Microarrays for an Integrative Genomics (Paperback)

Foreword	xi
	Preface	xiii
	Acknowledgments	xvii
1	Introduction	1
1.1	The Future Is So Bright...	1
1.2	Functional Genomics	4
1.3	Missing the Forest for the Dendrograms	13
1.4	Functional Genomics, Not Genetics	19
1.5	Basic Biology	25
2	Experimental Design	37
2.1	The Safe Conception of a Functional Genomic Experiment	37
2.2	Gene-Clustering Dogma	60
3	Microarray Measurements to Analyses	69
3.1	Generic Features of Microarray Technologies	69
3.2	Replicate Experiments, Reproducibility, and Noise	88
3.3	Prototypical Objectives and Questions	116
3.4	Preprocessing: Filters and Normalization	121
3.5	Background on Fold	127
3.6	Dissimilarity and Similarity Measures	137
4	Genomic Data-Mining Techniques	149
4.1	Introduction	149
4.2	What Can Be Clustered in Functional Genomics?	149
4.3	What Does it Mean to Cluster?	150
4.4	Hierarchy of Bioinformatics Algorithms	151
4.5	Data Reduction and Filtering	155
4.6	Self-Organizing Maps	161
4.7	Finding Genes That Split Sets	169
4.8	Phylogenetic-Type Trees	172
4.9	Relevance Networks	181
4.10	Other Methods	189
4.11	Which Technique Should I Use?	191
4.12	Determining the Significance of Findings	195
4.13	Genetic Networks	203
5	Bio-Ontologies, Data Models, Nomenclature	215
5.1	Ontologies	216
5.2	Expressivity versus Computability	224
5.3	Ontology versus Data Model versus Nomenclature	226
5.4	Data Model Introduction	231
5.5	Nomenclature	239
5.6	Postanalysis Challenges	247
6	From Functional Genomics to Clinical Relevance	249
6.1	Electronic Medical Records	249
6.2	Standardized Vocabularies for Clinical Phenotypes	251
6.3	Privacy of Clinical Data	252
6.4	Costs of Clinical Data Acquisition	256
7	The Near Future	257
7.1	New Methods for Gene Expression Profiling	257
7.2	Respecting the Older Generation	266
7.3	Selecting Software	271
7.4	Investing in the Future of the Genomic Enterprise	273
	Glossary	277
	References	283
	Index	296