PathTurbEr

Pathway Perturbation Driver identification

Acknowledgement

If you find PathTeurbEr as useful for your research, please cite our work by including the following citation:

• Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods. Briefings in Bioinformatics Link to the paper
• Citation:

@article{10.1093/bib/bbab137,
    author = {Azad, A K M and Alyami, Salem A},
    title = "{Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods}",
    journal = {Briefings in Bioinformatics},
    year = {2021},
    month = {04},
    issn = {1477-4054},
    doi = {10.1093/bib/bbab137},
    url = {https://doi.org/10.1093/bib/bbab137},
    note = {bbab137},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab137/37074977/bbab137.pdf},
}

Installation

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• Visual Studio
• JAGS: Can be downloaded from here. After installing JAGS, the binary file path (e.g. C:\Program Files\JAGS\JAGS-4.3.0\x64\bin) should be used within the code to run. Moreover, the SharpJags.dll (download from here) file should be added as a reference into the visual studio project.

Parameter list

• Overall parameters: bnmcmc_method (i.e. NS, HAR, MH, or, All), and more
• BNMCMC parameters: maxParent, maxChild, nSamplingIter, nBurnIn
• JAGS parameters: gamma_prior_a, gamma_prior_b, nSamplingIter, nBurnIn

Running a demo

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Dataset

• Case data: [GSE38376] Laptinib-resistant gene expression of SKBR3 Breast cancer cell-line formatted as gene-level data, which can be found here.
• Control data: [GSE38376] Laptinib-sensitive gene expression of SKBR3 Breast cancer cell-line formatted as gene-level data, which can be found here.
• Pathway data: Signalling pathways collected from KEGG database, which can be found here

DE analysis

Run DE_GSE38376_GEO2R_code.R in R to conduct differential expression analysis. The output of this step should later be filtered based on desired threshold values of parameter, e.g. logFC, p-value, adj.pval, etc. Note, this file independently collect the same gene expression data as above.

Pathway Enrichment of DEGs

Run Pathway_geneSetEnrichmentAnalysis.R for finding enrichment of the DEGs, found from previous step.

MCMC sampling (a Visual Studio project)

Optimal BN structure learning for a particular STP perturbation

Run BaseModule.cs for generating optimal STP perturbation BN from Neighbourhood sampler, Hit-and-Run sampler, and Metropolis-Hasting sampler. The files will be saved within the bin\Debug\BNMCMC output\ directory of the project under the default settings.

Statistical modeling of perturbation driver characterization

Run bugsSampling.cs for generating alpha values each of the inferred BN structures. The files will be saved within the bin\Debug\JAGS_output\ directory of the project under the default settings.