Methods of Microarray Data Analysis II
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Simon M. Lin, Kimberly F. Johnson (Eds.)
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Methods of Microarray Data Analysis II
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Kluwer Academic Publishers
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9780306475986
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1
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CHF 96.80
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Naturwissenschaft
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English
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214
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Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques.
Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
Writ en for:
Academic and industrial researchers
5 EXTRACTING GLOBAL STRUCTURE FROM GENE EXPRESSION PROFILES
(p. 81-82)
Charless Fowlkes1, Qun Shan2, Serge Belongie3, and Jitendra Malik1
Departments of Computer Science1 and Molecular Cell Biology 2, University of California at
Berkeley, Department of Computer Science and Engineering, University of California at San Diego3
Abstract:
We have developed a program, GENECUT, for analyzing datasets from gene expression profiling. GENECUT is based on a pairwise clustering method known as Normalized Cut (Shi and Malik, 1997). GENECUT extracts global structures by progressively partitioning datasets into well-balanced groups, performing an intuitive k-way partitioning at each stage in contrast to commonly used 2-way partitioning schemes. By making use of the Nyström approximation, it is possible to perform clustering on very large genomic datasets.
Key words:
gene expression profiles, clustering analysis, spectral partitioning
1. INTRODUCTION
DNA microarray technology empowers biologists to analyze thousands of mRNA transcripts in parallel, providing insights about the cellular states of tumor cells, the effect of mutations and knockouts, progression of the cell cycle, and reaction to environmental stresses or drug treatments. Gene expression profiles also provide the necessary raw data to interrogate cellular transcription regulation networks. Efforts have been made in identifying cis acting elements based on the assumption that co-regulated genes have a higher probability of sharing transcription factor binding sites. There is a well-recognized need for tools that allow biologists to explore public domain microarray datasets and integrate insights gained into their own research. One important approach for structuring the exploration of gene expression data is to find coherent clusters of both genes and experimental conditions. The association of unknown genes with functionally well-characterized genes will guide the formation of hypotheses and suggest experiments to uncover the function of these unknown genes. Similarly, experimental conditions that cluster together may affect the same regulatory pathway.
Unsupervised clustering is a classical data analysis problem that is still an active area of intensive research in the computer science and statistics communities (Ripley, 1996). Broadly speaking, the goal of clustering is to partition a set of feature vectors into k groups such that the partition is"good" according to some cost function. In the case of genes, the feature vector is usually the degree of induction or suppression over some set of experimental conditions. As of yet, there is no clear consensus as to which algorithms are most suitable for gene expression data.
Clustering methods generally fall into one of two categories: central or pairwise (Buhmann, 1995). Central clustering is based on the idea of prototypes, wherein one finds a small number of prototypical feature vectors to serve as"cluster centers". Feature vectors are then assigned to the most similar cluster center. Pairwise methods are based directly on the distances between all pairs of feature vectors in the data set. Pairwise methods don’t require one to solve for prototypes, which provides certain advantages over central methods. For example, when the shape of the clusters are not simple, compact clouds in feature space, central methods are ill-suited while pairwise methods perform well since similarity is allowed to propagate in a transitive fashion from neighbor to neighbor. A family of genes related by a series of small mutations might well exhibit this sort of structure, particularly when features are based on sequence data. Clustering algorithms can also often be characterized as greedy or global in nature. The agglomerative clustering method used by Eisen et al. (1998) to order microarray data is an example of a greedy pairwise method: it starts with a full matrix of pairwise distances, locates the smallest value, merges the corresponding pair, and repeats until the whole dataset has been merged into a single cluster. Because this type of process only considers the closest pair of data points at each step, global structure present in the data may not be handled properly.
Contents
5
Contributors
7
Acknowledgements
9
Preface
10
INTRODUCTION
11
CAMDA 2001 Data Sets
12
Feature Selection and Extraction
12
Clustering Strategies
13
Modeling Complex Systems
14
Ontologies, Semantic Understanding, and Functional Genomics
15
A standard protocol?
