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.
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
Hinrich Goehlmann and Willem Talloen work at Johnson & Johnson Pharmaceutical R&D as Principal Scientist and Senior Biostatistician, respectively.
Reviews for Gene Expression Studies Using Affymetrix Microarrays
... useful for practitioners working in gene expression studies ... a valuable self-contained introductory material presenting all aspects of gene expression studies using Affymetrix microarrays. The book is well-arranged. The inserted boxes explaining biological or statistical concepts help to make the book very readable. ... a well-written overview over a broad range of problems and solutions with the interpretation. ...-ISCB News, No. 51, June 2011 ...The book is very well organized and well written, and covers many of the major topics of microarrays experiments ... A nice feature of this book, and one which makes it very pleasant to read, is the use of full-color illustrations and plots, as well as boxes with some highlighted information, such as biological and statistical concepts. ...-Guilherme J.M. Rosa, Biometrics, December 2010 The target audience of this book is practicing biologists making use of microarray technology, but it may be of great interest to their statistician collaborators or statisticians new to the field. ... When I began investigating bioinformatics as a statistics graduate student several years ago, it would have saved me a great deal of time to have a single resource such as this to help me understand this aspect of the field. ... Chapter 5 ... essentially serves as a catalog of commonly applied statistical methods for gene expression (or more generally, high-dimensional) data. The breadth of this catalog is impressive. ... -Journal of the American Statistical Association, Sept. 2010, Vol. 105, No. 491 Written by a molecular biologist and a biostatistician with a combined decade of experience in practical expression profling experiments and data analyses, this text 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. -Zentralblatt MATH