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.