Input: Multiple Omics Datasets
    |
    v
Phase 1: Data Loading & QC
    |-- Load RNA-seq (expression matrix)
    |-- Load proteomics (protein abundance)
    |-- Load methylation (beta values or M-values)
    |-- Load variants (CNV, SNV from VCF)
    |-- Load metabolomics (metabolite abundance)
    |-- Quality control per omics type
    |
    v
Phase 2: Sample Matching
    |-- Match samples across omics by ID
    |-- Identify common samples
    |-- Handle batch effects
    |-- Normalize sample identifiers
    |
    v
Phase 3: Feature Mapping
    |-- Map features to common identifier space (genes, proteins, metabolites)
    |-- Link CpG sites to genes (promoter, gene body)
    |-- Map variants to genes
    |-- Create unified feature matrix
    |
    v
Phase 4: Cross-Omics Correlation
    |-- Gene expression vs protein abundance (translation efficiency)
    |-- Promoter methylation vs expression (epigenetic regulation)
    |-- CNV vs expression (dosage effect)
    |-- eQTL variants vs expression (genetic regulation)
    |-- Metabolite vs enzyme expression (metabolic flux)
    |
    v
Phase 5: Multi-Omics Clustering
    |-- MOFA+ (Multi-Omics Factor Analysis) for latent factors
    |-- NMF (Non-negative Matrix Factorization) for patient subtypes
    |-- Joint clustering across omics
    |-- Identify omics-specific vs shared variation
    |
    v
Phase 6: Pathway-Level Integration
    |-- Aggregate omics to pathway level
    |-- Score pathway dysregulation (combined evidence)
    |-- Use ToolUniverse enrichment tools (Reactome, KEGG, GO)
    |-- Identify driver pathways across omics
    |
    v
Phase 7: Biomarker Discovery
    |-- Feature selection across omics
    |-- Multi-omics signatures for classification
    |-- Cross-validation and performance
    |-- Interpretation and biological validation
    |
    v
Phase 8: Generate Integrated Report
    |-- Summary statistics per omics
    |-- Cross-omics correlation results
    |-- Multi-omics clusters and subtypes
    |-- Top dysregulated pathways
    |-- Multi-omics biomarkers
    |-- Biological interpretation

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def match_samples_across_omics(omics_data_dict):
    """
    Match samples across multiple omics datasets.

    Parameters:
    omics_data_dict: {
        'rnaseq': DataFrame (genes x samples),
        'proteomics': DataFrame (proteins x samples),
        'methylation': DataFrame (CpGs x samples),
        'cnv': DataFrame (genes x samples)
    }

    Returns:
    - common_samples: List of sample IDs present in all omics
    - matched_data: Dict of DataFrames with common samples only
    """
    # Extract sample IDs from each omics
    sample_ids = {
        omics_type: set(df.columns)
        for omics_type, df in omics_data_dict.items()
    }

    # Find common samples (intersection)
    common_samples = set.intersection(*sample_ids.values())

    # Subset each omics to common samples
    matched_data = {
        omics_type: df[sorted(common_samples)]
        for omics_type, df in omics_data_dict.items()
    }

    return sorted(common_samples), matched_data

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**CpG to gene mapping**:
- **Promoter methylation**: CpGs within TSS ± 2kb
- **Gene body methylation**: CpGs within gene boundaries
- Average methylation per gene (weighted by probe coverage)

**CNV to gene mapping**:
- Use variant-analysis skill to identify genes in CNV regions
- Calculate copy number per gene (log2 ratio)

def correlate_rna_protein(rnaseq_data, proteomics_data):
    """
    Correlate mRNA and protein levels for each gene.

    Expected: Positive correlation (r ~ 0.4-0.6 typical)
    Discordance indicates post-transcriptional regulation
    """
    # Find common genes
    common_genes = set(rnaseq_data.index) & set(proteomics_data.index)

    correlations = {}
    for gene in common_genes:
        rna = rnaseq_data.loc[gene]
        protein = proteomics_data.loc[gene]

        # Spearman correlation (robust to outliers)
        r, p = spearmanr(rna, protein)
        correlations[gene] = {'r': r, 'p': p}

    # Identify discordant genes (low RNA-protein correlation)
    discordant = {g: v for g, v in correlations.items() if abs(v['r']) < 0.2}

    return correlations, discordant

def correlate_methylation_expression(methylation_data, rnaseq_data):
    """
    Correlate promoter methylation with gene expression.

    Expected: Negative correlation (increased methylation → decreased expression)
    """
    # For each gene with promoter methylation
    results = {}
    for gene in methylation_data.index:
        if gene in rnaseq_data.index:
            meth = methylation_data.loc[gene]  # Average promoter beta
            expr = rnaseq_data.loc[gene]

            r, p = spearmanr(meth, expr)
            results[gene] = {'r': r, 'p': p, 'direction': 'repressive' if r < 0 else 'activating'}

    # Identify genes with strong methylation-expression anticorrelation
    regulated = {g: v for g, v in results.items() if v['r'] < -0.5 and v['p'] < 0.01}

    return results, regulated

def correlate_cnv_expression(cnv_data, rnaseq_data):
    """
    Correlate copy number with gene expression.

