In co-expression analysis one searches for genes that show a similar expression profile across numerous different microarray experiments, representing different environmental and stress conditions as well as developmental stages and tissue types.
In case of non-availability of the microarray data for the identical experimental conditions being analysed, microarray experiments representing broadly similar conditions may be used for the analysis.
The information generated by the protein kinase categorization using a phylogenetic approach, associated to the expression data obtained from microarray experiments, represents a useful tool in guiding the future characterization of these proteins.
We utilized available transcriptomic data of transcriptional responses extracted from 271 microarray experiments representing nine major immune-related studies (Additional file 1: Table S1 and Additional file 2: Supporting methods) [ 26– 34].
For transcriptional profiling, we followed a cyclic, two-color design and performed 28 oligonucleotide microarray hybridization experiments representing technical and biological replicates of the four cell states under investigation: WT grown in vitro (WT-vitro), WT grown in vivo (WT-vivo), AP grown in vitro (AP-vitro), and AP grown in vivo (AP-vivo), Fig. 1.
The data from a series of m microarray experiments can be represented as an m × n gene expression matrix (see Table 1), where each row represents a sample described by the expression of n genes from one experiment.
The gene expression measurements of the microarray experiment are represented by a matrix X = [x 1,...,x n ] of dimension n × p, with arrays as columns and genes as rows.
Each data point produced by a DNA microarray hybridization experiment represents the log of the ratio of expression levels of a particular gene under two different experimental conditions.
In this approach, the design of any microarray experiment can be represented in terms of a linear model for each gene.
We analyzed four cases of microarray experiments likely to represent the diversity of data presented in a typical microarray-intensive laboratory (Table 1).
