Figure 1

SSEM-Lasso network-inference methodology for prediction of gene targets. (A) In the training phase, transcript signals derived from a training compendium of Affymetrix yeast expression data estimated a gene interaction network using sparse simultaneous equation models and Lasso regression (SSEM-Lasso). The gene interaction network accounted for every gene’s effect on another gene within the compendium and was used to infer subsequent experimental perturbations of interest. (B) In the testing phase, experimental expression data was processed with the gene interaction network, and mRNA transcript signals were adjusted based on all inferred gene regulatory effects in the network. An outlier analysis yielded residual values for every gene in the compendium. Residuals were ranked by their absolute values, and genes with lower ranks were considered more accurate predictions of directly targeted genes of the experimental perturbation. (C) SSEM-Lasso “resolves” experimentally perturbed genes out of the background gene-gene interaction “noise” in the network. This results in a more stringent gene-target filter in comparison to standard z-score computation. The data shown is from a top2Δ/TOP2 heterozygous yeast deletion microarray experiment conducted in-house. The gene target, TOP2, is significantly perturbed when evaluated with SSEM-Lasso compared to the RNA z-score prediction.