    Expected: Positive correlation (gene dosage effect)
    """
    results = {}
    for gene in cnv_data.index:
        if gene in rnaseq_data.index:
            cnv = cnv_data.loc[gene]  # log2 ratio
            expr = rnaseq_data.loc[gene]

            r, p = pearsonr(cnv, expr)
            results[gene] = {'r': r, 'p': p}

    # Genes with dosage effect (CNV drives expression)
    dosage_genes = {g: v for g, v in results.items() if v['r'] > 0.5 and v['p'] < 0.01}

    return results, dosage_genes

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**Method 2: Joint NMF (Non-negative Matrix Factorization)**

Decompose multi-omics matrices into shared latent components.

```python
def joint_nmf_clustering(omics_data_dict, n_clusters=3):
    """
    Perform joint NMF across omics for clustering.

    Returns patient cluster assignments based on shared factors.
    """
    # Concatenate omics matrices (after normalization)
    combined_matrix = np.vstack([
        omics_data_dict['rnaseq'].values,
        omics_data_dict['proteomics'].values,
        omics_data_dict['methylation'].values
    ])

    # Run NMF
    from sklearn.decomposition import NMF
    model = NMF(n_components=n_clusters, init='nndsvd', random_state=42)
    W = model.fit_transform(combined_matrix)  # Feature loadings
    H = model.components_  # Sample coefficients

    # Cluster samples based on H (components)
    from sklearn.cluster import KMeans
    clusters = KMeans(n_clusters=n_clusters).fit_predict(H.T)

    return clusters, W, H

def integrate_pathway_evidence(omics_results, pathway_genes):
    """
    Score pathway dysregulation across omics.

    omics_results: {
        'rnaseq': {'gene': fold_change},
        'proteomics': {'gene': fold_change},
        'methylation': {'gene': methylation_diff},
        'cnv': {'gene': copy_number}
    }

    pathway_genes: List of genes in pathway
    """
    # For each gene in pathway
    pathway_scores = []
    for gene in pathway_genes:
        gene_score = 0
        evidence_count = 0

        # RNA-seq evidence
        if gene in omics_results['rnaseq']:
            gene_score += abs(omics_results['rnaseq'][gene])
            evidence_count += 1

        # Proteomics evidence
        if gene in omics_results['proteomics']:
            gene_score += abs(omics_results['proteomics'][gene])
            evidence_count += 1

        # Methylation evidence (negative correlation)
        if gene in omics_results['methylation']:
            gene_score += abs(omics_results['methylation'][gene])
            evidence_count += 1

        # CNV evidence
        if gene in omics_results['cnv']:
            gene_score += abs(omics_results['cnv'][gene])
            evidence_count += 1

        if evidence_count > 0:
            pathway_scores.append(gene_score / evidence_count)

    # Aggregate pathway score (mean of gene scores)
    pathway_score = np.mean(pathway_scores) if pathway_scores else 0

    return {
        'pathway_score': pathway_score,
        'n_genes_with_evidence': len(pathway_scores),
        'n_omics_types': evidence_count
    }

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def select_multiomics_features(X_dict, y, n_features=50):
    """
    Select top features across omics for classification.

    X_dict: {
        'rnaseq': DataFrame (samples x genes),
        'proteomics': DataFrame (samples x proteins),
        'methylation': DataFrame (samples x CpGs)
    }
    y: Target labels (disease vs control)

    Returns: Selected features per omics
    """
    from sklearn.feature_selection import SelectKBest, f_classif

    selected_features = {}
    for omics_type, X in X_dict.items():
        selector = SelectKBest(f_classif, k=min(n_features, X.shape[1]))
        selector.fit(X, y)

        # Get selected feature names
        selected_idx = selector.get_support()
        selected_features[omics_type] = X.columns[selected_idx].tolist()

    return selected_features

def multiomics_classification(X_dict, y, selected_features):
    """
    Train classifier using multi-omics features.
    """
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import cross_val_score

    # Concatenate selected features from each omics
    X_combined = []
    for omics_type, features in selected_features.items():
        X_combined.append(X_dict[omics_type][features])

    X_combined = pd.concat(X_combined, axis=1)

    # Train classifier
    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    scores = cross_val_score(clf, X_combined, y, cv=5, scoring='roc_auc')

    return {
        'mean_auc': scores.mean(),
        'std_auc': scores.std(),
        'n_features': X_combined.shape[1],
        'features_per_omics': {k: len(v) for k, v in selected_features.items()}
    }

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