--- name: zubyul-gene-networks-head description: Gene correlation network analysis bridging WGCNA, pgmpy Bayesian networks, and monad-bayes posterior inference. Connects zubyul's genomics background to the plurigrid interactome via HyperNetX hypergraph topology. --- # zubyul-gene-networks Skill > *Scale-free gene modules as Bayesian hypergraphs* ## Origin: zubyul/WGCNA + zubyul/jonikas_lab_data_analysis_misc Yuliya Zubak (zubyul) built WGCNA pipelines for weighted gene correlation network analysis and processed large genetic sequence data in the Jonikas lab. ## What's Possible ### 1. WGCNA -> pgmpy Bridge ======= description: Gene correlation network analysis bridging WGCNA, pgmpy Bayesian networks, and monad-bayes posterior inference. Load when building gene co-expression modules, learning regulatory network structure, or using HyperNetX hypergraph topology on genomics data. --- # zubyul-gene-networks ## Origin Yuliya Zubak (zubyul) built WGCNA pipelines for weighted gene correlation network analysis and processed large genetic sequence data in the Jonikas lab. Repos: `zubyul/WGCNA`, `zubyul/jonikas_lab_data_analysis_misc`. ## WGCNA -> pgmpy Bridge >>>>>>> origin/main - Module eigengenes from WGCNA become nodes in a pgmpy Bayesian Network - Structure learning (Hill Climb / MMHC) discovers regulatory edges - monad-bayes: `TracedT (WeightedT SamplerIO)` for posterior over network topologies - Each MCMC step proposes an edge addition/removal, weighted by BIC score <<<<<<< HEAD ### 2. HyperNetX Hypergraph Topology ======= ## HyperNetX Hypergraph Topology >>>>>>> origin/main - Gene modules are hyperedges (one module = many genes) - Modularity clustering on the hypergraph partitions functional groups - Homology mod 2 detects topological holes in the regulatory network - Contagion dynamics model gene expression cascades <<<<<<< HEAD ### 3. monad-bayes Integration ```haskell -- Posterior over WGCNA module assignments moduleAssignment :: MonadMeasure m => Int -> m (Vector ModuleID) moduleAssignment nGenes = do -- Prior: Dirichlet-Multinomial over module labels weights <- dirichlet (replicate nModules 1.0) assignments <- replicateM nGenes (categorical weights) -- Likelihood: within-module correlation > between-module ======= ## Bayesian Module Assignment ```haskell moduleAssignment :: MonadMeasure m => Int -> m (Vector ModuleID) moduleAssignment nGenes = do weights <- dirichlet (replicate nModules 1.0) assignments <- replicateM nGenes (categorical weights) >>>>>>> origin/main forM_ (pairs assignments) $ \(i, j) -> if sameModule i j then factor (Exp (log (correlation i j))) else factor (Exp (log (1 - correlation i j))) return assignments ``` <<<<<<< HEAD ### 4. GF(3) Trit Classification | Component | Trit | Role | |-----------|------|------| | WGCNA eigengenes | +1 | Generation (data -> modules) | | pgmpy BN learning | 0 | Coordination (structure) | | monad-bayes posterior | -1 | Validation (model selection) | Conservation: +1 + 0 + (-1) = 0 ## Edges in Interactome TUI - -> monad-bayes (w=0.70, Bayesian network priors) - -> pgmpy (w=0.80, BN structure learning) - -> HyperNetX (w=0.85, hypergraph modules) - -> zubyul/Nikolova_lab (w=0.90, gene-brain bridge) ## Trit: 0 (ERGODIC - bridges genomics to interactome) ======= ## Concrete Affordances ### Clone Upstream Repositories ```bash # WGCNA analysis pipeline git clone https://github.com/zubyul/WGCNA.git /Users/alice/v/zubyul-wgcna # Jonikas lab data processing git clone https://github.com/zubyul/jonikas_lab_data_analysis_misc.git /Users/alice/v/zubyul-jonikas ``` ### Bayesian Network Structure Learning from WGCNA Eigengenes (pgmpy) Learn the regulatory DAG over module eigengenes discovered by WGCNA: ```python # pip install pgmpy pandas numpy import pandas as pd import numpy as np from