In addition to gene matching, it is also necessary to summarize transcript-level expression which is often measured using multiple probes.
As expression for any given gene is measured by multiple probes (Additional file 1), we summarized the information using R function collapseRows [ 48, 49].
As the expression profile datasets were generated on different platforms, and as many of the genes represented were measured on multiple probes in any one dataset, a method to select the optimal probe to represent each gene in an unbiased fashion was used when joining multiple datasets.
Because genome tiling arrays provide end-to-end coverage of the target genome and measure transcriptional activity from multiple probes, they are capable of detecting the transcriptome in a comprehensive and relatively unbiased way.
Since bacteriochlorin-based fluorophores have quite sharp emission spectra, it should be possible to deliver a cocktail of 3 or more distinctly targeted probe conjugates to measure multiple receptors simultaneously all with a single excitation.
For most array platforms, multiple probes were available to measure expression for a given human gene.
A further extension could combine multiple windows per transcript into a single measure in a way similar to combining multiple probes in the RMA microarray algorithm [ 9] but this suggestion requires further exploration and analysis.
For genes with multiple probes, each probe was used as an independent measure of the expression level.
In some cells two or more FISH spots can be seen for each of the probes, and multiple probes on the same chromosome allow for an indirect measure of axial compaction.
Since many of the genes in our panel are represented by multiple probes, the average expression of each probe was measured across all samples and the probe with the highest overall signal for each gene in our panel was selected as the equivalent diagnostic feature.
