects4.4 Reducing Batch Effects by Normalization and Statistical Adjustment; 4.4.1 Between and Within Batch Normalization with Multi-array Methods; 4.4.2 Statistical Adjustment; 4.5 Sample Pooling and Sample Splitting; 4.5.1 Sample Pooling; 4.5.2 Sample Splitting: Technical Replicates; 4.6 Pilot Experiments; 4.7 Conclusions; Acknowledgements; 5 Aspects of Technical Bias; 5.1 Introduction; 5.2 Observational Studies; 5.2.1 Same Protocol, Different Times of Processing; 5.2.2 Same Protocol, Different Sites (Study 1); 5.2.3 Same Protocol, Different Sites (Study 2)
5.2.4 Batch Effect Characteristics at the Probe Level5.3 Conclusion; 6 Bioinformatic Strategies for cDNA-Microarray Data Processing; 6.1 Introduction; 6.1.1 Spike-in Experiments; 6.1.2 Key Measures - Sensitivity and Bias; 6.1.3 The IC Curve and MA Plot; 6.2 Pre-processing; 6.2.1 Scanning Procedures; 6.2.2 Background Correction; 6.2.3 Saturation; 6.2.4 Normalization; 6.2.5 Filtering; 6.3 Downstream Analysis; 6.3.1 Gene Selection; 6.3.2 Cluster Analysis; 6.4 Conclusion; 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance; 7.1 Introduction
7.1.1 Microarray Gene Expression Data
Record Nr. UNINA-9910831169203321
Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Batch effects and noise in microarray experiments, sources, and solutions [[electronic resource] /] / edited by Andreas Scherer
Pubbl/distr/stampa Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Descrizione fisica 1 online resource (282 p.)
Disciplina 572.8
572.8636
611.01816
Altri autori (Persone) SchererAndreas <1966->
Collana Wiley Series in Probability and Statistics
Soggetto topico DNA microarrays
DNA microarrays - Experiments
ISBN 1-282-37950-X
9786612379505
0-470-68598-0
0-470-68599-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Batch Effects and Noise in Microarray Experiments; Contents; List of Contributors; Foreword; Preface; 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction; 2 Microarray Platforms and Aspects of Experimental Variation; 2.1 Introduction; 2.2 Microarray Platforms; 2.2.1 Affymetrix; 2.2.2 Agilent; 2.2.3 Illumina; 2.2.4 Nimblegen; 2.2.5 Spotted Microarrays; 2.3 Experimental Considerations; 2.3.1 Experimental Design; 2.3.2 Sample and RNA Extraction; 2.3.3 Amplification; 2.3.4 Labeling; 2.3.5 Hybridization; 2.3.6 Washing; 2.3.7 Scanning
2.3.8 Image Analysis and Data Extraction2.3.9 Clinical Diagnosis; 2.3.10 Interpretation of the Data; 2.4 Conclusions; 3 Experimental Design; 3.1 Introduction; 3.2 Principles of Experimental Design; 3.2.1 Definitions; 3.2.2 Technical Variation; 3.2.3 Biological Variation; 3.2.4 Systematic Variation; 3.2.5 Population, Random Sample, Experimental and Observational Units; 3.2.6 Experimental Factors; 3.2.7 Statistical Errors; 3.3 Measures to Increase Precision and Accuracy; 3.3.1 Randomization; 3.3.2 Blocking; 3.3.3 Replication; 3.3.4 Further Measures to Optimize Study Design
