Methodology article

Systematic interpretation of microarray data using experiment annotations

Kurt Fellenberg¹ , Christian H Busold¹ , Olaf Witt^2,3 , Andrea Bauer¹ , Boris Beckmann¹ , Nicole C Hauser⁴ , Marcus Frohme¹ , Stefan Winter⁵ , Jürgen Dippon⁵ and Jörg D Hoheisel¹

¹  Department of Functional Genome Analysis, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany

²  Department of Pediatrics I, University of Göttingen, Robert-Koch-Str. 40, D-37075 Göttingen, Germany

³  Experimental Pediatric Oncology, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany

⁴  Fraunhofer IGB, Nobelstr. 12, 70569 Stuttgart, Germany

⁵  Institute for Stochastics and Applications, University of Stuttgart, Pfaffenwaldring 57, D-70569 Stuttgart, Germany

author email corresponding author email

BMC Genomics 2006, 7:319doi:10.1186/1471-2164-7-319

Published:	20 December 2006

Abstract

Background

Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format.

Results

We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design.

Conclusion

Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.