A general question in the analysis of biological experiments is how to maximize statistical
information present in the data while at the same time keeping bias at a minimal level.
This can be reformulated as the question whether to perform differential analysis or only explorative
screens. In this contribution we discuss this old paradigm in the context of a differential
microarray experiment. The transcription factor Lmx1b is knocked out in a mouse model in order
to gain further insight into gene regulation taking place in Nail-patella syndrome, a disease caused
by mutations of this gene. We review several statistical methods and contrast them with supervised
learning on the two differential modes and unsupervised, explorative analysis. Moreover we
propose a novel method for analyzing single clusters by projecting them back on specific experiments.
Our reference is the identification of three well-known targets. We find that by integrating
all results we are able to confirm these target genes. Furthermore, hypotheses on further potential
target genes are formulated.
Keywords: microarray analysis, Nail-patella syndrome, Lmx1b, linear mixing models, recursive feature extraction