3.4 Systematic Errors in Microarray Studies3.4.1 Selection Bias; 3.4.2 Observational Bias; 3.4.3 Bias at Specimen/Tissue Collection; 3.4.4 Bias at mRNA Extraction and Hybridization; 3.5 Conclusion; 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies; 4.1 Introduction; 4.1.1 Batch Effects; 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments; 4.2.1 Using the Linear Model for Design; 4.2.2 Examples of Design Guided by the Linear Model; 4.3 Blocks and Batches; 4.3.1 Complete Block Designs; 4.3.2 Incomplete Block Designs
4.3.3 Multiple Batch Effects4.4 Reducing Batch Effects by Normalization and Statistical Adjustment; 4.4.1 Between and Within Batch Normalization with Multi-array Methods; 4.4.2 Statistical Adjustment; 4.5 Sample Pooling and Sample Splitting; 4.5.1 Sample Pooling; 4.5.2 Sample Splitting: Technical Replicates; 4.6 Pilot Experiments; 4.7 Conclusions; Acknowledgements; 5 Aspects of Technical Bias; 5.1 Introduction; 5.2 Observational Studies; 5.2.1 Same Protocol, Different Times of Processing; 5.2.2 Same Protocol, Different Sites (Study 1); 5.2.3 Same Protocol, Different Sites (Study 2)
5.2.4 Batch Effect Characteristics at the Probe Level5.3 Conclusion; 6 Bioinformatic Strategies for cDNA-Microarray Data Processing; 6.1 Introduction; 6.1.1 Spike-in Experiments; 6.1.2 Key Measures - Sensitivity and Bias; 6.1.3 The IC Curve and MA Plot; 6.2 Pre-processing; 6.2.1 Scanning Procedures; 6.2.2 Background Correction; 6.2.3 Saturation; 6.2.4 Normalization; 6.2.5 Filtering; 6.3 Downstream Analysis; 6.3.1 Gene Selection; 6.3.2 Cluster Analysis; 6.4 Conclusion; 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance; 7.1 Introduction
7.1.1 Microarray Gene Expression Data
Record Nr. UNINA-9910841365003321
Chichester, West Sussex ; ; Hoboken, : J. Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A biologist's guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen
A biologist's guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen
Autore Knudsen Steen
Pubbl/distr/stampa New York, : Wiley-Interscience, c2002
Descrizione fisica 1 online resource (148 p.)
Disciplina 572.8/636
572.8636
Soggetto topico DNA microarrays
Soggetto genere / forma Electronic books.
ISBN 1-280-55646-3
9786610556465
0-471-46118-0
0-471-22758-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Preface xi -- Acknowledgments xiii -- 1 Introduction I -- 1.1 Hybridization 1 -- 1.2 Affymetrix GeneChip Technology 3 -- 1.3 Spotted Arrays 6 -- 1.4 Serial Analysis of Gene Expression (SAGE) 8 -- 1.5 Example: Affymetrix vs. Spotted Arrays 9 -- 1.6 Summary 11 -- 1.7 Further Reading 13 -- 2 Overview of Data Analysis 15 -- 3 Basic Data Analysis 17 -- 3.1 Absolute Measurements 17 -- 3.2 Scaling 18 -- 3.2.1 Example: Linear and Nonlinear Scaling 20 -- 3.3 Detection of Outliers 20 -- 3.4 Fold Change 21 -- 3.5 Significance 22 -- 3.5.1 Nonparametric Tests 24 -- 3.5.2 Correction for Multiple Testing 24 -- 3.5.3 Example I: t-Test and ANOVA 25 -- 3.5.4 Example II: Number of Replicates 26 -- 3.6 Summary 28 -- 3.7 Further Reading 29 -- 4 Visualization by Reduction of Dimensionality 33 -- 4.1 Principal Component Analysis 33 -- 4.2 Example 1: PCA