Systematic interpretation of microarray data using experiment annotations.
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.


CONCLUSIONS
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.
Fellenberg
Kurt
K
Department of Functional Genome Analysis, German Cancer Research Center, PO 101949, D-69009 Heidelberg, Germany. k.fellenberg@dkfz.de
Busold
Christian H
CH
Witt
Olaf
O
Bauer
Andrea
A
Beckmann
Boris
B
Hauser
Nicole C
NC
Frohme
Marcus
M
Winter
Stefan
S
Dippon
Jürgen
J
Hoheisel
Jörg D
JD
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*; D015203:Reproducibility of Results