Since this is not the general case in gene expression distributions, values of linear correlations are not enough to determine statistical dependency (Hernández-Lemus et al. 2009).
Distinct features of the gene expression distributions prompted us on a systematic search for regulatory interactions, revealing a network of interlocked positive and negative feedback loops.
Panel (a), (b) and (c), as before, show the expression distributions of the probe set.
Genes were plotted in box-plots in order to examine the expression distributions in further detail.
Since the variances of target and non-target expression distributions are allowed to be unequal, unintuitive interpretation of expression data can occur (Figure S1).
Consequently, the selection of cells used for PALM imaging (those exhibiting midcell FtsZ-mEos2 localization) may have different expression distributions from the whole cell population.
This figure shows very good correlation among expression distributions verifying that the correlation at the probe level is indeed preserved after expression summary calculation.
Under the assumption of equal expression distributions and linear experimental artifacts, this linear scaling should suffice to make all distributions equal irrespective of the nature of these distributions.
In order to actually test the hypothesis that all gene expression distributions are identical, we developed a linear scaling method to inspect for similarity among probe intensity distributions.
For the analysis of gene expression distributions, MAS 5.0 was used because the algorithm does not alter the gene expression distribution, whereas, RMA utilizes quantile normalization of probes prior to summarization and, therefore, has the potential to remove group level differences in gene expression distributions.
Visual inspection of the QLT distributions can be used to illustrate the degree of linear and nonlinear experimental variations in the data set given the assumption of equal gene expression distributions.