on Small Data Matrix 35 -- 4.3 Example 2: PCA on Real Data 37 -- 4.4 Summary 37 -- 4.5 Further Reading 39 -- 5 Cluster Analysis 41 -- 5.1 Hierarchical Clustering 41 -- 5.2 K-means Clustering 43 -- 5.3 Self-Organizing Maps 44 -- 5.4 Distance Measures 45 -- 5.4.1 Example: Comparison of Distance Measures 47 -- 5.5 Normalization 49 -- 5.6 Visualization of Clusters 50 -- 5.6.1 Example: Visualization of Gene Clusters in -- Bladder Cancer 50 -- 5.7 Summary 50 -- 5.8 Further Reading 52 -- 6 Beyond Cluster Analysis 55 -- 6.1 Function Prediction 55 -- 6.2 Discovery of Regulatory Elements in Promoter -- Regions 56 -- 6.2.1 Example 1: Discovery of Proteasomal Element 57 -- 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle -- Box (MCB) 57 -- 6.3 Integration of data 58 -- 6.4 Summary 59 -- 6.5 Further Reading 59 -- 7 Reverse Engineering of Regulatory Networks 63 -- 7.1 The Time-Series Approach 63 -- 7.2 The Steady-State Approach 64 -- 7.3 Limitations of Network Modeling 65 -- 7.4 Example 1: Steady-State Model 65 -- 7.5 Example 2: Steady-State Model on Real Data 66 -- 7.6 Example 3: Steady-State Model on Real Data 68 -- 7.7 Example 4: Linear Time-Series Model 68 -- 7.8 Further Reading 71 -- 8 Molecular Classifiers 75 -- 8.1 Classification Schemes 76 -- 8.1.1 Nearest Neighbor 76 -- 8.1.2 Neural Networks 76 -- 8.1.3 Support Vector Machine 76 -- 8.2 Example I: Classification of Cancer Subtypes 77 -- 8.3 Example II: Classification of Cancer Subtypes 78 -- 8.4 Summary 79 -- 8.5 Further Reading 79 -- 9 Selection of Genes for Spotting on Arrays 81 -- 9.1 Gene Finding 82 -- 9.2 Selection of Regions Within Genes 82 -- 9.3 Selection of Primers for PCR 83 -- 9.4 Selection of Unique Oligomer Probes 83 -- 9.4.1 Example: Finding PCR Primers for Gene -- AF105374 83 -- 9.5 Experimental Design 84 -- 9.6 Further Reading 84 -- 10 Limitations of Expression Analysis 87 -- 10.1 Relative VersusAbsoluteRNA Quantification 88 -- 10.2 Further Reading 88 -- 11 Genotyping Chips 91 -- 11.1 Example: NeuralNetworksfor GeneChipprediction 91 -- 11.2 Further Reading 93 -- 12 Software Issues and Data Formats 95 -- 12.1 Standardization Efforts 96 -- 12.2 Standard File Format 97 -- 12.2.1 Example: Small Scripts in Awk 97 -- 12.3 Software for Clustering 98 -- 12.3.1 Example: Clustering with ClustArray 99 -- 12.4 Software for Statistical Analysis 99 -- 12.4.1 Example: StatisticalAnalysis with R 99 -- 12.4.2 The affyR Software Package 103 -- 12.4.3 Commercial Statistics Packages 103 -- 12.5 Summary 103 -- 12.6 Further Reading 104 -- 13 Commercial Software Packages 105 -- 14 Bibliography 109 -- Index 123.
Record Nr. UNINA-9910146082503321
Knudsen Steen  
New York, : Wiley-Interscience, c2002
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A biologist's guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen
A biologist's guide to analysis of DNA microarray data [[electronic resource] /] / Steen Knudsen
Autore Knudsen Steen
Pubbl/distr/stampa New York, : Wiley-Interscience, c2002
Descrizione fisica 1 online resource (148 p.)
