Analysis of Array Data and Clinical Validation of Array-Based Assays

Chapter

Abstract

High-throughput array-based assays have been widely used for diagnostics and biomarker discovery. However, the development of these assays requires analysis of high-dimensional genomic data and clinical validation of the resulting models, which remain challenging. In this chapter, we describe all the steps of array-based data analysis from data quality control and normalization to higher-level analyses such as clustering, dimensionality reduction, and predictive modeling, with special emphasis on the pitfalls and dangers of such analyses. We then tackle the problems related to clinical validation of array-based biomarkers and predictive assays, which include reproducibility and portability of the initial discovery and its translation into clinics. As array-based data, and those generated by the next-generation sequencing technologies, become less expensive to produce and more widely available, the growing number of patients for whom we have genomic data will open new opportunities for development of robust and reliable genomic biomarkers—provided we apply the lessons we have learned from this last decade of array-based studies.

Keywords

Root Mean Square Error Feature Selection Receive Operating Characteristic Curve Performance Criterion Independent Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Biostatistics and Computational BiologyDana-Farber Cancer InstituteBostonUSA
  2. 2.Biostatistics, Harvard School of Public HealthBostonUSA

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