A signalling entropy derived prognostic score outperforms microarray based prognostic indicators in lung adenocarcinoma

However, a microarray based approximation of the Kratz et al.

We note that not all proteins in the PIN have a corresponding probe in the microarray or sequence in the RNA-seq data, consequentially the PIN we consider is the maximally connected component of the original PIN after the removal of missing proteins.

It is of particular note that signalling entropy is significantly prognostic if computed from either microarray or RNA-seq data sets, this result attests to the biological relevance of our measure which is not masked by experimental technique.

In order to assess the prognostic significance of signalling entropy in breast cancer, we first computed its value for each microarray sample of the Molecular Taxonomy of Breast Cancer International Research Consortium dataset (METABRIC) [30], a total of 1980 samples divided into a discovery and validation sets of equal proportion.

These results are in contrast to the performance of MammaPrint, a microarray based breast cancer prognostic signature currently being assessed in the MINDACT trial [41].

Due to differences in the normalisation between RT-PCR and microarrays , a direct comparison between our measure and OncotypeDX is difficult to perform.

The plots display the concordance indices for signalling entropy and a microarray based approximation of OncotypeDX in each data set alongside 95% confidence intervals.

A) The plots display the concordance indices for signalling entropy and a microarray based approximation of the Kratz et al.

Although integrative analysis of multiple microarray gene expression (MAGE) datasets allows to distill the maximum relevant biological information from genomic datasets, the unwanted variation, so-called batch-effects arising from data merged from difference sources has been a major challenge to impede such effort [61].

Indeed, after aggregating all high-throughput transcriptional data that is currently available for E. coli, the most well-studied model microbe, we are still limited to a few thousands microarray or RNA-Seq experiments that cover more than 30 strains, a dozen different media and a multitude of other genetic (knockout, over-expressions, re-wirings), or environmental (carbon limitation, chemicals, abiotic factors) perturbations.

microarrays vs. RNA-Seq), in different labs and under different environmental conditions, appropriate normalization schemes are both of paramount importance and with an added complexity.

We integrated the RNA-Seq dataset (64 samples) to the E. coli Microarray Compendium (EcoMAC) that consists of 2198 microarrays of 4189 genes for which raw files were downloaded and normalized by RMA (robust multichip average) method [29].

[8] combined two microarray data sets to create a single transcriptional signature of lung adenocarcinoma and screened it against CMap.

Since each of the individual disease signatures was constructed using dozens or even hundreds of microarrays , there is fairly strong evidence for every gene in each signature.

In contrast, the drug response data in CMap is noisy: the 1,309 drugs have each been tested only a median of 4 times (4 treatment microarrays ).

Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophi/a melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms.

Microarray metadataset

Analysis of microarray metadataset.