The Affymetrix GeneChip (R) system is one of the most widely adapted microarray platforms. However, due to the overwhelming amount of information available, many Affymetrix users tend to stick to the default analysis settings and may end up drawing sub-optimal conclusions. Written by a molecular biologist and a biostatistician with a combined decade of experience in practical expression profiling experiments and data analyses, Gene Expression Studies Using Affymetrix Microarrays tears down the omnipresent language barriers among molecular biology, bioinformatics, and biostatistics by explaining the entire process of a gene expression study from conception to conclusion.
Truly Multidisciplinary: Merges Molecular Biology, Bioinformatics, and Biostatistics
This authoritative resource covers important technical and statistical pitfalls and problems, helping not only to explain concepts outside the domain of researchers, but to provide additional guidance in their field of expertise. The book also describes technical and statistical methods conceptually with illustrative, full-color examples, enabling those inexperienced with gene expression studies to grasp the basic principles.
Gene Expression Studies Using Affymetrix Microarrays provides novices with a detailed, yet focused introductory course and practical user guide. Specialized experts will also find it useful as a translation dictionary to understand other involved disciplines or to get a broader picture of microarray gene expression studies in general. Although focusing on Affymetrix gene expression, this globally relevant guide covers topics that are equally useful for other microarray platforms and other Affymetrix applications.
Hinrich Goehlmann and Willem Talloen work at Johnson & Johnson Pharmaceutical R&D as Principal Scientist and Senior Biostatistician, respectively.
Biological question Why gene expression? Biotechnological advancements Research Question Main types of research questions Affymetrix microarrays Probes Probe sets Array types Standard lab processes Affymetrix data quality Running the experiment Biological experiment Microarray experiment Data analysis preparation Data preprocessing Quality control Data analysis Why do we need statistics? The curse of high-dimensionality Gene filtering Unsupervised data exploration Detecting differential expression Supervised prediction Pathway analysis Other analysis approaches Presentation of results Data visualization Biological Interpretation Data publishing Reproducible research Pharmaceutical R&D The need for early indications Critical Path Initiative Drug Discovery Drug Development Clinical Trials Using R and Bioconductor R and Bioconductor R and Sweave R and Eclipse Automated array analysis Other software for microarray analysis Future Perspectives Co-analyzing different data types The microarrays of the future Next-gen sequencing: The end for microarrays? Bibliography