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E-raamat: Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

(Johnson & Johnson Pharmaceutical R&D, NJ), (Rutgers University, NJ),
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Exploration and Analysis of DNA Microarray and Other High-Dimensional DataSuurem pilt
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Amaratunga, Cabrera and Shkedy describe several computational, visual, and statistical tools being used to analyze the mountains of data produced by DNA microarray experiments and extract useful information about biological processes. They cover genomics basics, microarrays, processing the scanned image, preprocessing microarray data, summarization, two-group comparative experiments, model-based inference and experimental design considerations, analyzing gene sets, pattern discovery, class prediction, and protein arrays. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Praise for the First Edition

“…extremely well written…a comprehensive and up-to-date overview of this important field.” – Journal of Environmental Quality

Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity.

The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features:

  • A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis
  • New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods
  • Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study

The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.

Arvustused

Featuring new information on interpretation of findings, class prediction, ABC clustering, limma for mixed models, biclustering, mass spectrometry, tracking Spearman correlations, and more, this \extremely well written" (Journal of Environmental Quality) book is a choice reference for scientists, teachers, and students interested in DNA data analysis.  (Zentralblatt MATH, 1 October 2014)

In summary this is an excellent text for both life scientist and computer/mathematicians.  Highly recommended.  (Scientific Computing, 1 August 2014)

 

