  Background: The availability of high throughput methods for measurement of
mRNA concentrations makes the reliability of conclusions drawn from the data
and global quality control of samples and hybridization important issues. We
address these issues by an information theoretic approach, applied to
discretized expression values in replicated gene expression data.
  Results: Our approach yields a quantitative measure of two important
parameter classes: First, the probability $P(\sigma | S)$ that a gene is in the
biological state $\sigma$ in a certain variety, given its observed expression
$S$ in the samples of that variety. Second, sample specific error probabilities
which serve as consistency indicators of the measured samples of each variety.
The method and its limitations are tested on gene expression data for
developing murine B-cells and a $t$-test is used as reference. On a set of
known genes it performs better than the $t$-test despite the crude
discretization into only two expression levels. The consistency indicators,
i.e. the error probabilities, correlate well with variations in the biological
material and thus prove efficient.
  Conclusions: The proposed method is effective in determining differential
gene expression and sample reliability in replicated microarray data. Already
at two discrete expression levels in each sample, it gives a good explanation
of the data and is comparable to standard techniques.
