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Microarrays for an Integrative Genomics Computational Molecular Biology - Isaac S. Kohane, Alvin Kho, Atul J. Butte

Contents


Chapter 1: Introduction....................................................................................................................7
1.1 The Future Is So Bright…..................................................................................................7
1.2 Functional Genomics.........................................................................................................9
1.2.1 Informatics and advances in enabling technology..................................................12
1.2.2 Why do we need new techniques?.........................................................................14
1.3 Missing the Forest for the Dendrograms, or One Aspect of Integrative Genomics..........15
1.3.1 Sociology of a functional genomics pipeline...........................................................18
1.4 Functional Genomics, Not Genetics................................................................................19
1.4.1 In silico analysis will never substitute for in vitro and in vivo...................................21
1.5.1 Biological caveats in mRNA measurements...........................................................23
1.5.2 Sequence−level genomics......................................................................................27
1.5.3 Proteomics..............................................................................................................29
1.5 Basic Biology...................................................................................................................29

Chapter 2: Experimental Design....................................................................................................32
2.1 The Safe Conception of a Functional Genomic Experiment............................................32
2.1.1 Experiment design space........................................................................................32
2.1.2 Expression space....................................................................................................34
2.1.3 Exercising the expression space.............................................................................38
2.1.4 Discarding data and low−hanging fruit....................................................................44
2.2 Gene−Clustering Dogma.................................................................................................48
2.2.1 Supervised versus unsupervised learning..............................................................49
2.2.2 Figure of merit: The elusive gold standard in functional genomics.........................51
Chapter 3: Microarray Measurements to Analyses......................................................................55
3.1 Generic Features of Microarray Technologies.................................................................55
3.1.1 Robotically spotted microarrays..............................................................................58
3.1.2 Oligonucleotide microarrays....................................................................................60
3.2 Replicate Experiments, Reproducibility, and Noise.........................................................70
3.2.1 What is a replicate experiment? A reproducible experimental outcome?...............71
3.2.2 Reproducibility across repeated microarray experiments: Absolute expression
level and fold difference............................................................................................72
3.2.3 Cross−plat form (technology) reproducibility...........................................................75
3.2.4 Pooling sample probes and PCR for replicate experiments....................................76
3.2.5 What is noise?.........................................................................................................77
3.2.6 Sources and examples of noise in the generic microarray experiment...................78
3.2.7 Biological variation as noise: The Human Genome Project and irreproducibility
of expression measurements....................................................................................86
3.2.8 Managing noise.......................................................................................................87
3.3 Prototypical Objectives and Questions in Microarray Analyses.......................................90
3.3.1 Two examples: Inter−array and intra−array............................................................91
3.4 Preprocessing: Filters and Normalization........................................................................93
3.4.1 Normalization..........................................................................................................94

Chapter 3: Microarray Measurements to Analyses
3.5 Background on Fold.........................................................................................................98
3.5.1 Fold calculation and significance..........................................................................100
3.5.2 Fold change may not mean the same thing in different expression
measurement technologies.....................................................................................102
3.6 Dissimilarity and Similarity Measures............................................................................104
3.6.1 Linear correlation..................................................................................................105
3.6.2 Entropy and mutual information............................................................................106
3.6.3 Dynamics..............................................................................................................111
Chapter 4: Genomic Data−Mining Techniques...........................................................................114
4.1 Introduction...................................................................................................................114
4.2 What Can Be Clustered in Functional Genomics?.........................................................114
4.3 What Does it Mean to Cluster?......................................................................................115
4.4 Hierarchy of Bioinformatics Algorithms Available in Functional Genomics....................115
4.5 Data Reduction and Filtering.........................................................................................118
4.5.1 Variation filter........................................................................................................119
4.5.2 Low entropy filter...................................................................................................119
4.5.3 Minimum expression level filter.............................................................................122
4.5.4 Target ambiguity filter............................................................................................122
4.6 Self−Organizing Maps...................................................................................................123
4.6.1 K−means clustering..............................................................................................127
4.7 Finding Genes That Split Sets.......................................................................................129
4.8 Phylogenetic−Type Trees..............................................................................................131
4.8.1 Two−dimensional dendrograms............................................................................135
4.9 Relevance Networks......................................................................................................137
4.10 Other Methods.............................................................................................................144
4.11 Which Technique Should I Use?..................................................................................145
4.12 Determining the Significance of Findings.....................................................................148
4.12.1 Permutation testing.............................................................................................148
4.12.2 Testing and training sets.....................................................................................149
4.12.3 Performance metrics...........................................................................................151
4.12.4 Receiver operating characteristic curves............................................................152
4.13 Genetic Networks.........................................................................................................154
4.13.1 What is a genetic network?.................................................................................154
4.13.2 Reverse−engineering and modeling a genetic network using limited data.........154
4.13.3 Bayesian networks for functional genomics........................................................157
Chapter 5: Bio−Ontologies, Data Models, Nomenclature..........................................................163
Overview.............................................................................................................................163
5.1 Ontologies.....................................................................................................................164
5.1.1 Bio−ontology projects............................................................................................165
5.1.2 Advanced knowledge representation systems for bio−ontology...........................168
5.2 Expressivity versus Computability.................................................................................169
5.3 Ontology versus Data Model versus Nomenclature.......................................................171
5.3.1 Exploiting the explicit and implicit ontologies of the biomedical literature.............172
5.4 Data Model Introduction.................................................................................................176
5.5 Nomenclature.................................................................................................................181
5.5.1 The unique gene identifier.....................................................................................184
5.6 Postanalysis Challenges................................................................................................187

Chapter 5: Bio−Ontologies, Data Models, Nomenclature
5.6.1 Linking to downstream biological validation..........................................................187
5.6.2 Problems in determining the results......................................................................187
Chapter 6: From Functional Genomics to Clinical Relevance—Getting the Phenotype
Right..............................................................................................................................................189
6.1 Electronic Medical Records...........................................................................................189
6.2 Standardized Vocabularies for Clinical Phenotypes......................................................190
6.3 Privacy of Clinical Data..................................................................................................191
6.3.1 Anonymization.......................................................................................................192
6.3.2 Privacy rules..........................................................................................................193
6.4 Costs of Clinical Data Acquisition..................................................................................193
Chapter 7: The Near Future..........................................................................................................195
Overview.............................................................................................................................195
7.1 New Methods for Gene Expression Profiling.................................................................195
7.1.1 Electronic positioning of molecules: Nanogen......................................................197
7.1.2 Ink−jet spotting of arrays: Agilent..........................................................................198
7.1.3 Coded microbeads bound to oligonucleotides: Illumina........................................199
7.1.4 Serial Analysis of Gene Expression (SAGE).........................................................201
7.1.5 Parallel signature sequencing on microbead arrays: Lynx....................................203
7.1.6 Gel pad technology: Motorola...............................................................................203
7.2 Respecting the Older Generation..................................................................................203
7.2.1 The generation gap...............................................................................................204
7.2.2 Separating the wheat from the chaff.....................................................................205
7.2.3 A persistent problem.............................................................................................206
7.3 Selecting Software.........................................................................................................206
7.4 Investing in the Future of the Genomic Enterprise.........................................................209

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