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A generic concept for large-scale microarray analysis dedicated to medical diagnostics
1Department of Medical Informatics and Biomathematics, University of Münster, Germany. Martin.Dugas@ukmuenster.de
Background:
The development of diagnostic procedures based on microarray analysis confronts the bioinformatician and the biomedical researcher with a variety of challenges. Microarrays generate a huge amount of data. There are many, not yet clearly defined, data processing steps and many clinical response variables which may not match gene expression patterns.
Objectives:
To design a generic concept for largescale microarray experiments dedicated to medical diagnostics; to create a system capable of handling several 1000 microarrays per analysis and more than 100 clinical response variables; to design a standardized workflow for quality control, data calibration, identification of differentially expressed genes and estimation of classification accuracy; and to provide a user-friendly interface for clinical researchers with respect to biomedical interpretation.
Methods:
We designed a database structure suitable for the storage of microarray data and analysis results. We applied statistical procedures to identify differential genes and developed a technique to estimate classification accuracy of gene patterns with confidence intervals.
Results:
We implemented a Gene Analysis Management System (GAMS) based on this concept, using MySQL for data storage, R/Bioconductor for analysis and PHP for a web-based front-end for the exploration of microarray data and analysis results. This system was utilized with large data sets from several medical disciplines, mainly from oncology (approximately 2000 microarrays).
Conclusions:
A systematic approach is necessary for the analysis of microarray experiments in a medical diagnostics setting to get comprehensible results. Due to the complexity of the analysis, data processing (by bioinformaticians) and interactive exploration of results (by biomedical experts) should be separated.
