Microarray Quality Control
Microarray Quality Control provides a comprehensive resource for ensuring quality control in every step of this complex process. From experimental design to data processing, analysis, and interpretation, the emphasis in this text remains on practical advice for each stage of planning and running a microarray study. Chapters cover:
* Quality of biological samples
* Quality of DNA
* Hybridization protocols Scanning
* Data acquisition
* Image analysis
* Data analysis
Written for the broad group of workers-biologists, mathematicians, statisticians, engineers, physicians, and computational scientists-involved in microarray studies, Microarray Quality Control features a straightforward style easily accessed by various disciplines. Useful checklists and tips help ensure the integrity of results, and each chapter contains a thorough review of pertinent literature.
The only complete, systematic treatment of the topic available, Microarray Quality Control offers students and practitioners an invaluable resource for improving experimental quality and efficiency.
1. Quality of Biological Samples.
1.1 Tissue Acquisition, Handling, and Storage.
1.2 Pathological Evaluation.
1.3 Tissue heterogeneity and Laser Capture Microdissection.
2. Microarray Production: Quality of DNA and Printing.
2.1 Quality Control for cDNA Probes.
2.2 Long-oligo Arrays.
2.3 Slide Coating.
2.4 Slide Autofluorescence.
2.5 Printing Quality Control.
3. Quality of Microarray Hybridization.
3.1 RNA and cDNA Labeling Quality.
3.2 Specificity versus Sensitivity.
3.3 Amplification Strategies.
3.4 Indirect Labeling.
3.5 Automation of Microarray Hybridization.
3.6 Hybridization Reference.
3.7 Validation of Microarray Experiments.
4. Scanners and Data Acquisition.
4.1 Basic Principles of Scanners.
4.2 Basic Principles of Imagers.
4.3 Calibration and PMT Gain.
4.4 Characteristics of Different Noise Sources.
5. Image Analysis.
5.1 Grid Alignment.
5.2 Spot Segmentation.
5.3 Extracting Information.
5.4 Appendix: Image Processing.
6. Quality Control in Data Analysis.
6.3 Missing Values.
6.4 Data Management Issues.
6.5 Small-Sample-Size Issues.
Ilya Shmulevich, Ph.D., received his Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, Indiana in 1997. In 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. Presently, he is an Assistant Professor at the Cancer Genomics Laboratory at The University of Texas M. D. Anderson Cancer Center in Houston, TX. He is an Associate Editor of Environmental Health Perspectives: Toxicogenomics. His research interests include computational genomics, nonlinear signal and image processing, computational learning theory, and music recognition and perception.
Jaakko Astola, Ph.D., received his B.Sc., M.Sc. and Ph.D. degrees from Turku University, Finland. He has worked at Turku University, the Research Institute for Mathematical Sciences of Kyoto University, Kyoto, Japan, Lappeenranta University of Technology, Lappeenranta, Finland, and Eindhoven University of Technology, The Netherlands. From 1987 to 1992 he was Associate Professor in Applied Mathematics at Tampere University, Tampere, Finland. From 1993 he has been Professor of Signal Processing at Tampere University of Technology leading a group of about 60 scientists and was nominated Academy Professor by Academy of Finland (2001-2006). His research group was elected as a Centre of Excellence in Research by the Academy of Finland for the years 2000-2005. He is also director of Tampere International Center for Signal Processing (founded 1997) that has already hosted numerous visiting scientists in Signal Processing as well as organized workshops and conferences. He has about 25 years of experience in teaching both undergraduate and graduate level. He has likewise 25 years of experience in scientific research in mathematics, communications theory, and signal processing. He has authored over 400 papers and four textbooks on signal processing and its applications.
"The book may be recommended for all researchers and students working with the DNA microarray technology" (Annals of Biomedical Engineering, June 2005)
"...a useful source of ideas and potential issues." (Journal of the American Statistical Association, June 2005)
"This book is recommended [for] all libraries serving researchers using high-throughput techniques." (E-STREAMS, March 2005)
“…an essential laboratory resource for scientists studying gene regulation and for all experimental biologists interested in the emerging practical applications of microarrays.” (Engineering in Life Sciences, Vol.5, No.5, 2004)
"…offers practical guidance for each stage of planning and execution of microarray studies…" (Genetic Engineering News, Vol. 24, No. 6, March 15, 2004)
"…the breadth of issues encompassed by quality control in microarray studies are covered…a valuable systematic treatment of microarray quality control." (Drug Discovery and Development, March 2004)