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Simulation of DNA array hybridization experiments and evaluation of critical parameters during subsequent image and data analysis

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50637

Wierling,  Christoph K.
Systems Biology (Christoph Wierling), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

Steinfath,  Matthias
Max Planck Society;

Clark,  Matthew
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50409

Lehrach,  Hans
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50202

Herwig,  Ralf
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Wierling, C. K., Steinfath, M., Elge, T., Schulze-Kremer, S., Aanstad, P., Clark, M., et al. (2002). Simulation of DNA array hybridization experiments and evaluation of critical parameters during subsequent image and data analysis. BMC Bioinformatics, 3(1), 29-29.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-8B8B-0
Abstract
Background: Gene expression analyses based on complex hybridization measurements have increased rapidly in recent years and have given rise to a huge amount of bioinformatic tools such as image analyses and cluster analyses. However, the amount of work done to integrate and evaluate these tools and the corresponding experimental procedures is not high. Although complex hybridization experiments are based on a data production pipeline that incorporates a significant amount of error parameters, the evaluation of these parameters has not been studied yet in sufficient detail. Results: In this paper we present simulation studies on several error parameters arising in complex hybridization experiments. A general tool was developed that allows the design of exactly defined hybridization data incorporating, for example, variations of spot shapes, spot positions and local and global background noise. The simulation environment was used to judge the influence of these parameters on subsequent data analysis, for example image analysis and the detection of differentially expressed genes. As a guide for simulating expression data real experimental data were used and model parameters were adapted to these data. Our results show how measurement error can be balanced by the analysis tools. Conclusions: We describe an implemented model for the simulation of DNA-array experiments. This tool was used to judge the influence of critical parameters on the subsequent image analysis and differential expression analysis. Furthermore the tool can be used to guide future experiments and to improve performance by better experimental design. Series of simulated images varying specific parameters can be downloaded from our web-site: http://www.molgen.mpg.de/~lh_bioinf/projects/simulation/biotech/