NetCoS: Network-Enhanced Connectivity Score

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

Flowchart of the Network-Enhanced Connectivity Score Algorithm

(a) Input and Data Pre-processing:

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.

(b) Linear Mixed Model (LMM):

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.

(c) Meta-Analysis (MA) of Drug 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.

(d) Signal Propagation via MITHrIL:

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.

(e) Connectivity Score Calculation:

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.

References

[1] M. Prudencio, J. Humphrey, S. Pickles, A.-L. Brown, S. E. Hill, J. M. Kachergus, J. Shi, M. G. Heckman, M. R. Spiegel, C. Cook et al., "Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia," The Journal of Clinical Investigation, vol. 130, 2020.

[2] L. C. for Transcriptomics (Broad Institute), L1000 Dataset -small molecule perturbagens- LINCS Trans-Center Project, 2014.

[3] D. Bates, M. Mächler, B. Bolker, and S. Walker, "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, vol. 67, pp. 1–48, 2015.

[4] K. K. M. Koudijs, S. Böhringer, and H.-J. Guchelaar, "Validation of transcriptome signature reversion for drug repurposing in oncology," Briefings in Bioinformatics, vol. 24, p. bbac490, 2023.

[5] M. Harrer, P. Cuijpers, T. Furukawa, and D. Ebert, Doing Meta-Analysis with R: A Hands-On Guide, Chapman and Hall/CRC, 2021.

[6] S. Alaimo, R. Giugno, M. Acunzo, D. Veneziano, A. Ferro, and A. Pulvirenti, "Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification," Oncotarget, vol. 7, p. 54572, 2016.

[7] B. Chen, L. Ma, H. Paik, M. Sirota, W. Wei, M.-S. Chua, S. So, and A. J. Butte, "Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets," Nature Communications, vol. 8, p. 16022, 2017.

[8] A.-L. Brown, O. G. Wilkins, M. J. Keuss, S. E. Hill, M. Zanovello, W. C. Lee, A. Bampton, F. C. Y. Lee, L. Masino, Y. A. Qi et al., "TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A," Nature, vol. 603, pp. 131–137, 2022.

Tech stack

• Python — pipeline orchestration, connectivity score, ChEMBL validation, plotting. Dependencies in requirements.txt (joblib, matplotlib, numpy, pandas, pyreadr, requests, scipy); package metadata in setup.py (drug_repurposing, v0.1.0).
• R — LMM fitting, LINCS data handling, signature meta-analysis. Under tsr/ (legacy workflows) and new_tsr/R/ (refactored OOP version).
• Julia — LMM optimization (tsr/julia/, modules/drug_signature/julia/).
• MITHrIL — external Java tool, run as a subprocess (step2_disease_run_MITHrIL.py, step6_drug_run_MITHrIL.py).

Running the pipeline

• Full pipeline — orchestrated by the numbered step1..7 scripts in src/, typically invoked from cs_batch.py.
• ChEMBL validation — src/validations/chembl/, starting at Preprocess_binChen2017_TCGA_LINCS_data.py.
• R workflows — enter the relevant tsr/<disease>/ folder; scripts assume that as the working directory.

Experiments are switched by which conf.py is active in src/ (there is no CLI flag) — see the config system in the pipeline reference below. Heavy R/Julia jobs can take hours.

Documentation

• docs/pipeline.md — precise stage-by-stage pipeline & architecture reference: the two pipelines (TSR/LMM and BinChen2017/ChEMBL), the drug-collapse inversion, the sign convention, the CS run registry, the config system, and the repository layout.