Network-based Bayesian Inference of Drivers NetBID
Create an appropriate format for the dataset
- 1.MGH_data_Preprocessing.R
Filtering and data construction for SJARACNe algorithm.
- 2.Network_Reconstruction.R
Perform SJARACNe algorithm, extracting non-linear correlations between proteins
- 3.SJARACNe.sh
Perform Bayesian Inference algorithm for feature selection
- 4.Driver_Inference.R
Load dataset into Cytoscape for better visualization of results for annotation and Enrichment analysis.
- 5.Cytoscape_load.R
Advanced analysis for plotting and interpretation
- 6.Advanced_Analysis.R
For the biological informed neural network (b-iNN) is followed the PasNet architecture. The changes are committed to the b-iNN are described in the following steps:
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Pathway Layer At this step it is created the REACTOM_pathway_mask.csv and GO_pathway_mask.csv documents for Reactom and GO pathway libraries, respectively. These documents are used for the two different b-iNN that are created for this study.
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Explainable AI feature Next, the Train.py file of PasNet modified (the modified python script is in Neural network folder)
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Receiver Operating Characteristic plot The EvalFunc.py file of PasNet modified (the modified python script is in Neural network folder)
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Train and Test data For the training and test of the model has used the MGH dataset. The MGH dataset is normalized with mean = 0 and standard deviation = 1. The produced datasets are the following:
- Train: std_train_0_0.csv
- Test: std_test_0_0.csv
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Execution of b-iNN For the execution of the experiment first it is executed the Run_EmpiricalSearch.py. After optimal L2 and LR attributes are detected, it is executed the Run.py.
The .h5ad scRNA-seq data for the analysis are accessible in the following link: https://www.covid19cellatlas.org/index.patient.html (“00803_MGH_Broad00803_MGH_Broad”). The scRNA-seq analysis can then be recreated using the “scDIOR Python MGH.ipynB” script in Python to make an adata object and then the “MGHscRNAseq.R” script in R to convert the adata object into Seurat object and then perform downstream analysis.
D-SJARACNe was assembled using the Day 7 SJARACNe network (“Cytoscape_network_D7.cys”) with data from Zhou et al. (https://doi.org/10.1038/s41587-022-01474-0). The drug-drug interaction network was assembled by using the drugs from D-SJARACNe as input in the STITCH database. Visualizations were performed in the Gephi v0.9 platform.