Preface xv
1 A Brief Introduction 1(9)
1.1 A Note on Exploratory Data Analysis,
3(1)
1.2 Computing Considerations and Software,
4(1)
1.3 A Brief Outline of the Book,
5(1)
1.4 Data Sets and Case Studies,
6(4)
1.4.1 The Golub Data,
6(1)
1.4.2 The Mouses Data,
7(1)
1.4.3 The Khan Data,
7(1)
1.4.4 The Sialin Data,
7(1)
1.4.5 The Behavioral Study Data,
8(1)
1.4.6 The Spiked-In Data,
8(1)
1.4.7 The APOAI Study,
8(1)
1.4.8 The Breast Cancer Data,
8(1)
1.4.9 Platinum Spike Data Set,
9(1)
1.4.10 Human Epidermal Squamous Carcinoma Cell Line A431 Experiment,
9(1)
1.4.11 Note: Public Repositories of Microarray Data,
9(1)
2 Genomics Basics 10(15)
2.1 Genes,
10(1)
2.2 Deoxyribonucleic Acid,
11(1)
2.3 Gene Expression,
12(2)
2.4 Hybridization Assays and Other Laboratory Techniques,
14(2)
2.5 The Human Genome,
16(1)
2.6 Genome Variations and Their Consequences,
17(2)
2.7 Genomics,
19(1)
2.8 The Role of Genomics in Pharmaceutical Research and Clinical Practice,
19(3)
2.9 Proteins,
22(1)
2.10 Bioinformatics,
23(1)
Supplementary Reading,
24(1)
3 Microarrays 25(14)
3.1 Types of Microarray Experiments,
26(4)
3.1.1 Experiment Type 1: Tissue-Specific Gene Expression,
26(1)
3.1.2 Experiment Type 2: Developmental Genetics,
26(1)
3.1.3 Experiment Type 3: Genetic Diseases,
27(1)
3.1.4 Experiment Type 4: Complex Diseases,
28(1)
3.1.5 Experiment Type 5: Pharmacological Agents,
28(1)
3.1.6 Experiment Type 6: Plant Breeding,
29(1)
3.1.7 Experiment Type 7: Environmental Monitoring,
29(1)
3.2 A Very Simple Hypothetical Microarray Experiment,
30(1)
3.3 A Typical Microarray Experiment,
31(4)
3.3.1 Microarray Preparation,
32(1)
3.3.2 Sample Preparation,
33(1)
3.3.3 The Hybridization Step,
33(1)
3.3.4 Scanning the Microarray,
34(1)
3.3.5 Interpreting the Scanned Image,
34(1)
3.4 Multichannel cDNA Microarrays,
35(1)
3.5 Oligonucleotide Microarrays,
36(1)
3.6 Bead-Based Arrays,
37(1)
3.7 Confirmation of Microarray Results,
37(1)
Supplementary Reading and Electronic References,
37(2)
4 Processing the Scanned Image 39(21)
4.1 Converting the Scanned Image to the Spotted Image,
39(3)
4.1.1 Gridding,
40(1)
4.1.2 Segmentation,
40(1)
4.1.3 Quantification,
41(1)
4.2 Quality Assessment,
42(7)
4.2.1 Visualizing the Spotted Image,
43(1)
4.2.2 Numerical Evaluation of Array Quality,
44(1)
4.2.3 Spatial Problems,
45(1)
4.2.4 Spatial Randomness,
46(1)
4.2.5 Quality Control of Arrays,
47(1)
4.2.6 Assessment of Spot Quality,
48(1)
4.3 Adjusting for Background,
49(4)
4.3.1 Estimating the Background,
49(3)
4.3.2 Adjusting for the Estimated Background,
52(1)
4.4 Expression-Level Calculation for Two-Channel cDNA Microarrays,
53(1)
4.5 Expression-Level Calculation for Oligonucleotide Microarrays,
53(5)
4.5.1 The Average Difference,
54(1)
4.5.2 A Weighted Average Difference,
54(1)
4.5.3 Perfect Matches Only,
55(1)
4.5.4 Background Adjustment Approach,
56(1)
4.5.5 Model-Based Approach,
56(1)
4.5.6 Absent-Present Calls,
57(1)
Supplementary Reading,
58(1)
Software Notes,
58(2)
5 Preprocessing Microarray Data 60(26)
5.1 Logarithmic Transformation,
60(2)
5.2 Variance Stabilizing Transformations,
62(1)
5.3 Sources of Bias,
63(1)
5.4 Normalization,
63(2)
5.5 Intensity-Dependent Normalization,
65(12)
5.5.1 Smooth Function Normalization,
68(1)
5.5.2 Quantile Normalization,
68(3)
5.5.3 Stagewise Normalization,
71(1)
5.5.4 Normalization of Two-Channel Arrays,
71(5)
5.5.5 Spatial Normalization,
76(1)
5.5.6 Normalization of Oligonucleotide Arrays,
76(1)
5.6 Judging the Success of a Normalization,
77(3)
5.7 Outlier Identification,
80(1)
5.8 Nonresistant Rules for Outlier Identification,
80(1)
5.9 Resistant Rules for Outlier Identification,
81(2)
5.10 Assessing Replicate Array Quality,
83(1)
Software Notes,
83(3)
6 Summarization 86(23)
6.1 Replication,
86(1)
6.2 Technical Replicates,
87(4)
6.3 Biological Replicates,
91(1)
6.4 Experiments with Both Technical and Biological Replicates,
91(4)
6.5 Multiple Oligonucleotide Arrays,
95(1)
6.6 Estimating Fold Change in Two-Channel Experiments,
96(1)
6.7 Bayes Estimation of Fold Change,
97(1)
6.8 Estimating Fold Change Affymetrix Data,
98(2)
6.9 RMA Summarization of Multiple Oligonucleotide Arrays Revisited,
100(5)
6.10 Factor Analysis for Robust Microarray Summarization, FARMS,
105(1)
Software Notes,
106(3)
7 Two-Group Comparative Experiments 109(57)
7.1 Basics of Statistical Hypothesis Testing,
111(1)
7.2 Fold Changes,
112(2)
7.3 The Two-Sample t-Test,
114(2)
7.4 Diagnostic Checks,
116(2)
7.5 Robust t-Tests,
118(1)
7.6 The Mann-Whitney-Wilcoxon Rank Sum Test,
119(2)
7.7 Multiplicity Adjustment: The Familywise Error Rate,
121(4)
7.7.1 A Pragmatic Approach to the Issue of Multiplicity,
122(1)
7.7.2 Simple Multiplicity Adjustments,
123(1)
7.7.3 Sequential Multiplicity Adjustments,
123(2)
7.8 Multiplicity Adjustment: The False Discovery Rate,
125(5)
7.8.1 Benjamini and Hochberg Procedure,
125(4)
7.8.2 The Positive False Discovery Rate,
129(1)
7.9 Resampling-Based Multiple Testing Procedures,
130(2)
7.10 Small-Variance-Adjusted t-Tests and SAM,
132(9)
7.10.1 Modifying the t-Statistic,
135(1)
7.10.2 Assessing Significance with the SAM t Statistic,
135(3)
7.10.3 Strategies for Using SAM,
138(1)
7.10.4 An Empirical Bayes Framework,
139(1)