pgmpy.estimators import HillClimbSearch, BicScore from pgmpy.models import BayesianNetwork from pgmpy.estimators import MaximumLikelihoodEstimator np.random.seed(42) # Simulated WGCNA module eigengenes (replace with real eigengene matrix from R) # Columns = module colors (WGCNA convention), rows = samples n_samples = 200 eigengenes = pd.DataFrame({ 'ME_blue': np.random.randn(n_samples), 'ME_brown': np.random.randn(n_samples), 'ME_turquoise': np.random.randn(n_samples), 'ME_green': np.random.randn(n_samples), 'ME_yellow': np.random.randn(n_samples), }) # Inject causal structure: blue -> brown, turquoise -> green, blue -> yellow eigengenes['ME_brown'] += 0.6 * eigengenes['ME_blue'] eigengenes['ME_green'] += 0.5 * eigengenes['ME_turquoise'] eigengenes['ME_yellow'] += 0.4 * eigengenes['ME_blue'] + 0.3 * eigengenes['ME_turquoise'] # Discretize for BN (or use linear Gaussian BN) discretized = eigengenes.apply(lambda col: pd.cut(col, bins=3, labels=['low','mid','high'])) # Hill Climb structure learning with BIC scoring hc = HillClimbSearch(discretized) best_model = hc.estimate(scoring_method=BicScore(discretized), max_indegree=3) print("Learned DAG edges (regulatory relationships):") for edge in best_model.edges(): print(f" {edge[0]} -> {edge[1]}") # Fit parameters bn = BayesianNetwork(best_model.edges()) bn.fit(discretized, estimator=MaximumLikelihoodEstimator) print(f"\nNodes: {bn.nodes()}") print(f"Edges: {bn.edges()}") ``` ### Gene Module Hypergraph with HyperNetX Construct a hypergraph where each WGCNA module is a hyperedge containing its member genes: ```python # pip install hypernetx matplotlib import hypernetx as hnx import matplotlib.pyplot as plt # Gene-to-module assignments from WGCNA (replace with real output) # Each module (hyperedge) contains multiple genes (nodes) module_membership = { 'blue': ['BDNF', 'SLC6A4', 'HTR2A', 'FKBP5', 'NR3C1'], 'brown': ['COMT', 'MAOA', 'DRD2', 'DRD4', 'SLC6A3'], 'turquoise': ['DISC1', 'NRG1', 'DTNBP1', 'CACNA1C', 'ANK3', 'TCF4'], 'green': ['NTRK2', 'CREB1', 'ARC', 'HOMER1'], 'yellow': ['SLC6A4', 'TPH2', 'MAOA', 'HTR1A'], # note: SLC6A4, MAOA overlap } H = hnx.Hypergraph(module_membership) # Basic topology print(f"Nodes (genes): {H.number_of_nodes()}") print(f"Hyperedges (modules): {H.number_of_edges()}") # Genes shared across modules (hub genes / overlapping membership) for node in H.nodes(): memberships = H.nodes.memberships[node] if len(memberships) > 1: print(f" Hub gene {node} in modules: {memberships}") # Compute s-adjacency: two modules are s-adjacent if they share >= s genes for s in [1, 2]: adj = H.adjacency_matrix(s=s) print(f"\n{s}-adjacency matrix (modules sharing >= {s} genes):") print(adj.todense()) # Visualize hnx.drawing.draw(H, with_node_labels=True, with_edge_labels=True) plt.title("WGCNA Gene Module Hypergraph") plt.savefig("/tmp/gene_module_hypergraph.png", dpi=150, bbox_inches='tight') print("Hypergraph saved to /tmp/gene_module_hypergraph.png") ``` ### Load WGCNA Eigengenes from R Output Bridge R WGCNA output into Python: ```python # After running WGCNA in R, export eigengenes: # write.csv(MEs, "module_eigengenes.csv", row.names=TRUE) import pandas as pd eigengenes = pd.read_csv("/Users/alice/v/zubyul-wgcna/output/module_eigengenes.csv", index_col=0) print(f"Loaded {eigengenes.shape[1]} module eigengenes for {eigengenes.shape[0]} samples") print(eigengenes.head()) ``` ## Edges - -> monad-bayes (Bayesian network priors) - -> pgmpy (BN structure learning) - -> HyperNetX (hypergraph modules) - -> zubyul/Nikolova_lab (gene-brain bridge) >>>>>>> origin/main --- Source attribution: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/skills/zubyul-gene-networks/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; 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