Comparison of CMoA, GES and GI₅₀ profile similarities.

<p>(A) Venn diagram of the top 206 similar compound pairs (top 2.5%) using DTPA, DTGE and GI₅₀ sensitivity profiles. (B) top: Enrichment of the top 206 pairs based on DTPA similarity in the vector of 8256 (129*128/2) compound pairs ranked by DTGE similarity, and vice versa; middle: Enrichment of the top 206 pairs based on DTPA similarity in the vector of 8256 (129*128/2) compound pairs ranked by GI₅₀ correlation, and vice versa; bottom: Enrichment plot of the top 206 pairs using DTGE similarity in the vector of 8256 (129*128/2) compound pairs ranked by GI₅₀ correlation, and vice versa.</p

OncoLead: Network-based protein activity inference.

<p>(A) Drug perturbation induced genome-wide transcriptional changes are interpreted, based on multiple networks including ARACNE network, CHEA network, STRING network, and Gene knock-down (KD) network, with the VIPER algorithm, to infer changes in the activities of the regulatory proteins. The resulting four different protein activity matrixes were integrated into a single final protein activity matrix. In this way, VIPER analysis transforms drug-perturbation gene expression signatures into unbiased genome-wide regulator protein activity representations of CMoA. The left part is a simple illustration of how VIPER algorithm works based on the ARACNE network. First, ARACNE reverse engineers context-specific regulatory networks by leveraging a large collection of gene expression profiles (N > 100) from the same cellular context. Then, regulator’s activity is inferred by computing the enrichment of the genes in its regulon (from ARACNE) in every drug treatment signature sorted from the most over-expressed (colored in orange) to the most under-expressed (colored in green) genes. When there is positive or negative enrichment, the regulator is up-regulated or down-regulated (colored in red/blue). Regulator’s activity is represented by the normalized enrichment score. (B) IRS score decreases when progressively degrading the networks for MCF7 drug signatures. (C) Relative representation of how accurate each interactome is as a model for the transcriptional regulation in each of the three cell lines MCF7, PC3, and HL60 in CMAP database. Shown is the IRS in relative units for TRs inferred by OncoLead on each interactome (x-axis) / GES combination (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005599#sec013" target="_blank">Methods</a> for details). Percent IRS scores were obtained by dividing each specific IRS score by the largest score obtained across the three interactomes used in the analysis. (D) Distribution of the significant TRs inferred by OncoLead when adding increasing ratios of random noises to the Irinotecan signature in MCF7 cell line.</p