7.10.5 Understanding the SAM Adjustment,
139(2)
7.11 Conditional t,
141(4)
7.12 Borrowing Strength Across Genes,
145(4)
7.12.1 Simple Methods,
147(1)
7.12.2 A Bayesian Model,
148(1)
7.13 Two-Channel Experiments,
149(3)
7.13.1 The Paired Sample t Test and SAM,
150(1)
7.13.2 Borrowing Strength Via Hierarchical Modeling,
150(2)
7.14 Filtering,
152(8)
7.14.1 Filtering Based on Summarized Data,
152(4)
7.14.2 Filtering Based on Probe-Level Data,
156(4)
Supplementary Reading,
160(1)
Software Notes,
161(5)
8 Model-Based Inference and Experimental Design Considerations 166(34)
8.1 The F-Test,
167(1)
8.2 The Basic Linear Model,
168(3)
8.3 Fitting the Model in Two Stages,
171(1)
8.4 Multichannel Experiments,
171(1)
8.5 Experimental Design Considerations,
172(5)
8.5.1 Comparing Two Varieties with Two-Channel Microarrays,
172(2)
8.5.2 Comparing Multiple Varieties with Two-Channel Microarrays,
174(2)
8.5.3 Single-Channel Microarray Experiments,
176(1)
8.6 Miscellaneous Issues,
177(1)
8.7 Model-Based Analysis of Affymetrix Arrays,
177(18)
8.7.1 One-Way ANOVA,
177(2)
8.7.2 Linear Models for Microarray Data (Limma),
179(5)
8.7.3 A Joint Model for Gene Expression and Response,
184(3)
8.7.4 Analysis of Dose-Response Microarray Experiments,
187(4)
8.7.5 Analysis of Time Course Data,
191(4)
Supplementary Reading,
195(1)
Software Notes,
196(4)
9 Analysis of Gene Sets 200(10)
9.1 Methods for Identifying Enriched Gene Sets,
201(4)
9.1.1 MLP and Fisher's Test,
202(3)
9.1.2 GSEA and the Kolmogorov-Smirnov Test,
205(1)
9.2 ORA and Fisher'S Exact Test,
205(1)
9.3 Interpretation of Results,
206(1)
9.4 Example,
206(1)
Software Notes,
206(4)
10 Pattern Discovery 210(40)
10.1 Initial Considerations,
210(2)
10.2 Cluster Analysis,
212(17)
10.2.1 Dissimilarity Measures and Similarity Measures,
213(2)
10.2.2 Guilt by Association,
215(1)
10.2.3 Hierarchical Clustering,
216(6)
10.2.4 Partitioning Methods,
222(4)
10.2.5 Model-Based Clustering,
226(1)
10.2.6 Chinese Restaurant Clustering,
227(1)
10.2.7 Ensemble Methods for Clustering Samples,
228(1)
10.2.8 Discussion,
229(1)
10.3 Seeking Patterns Visually,
229(13)
10.3.1 Principal Components Analysis,
230(4)
10.3.2 Factor Analysis,
234(3)
10.3.3 Biplots,
237(1)
10.3.4 Spectral Map Analysis,
237(2)
10.3.5 Multidimensional Scaling,
239(1)
10.3.6 Projection Pursuit,
239(2)
10.3.7 Data Visualization with the Grand Tour and Projection Pursuit,
241(1)
10.4 Biclustering,
242(5)
10.4.1 Block Clustering,
243(1)
10.4.2 Gene Shaving,
244(1)
10.4.3 The Plaid Model,
244(3)
Supplementary Reading,
247(1)
Software Notes,
247(3)
11 Class Prediction 250(40)
11.1 Initial Considerations,
251(7)
11.1.1 Misclassification Rates,
251(1)
11.1.2 Reducing the Number of Classifiers,
252(6)
11.2 Linear Discriminant Analysis,
258(3)
11.3 Extensions of Fisher's LDA,
261(2)
11.4 Penalized Methods,
263(1)
11.5 Nearest Neighbors,
264(1)
11.6 Recursive Partitioning,
265(5)
11.6.1 Classification Trees,
267(2)
11.6.2 Activity Region Finding,
269(1)
11.7 Ensemble Methods,
270(3)
11.7.1 Random Forest,
271(1)
11.7.2 Enriched Random Forest,
272(1)
11.8 Enriched Ensemble Classifiers,
273(1)
11.9 Neural Networks,
273(3)
11.10 Support Vector Machines,
276(1)
11.11 Generalized Enriched Methods,
277(8)
11.11.1 Enriched Principal Components Analysis and Biplots,
279(2)
11.11.2 Enriched Penalized Methods: Lasso, SVM, P-SVM,
281(2)
11.11.3 Enriched Partial Least Squares (PLS),
283(2)
11.12 Integration of Genome Information,
285(1)
11.12.1 Integration of Gene Expression Data and Molecular Structure Data,
285(1)
11.12.2 Pathway Inference,
285(1)
Supplementary Reading,
286(1)
Software Notes,
286(4)
12 Protein Arrays 290(8)
12.1 Introduction,
290(1)
12.2 Protein Array Experiments,
291(1)
12.3 Special Issues with Protein Arrays,
292(1)
12.4 Analysis,
293(1)
12.5 Using Antibody Antigen Arrays to Measure Protein Concentrations,
294(4)
References 298(15)
Index 313
DHAMMIKA AMARATUNGA, PhD, is Senior Director and Janssen Fellow in the Nonclinical Statistics and Computing Department at Janssen R&D, a Johnson & Johnson pharmaceutical company. His research interests include analysis of large multivariate data sets generated by functional genomics research, robust and resistant statistical methods, linear and nonlinear modeling, and biostatistics.

JAVIER CABRERA, PhD, is Full Professor in the Department of Statistics at Rutgers University. He has published over 100 articles in his areas of research interest, which include DNA microarray, data mining of biopharmaceutical databases, computer vision, statistical computing and graphics, robustness, and biostatistics. He has also lectured at Cold Spring Harbor Laboratory, The Hong Kong University of Science and Technology, and National University of Singapore.

ZIV SHKEDY, PhD, is Associate Professor and Statistical Consultant in the Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Center for Statistics at Hasselt University, Belgium. He has published numerous journal articles on the topics of clinical and non-clinical trials, modeling infectious disease data, dose-response analysis, Bayesian modeling, bioinformatics, and analysis of gene expression data.
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