Abstract
BackgroundGene 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.ResultsIn 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.ConclusionsWe 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/
Highlights
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
Global background noise The global background is described by a randomly Gaussian distributed noise that is equal for the whole filter
The model was chosen for local background, because the intensity level of a given pixel depends on its neighbors
Summary
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. Global background noise The global background is described by a randomly Gaussian distributed noise that is equal for the whole filter. The model was chosen for local background, because the intensity level of a given pixel depends on its neighbors. This results in images that look quite the same as the background of experimental images. Global background noise From the border (non-spotted) area of an experimental filter image with a 16 bit depth the noise level was found to be about 16000 with a standard deviation of about 4000; the distribution is similar to Gaussian (data not shown). The correlation between input and output intensities were always higher than 0.99; so a realistic global background noise as give by the experimental reference does not influence the quantification of the programs
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