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The regulatory program uses the expression levels of the set of regulators to predict the condition-dependent mean expression of the co-expressed genes.
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In addition to the identification of significant expression patterns in tumors, we used the expression levels in healthy tissues to detect co-expressed genes under normal conditions.
Since at this early stage, sex determination is starting (Sry is expressed at 10.5 dpc, the possible skews in representation were followed using the expression levels of Y-specific markers such as UtY, which were not different between the groups.
We propose to utilize the analysis of variance for identification of stress-related gene modules by using the expression levels of the Eigen gene as the dependent variable and the treatment conditions of the sample as the independent variable.
The EES was computed using the expression levels of five single knockout mutants, and used to predict their expression levels in the double mutant.
This way we can simultaneously evaluate and compare mRNA and miRNA candidate regulators, using the expression levels of each class of regulators.
When deterministic analysis in used, the expression levels of both genes reach some steady state value and do not exhibit any further change.
Using the expression levels, we build a statistical model of expression for each probe set, based on an assumed bimodal distribution, that accounts for the two states of an expressed gene: Up and Down.
The data was normalized using the expression levels of L32.
The AUC values were then estimated using the expression levels of the created metagenes.
The data were then normalized using the expression levels of β-actin mRNA.
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