Discretization provides a conceptually simple tool to build expression networks

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes "microarray" data using known relationships which are successfully identified by our method.We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic--identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs

Androgen-responsive non-coding small RNAs extend the potential of HCG stimulation to act as a bioassay of androgen sufficiency

Background: It is unclear whether a short-term change in circulating androgens is associated with changes in the transcriptome of the peripheral blood mononuclear cells (PBMC). Aims & Methods: To explore the effect of hCG-stimulation on the PBMC-transcriptome, 12 boys with a median age (range) of 0.7yrs (0.3, 11.2) who received intramuscular hCG 1500u on 3 consecutive days as part of their investigations underwent transcriptomic array analysis on RNA extracted from peripheral blood mononuclear cells before and after hCG stimulation. Results: Median pre and post hCG testosterone for the overall group was 0.7nmol/l (<0.5,6) and 7.9nmol/l (<0.5, 31.5), respectively. Of the 12 boys, 3 (25%) did not respond to hCG stimulation with a pre and post median serum testosterone of <0.5nmol/l and <0.5nmol/l, respectively. When corrected for gene expression changes in the non-responders to exclude hCG effects, all 9 of the hCG responders consistently demonstrated a 20% or greater increase in the expression of piR-37153 and piR-39248, non-coding PIWI-interacting RNAs (piRNAs). In addition, of the 9 responders, 8, 6 and 4 demonstrated a 30%, 40% and 50% rise, respectively in a total of 2 further piRNAs. In addition, 3 of the responders showed a 50% or greater rise in the expression of another small RNA, SNORD5. On comparing fold change in serum testosterone with fold change in the above transcripts, a positive correlation was detected for SNORD5 (p=0.01). Conclusions: The identification of a dynamic and androgen-responsive PBMC-transcriptome extends the potential value of the hCG test for assessment of androgen sufficiency