Disciplina 572.8/636
572.8636
Soggetto topico DNA microarrays
ISBN 1-280-55646-3
9786610556465
0-471-46118-0
0-471-22758-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Preface xi -- Acknowledgments xiii -- 1 Introduction I -- 1.1 Hybridization 1 -- 1.2 Affymetrix GeneChip Technology 3 -- 1.3 Spotted Arrays 6 -- 1.4 Serial Analysis of Gene Expression (SAGE) 8 -- 1.5 Example: Affymetrix vs. Spotted Arrays 9 -- 1.6 Summary 11 -- 1.7 Further Reading 13 -- 2 Overview of Data Analysis 15 -- 3 Basic Data Analysis 17 -- 3.1 Absolute Measurements 17 -- 3.2 Scaling 18 -- 3.2.1 Example: Linear and Nonlinear Scaling 20 -- 3.3 Detection of Outliers 20 -- 3.4 Fold Change 21 -- 3.5 Significance 22 -- 3.5.1 Nonparametric Tests 24 -- 3.5.2 Correction for Multiple Testing 24 -- 3.5.3 Example I: t-Test and ANOVA 25 -- 3.5.4 Example II: Number of Replicates 26 -- 3.6 Summary 28 -- 3.7 Further Reading 29 -- 4 Visualization by Reduction of Dimensionality 33 -- 4.1 Principal Component Analysis 33 -- 4.2 Example 1: PCA on Small Data Matrix 35 -- 4.3 Example 2: PCA on Real Data 37 -- 4.4 Summary 37 -- 4.5 Further Reading 39 -- 5 Cluster Analysis 41 -- 5.1 Hierarchical Clustering 41 -- 5.2 K-means Clustering 43 -- 5.3 Self-Organizing Maps 44 -- 5.4 Distance Measures 45 -- 5.4.1 Example: Comparison of Distance Measures 47 -- 5.5 Normalization 49 -- 5.6 Visualization of Clusters 50 -- 5.6.1 Example: Visualization of Gene Clusters in -- Bladder Cancer 50 -- 5.7 Summary 50 -- 5.8 Further Reading 52 -- 6 Beyond Cluster Analysis 55 -- 6.1 Function Prediction 55 -- 6.2 Discovery of Regulatory Elements in Promoter -- Regions 56 -- 6.2.1 Example 1: Discovery of Proteasomal Element 57 -- 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle -- Box (MCB) 57 -- 6.3 Integration of data 58 -- 6.4 Summary 59 -- 6.5 Further Reading 59 -- 7 Reverse Engineering of Regulatory Networks 63 -- 7.1 The Time-Series Approach 63 -- 7.2 The Steady-State Approach 64 -- 7.3 Limitations of Network Modeling 65 -- 7.4 Example 1: Steady-State Model 65 -- 7.5 Example 2: Steady-State Model on Real Data 66 -- 7.6 Example 3: Steady-State Model on Real Data 68 -- 7.7 Example 4: Linear Time-Series Model 68 -- 7.8 Further Reading 71 -- 8 Molecular Classifiers 75 -- 8.1 Classification Schemes 76 -- 8.1.1 Nearest Neighbor 76 -- 8.1.2 Neural Networks 76 -- 8.1.3 Support Vector Machine 76 -- 8.2 Example I: Classification of Cancer Subtypes 77 -- 8.3 Example II: Classification of Cancer Subtypes 78 -- 8.4 Summary 79 -- 8.5 Further Reading 79 -- 9 Selection of Genes for Spotting on Arrays 81 -- 9.1 Gene Finding 82 -- 9.2 Selection of Regions Within Genes 82 -- 9.3 Selection of Primers for PCR 83 -- 9.4 Selection of Unique Oligomer Probes 83 -- 9.4.1 Example: Finding PCR Primers for Gene -- AF105374 83 -- 9.5 Experimental Design 84 -- 9.6 Further Reading 84 -- 10 Limitations of Expression Analysis 87 -- 10.1 Relative VersusAbsoluteRNA Quantification 88 -- 10.2 Further Reading 88 -- 11 Genotyping Chips 91 -- 11.1 Example: NeuralNetworksfor GeneChipprediction 91 -- 11.2 Further Reading 93 -- 12 Software Issues and Data Formats 95 -- 12.1 Standardization Efforts 96 -- 12.2 Standard File Format 97 -- 12.2.1 Example: Small Scripts in Awk 97 -- 12.3 Software for Clustering 98 -- 12.3.1 Example: Clustering with ClustArray 99 -- 12.4 Software for Statistical Analysis 99 -- 12.4.1 Example: StatisticalAnalysis with R 99 -- 12.4.2 The affyR Software Package 103 -- 12.4.3 Commercial Statistics Packages 103 -- 12.5 Summary 103 -- 12.6 Further Reading 104 -- 13 Commercial Software Packages 105 -- 14 Bibliography 109 -- Index 123.
Record Nr. UNINA-9910830115503321
Knudsen Steen  
New York, : Wiley-Interscience, c2002
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui