A cutting-edge guide to the analysis of DNA microarray data
Genomics is one of the major scientific revolutions of this century, and the use of microarrays to rapidly analyze numerous DNA samples has enabled scientists to make sense of mountains of genomic data through statistical analysis. Today, microarrays are being used in biomedical research to study such vital areas as a drug’s therapeutic value–or toxicity–and cancer-spreading patterns of gene activity.
Exploration and Analysis of DNA Microarray and Protein Array Data answers the need for a comprehensive, cutting-edge overview of this important and emerging field. The authors, seasoned researchers with extensive experience in both industry and academia, effectively outline all phases of this revolutionary analytical technique, from the preprocessing to the analysis stage.
Highlights of the text include:
- A review of basic molecular biology, followed by an introduction to microarrays and their preparation
- Chapters on processing scanned images and preprocessing microarray data
- Methods for identifying differentially expressed genes in comparative microarray experiments
- Discussions of gene and sample clustering and class prediction
- Extension of analysis methods to protein array data
Numerous exercises for self-study as well as data sets and a useful collection of computational tools on the authors’ Web site make this important text a valuable resource for both students and professionals in the field.
DHAMMIKA AMARATUNGA, PhD, is a Senior Research Fellow in the Nonclinical Biostatistics Department at Johnson & Johnson Pharmaceutical Research & Development, LLC. He has a doctorate in statistics from Princeton University and has been working in the pharmaceutical industry for over fifteen years. His research interests include analysis of large multivariate data sets, particularly those generated by functional genomics research, robust and resistant statistical methods, linear and nonlinear modeling, and biostatistics.
JAVIER CABRERA, PhD, is an Associate Professor in the Department of Statistics at Rutgers University. He has a doctorate in statistics from Princeton University and has over fifty publications in applied statistics. His research interests include DNA microarray, data mining of biopharmaceutical databases, computer vision, statistical computing and graphics, robustness, and biostatistics.