16
Web Companion
17
1 AN INTRODUCTION TO DNA MICROARRAYS
18
1. INTRODUCTION TO FUNCTIONAL GENOMICS
18
2. MICROARRAY TECHNOLOGY
19
3. MICROARRAY DATA
22
4. MICROARRAY EXPERIMENT GOALS
23
5. MICROARRAY EXPERIMENTAL DESIGN
25
6. MICROARRAY DATA ANALYSIS
26
7. RESULT VALIDATION
27
7.1 Sample and Data Triage
27
7.2 Statistical Validation
27
7.3 Biological Validation
28
8. CONCLUSION
28
REFERENCES
29
2 EXPERIMENTAL DESIGN FOR GENE MICROARRAY EXPERIMENTS AND DIFFERENTIAL EXPRESSION ANALYSIS
31
1. INTRODUCTION
31
2. DESIGN OF MICROARRAY EXPERIMENTS
32
2.1 Biological variation
33
2.2 Technological variations
34
2.3 Microarray quality checklist
36
3. EXPERIMENTAL DESIGNS THAT INCORPORATE BIOLOGICAL AND TECHNOLOGICAL VARIATION
37
3.1 Block designs
37
3.2 Randomization
38
3.3 Loop designs
38
3.4 Split plot designs
39
3.5 Optimal designs
40
4. DESIGN OF MICROARRAYS
41
5. NORMALIZATION MODELS
42
5.1 Data transformation and background removal
42
5.2 Linear vs. non-linear effects
43
5.3 Random vs. fixed effects
43
5.4 Ordinary least squares vs. orthogonal regression
43
5.5 Means vs. medians
43
5.6 Self-consistency
44
5.7 Flagging outliers
44
6. DIFFERENTIAL EXPRESSION
44
6.1 Error models
45
6.2 Bayesian approach
45
6.3 Adjustment for multiple comparisons and power considerations
46
7. FINAL REMARKS
46
ACKNOWLEDGEMENTS
47
REFERENCES
47
3 MICROARRAY DATA PROCESSING AND ANALYSIS
50
1. INTRODUCTION
50
2. DESIGN OF THE ARRAY
51
3. DATA ACQUISITION AND IMAGE ANALYSIS
53
4. NORMALISATION AND FILTERING
54
5. DATA STORAGE
55
6. ADDRESSING BIOLOGICAL QUESTIONS
57
7. DATA ANALYSIS
58
7.1 Two conditions comparison
58
7.2 Multiple conditions comparison.
58
7.3 Gene networks
64
8. CONCLUSIONS AND FUTURE PROSPECTS
65
REFERENCES
66
4 BIOLOGY-DRIVEN CLUSTERING OF MICROARRAY DATA
71
1. INTRODUCTION
71
2. THE ANNOTATION PROBLEM
72
2.1 Reannotating the Spots
72
2.2 Finding Functional Categories
73
3. PRELIMINARY ANALYSIS
76
3.1 Data Preprocessing
76
3.2 Updating Cell Line Classifications
78
3.3 Choosing a Distance Metric
79
4. CHROMOSOMAL CLUSTERING
80
5. FUNCTIONAL CLUSTERING
82
6. CONCLUSIONS
83
ACKNOWLEDGEMENTS
85
REFERENCES
85
5 EXTRACTING GLOBAL STRUCTURE FROM GENE EXPRESSION PROFILES
86
1. INTRODUCTION
86
2. CLUSTERING WITH NORMALIZED CUT
88
2.1 The NCut Criterion
88
2.2 K-Way Partitioning
89
2.3 Clustering Large Datasets
90
3. RESULTS
90
4. CONCLUSIONS
94
REFERENCES
94
6 SUPERVISED NEURAL NETWORKS FOR CLUSTERING CONDITIONS IN DNA ARRAY DATA AFTER REDUCING NOISE BY CLUSTERING GENE EXPRESSION PROFILES
96
1. INTRODUCTION
96
2. COMPARATIVE PERFORMANCES OF CLUSTERING METHODS
98
2.1 Data set used
98
2.2 Comparative runtimes
98
2.3 Comparative accuracy
100
2.4 Conclusions on comparative performances
101
3. CLUSTERING OF CONDITIONS
102
3.1 The problem of noisy patterns
102
3.2 Clustering of conditions and noise reduction
103
4. CONCLUSIONS
107
ACKNOWLEDGEMENTS
107
REFERENCES
107
7 BAYESIAN DECOMPOSITION ANALYSIS OF GENE EXPRESSION IN YEAST DELETION MUTANTS
109
1. INTRODUCTION
110
1.1 The Development of Cancer
110
1.2 Microarray Measurements and Analysis
110
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