The network-enhanced connectivity score compares the gene signature of a specific biological process—such as transcriptomics data from patients with a particular disease—to the gene signatures of reference drugs. It leverages interactomics by projecting this data into a network space encompassing protein-protein, protein-metabolite, and protein-mRNA interactions, and propagates signals across the network. The algorithm then calculates a similarity score between each drug and the process of interest, ultimately ranking the drugs based on their similarity. A visual representation of this pipeline is shown in Figure 1.
Figure 1
The algorithm requires two inputs:
- Disease Signature – Currently implemented for transcriptomic data [1], this includes paired transcriptomic data from healthy and diseased tissues across patients.
- Drug Signature – A collection of paired test-control microarray experiments for various metabolites [2]. Includes cell lines with pole-epsilon mutation.
Raw data undergoes preprocessing (PP), including filtering and removing duplicate gene IDs, ensuring that only unique gene identifiers are retained for further analysis.
An LMM is applied separately to drug and disease signatures [3][4]. This model extends traditional linear models by accounting for experimental variability through random effects, specifically RNA plate and cell type. The output is a log fold change representing the difference between the condition and the control data.
A meta-analysis [5] is performed to account for multiple perturbation time points (6 and 24 hours) and produce a consolidated measurement for each drug.
The resulting fold change data from the LMM is processed using the MITHrIL pipeline [6], which propagates the signal across an extensive interaction network. This step generates a perturbation score for each network node.
Using the perturbation scores, a connectivity score is computed between the disease and each drug based on the Kolmogorov-Smirnov statistic [4][7]. The drugs are then ranked according to these scores. Drugs with the most negative connectivity scores are most likely to counteract the disease effect.
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