Bulk Omics Clustering Analysis

分类: 工具与效率 | 上传者: TianGzlab | 下载: 0 | 版本: v1.0（最新）
用于对生物样本、特征或任何定量数据矩阵进行聚类的系统工作流程。通过严格的验证、比较和解释来实现多种聚类算法，以识别有意义的数据分组。
更新日志: Source: GitHub https://github.com/TianGzlab/OmicsClaw
目录结构

当前层级: tree/main/knowledge_base/bulk-omics-clustering/
📁 references/
- 📄 best_practices.md 8.4 KB
- 📄 clustering_methods_comparison.md 7.5 KB
- 📄 common-patterns.md 33.6 KB
- 📄 decision-guide.md 24.9 KB
- 📄 distance_metrics_guide.md 7.8 KB
- 📄 parameter_guide.md 9.1 KB
- 📄 r-quick-start.md 3.4 KB
- 📄 validation_metrics_guide.md 7.0 KB
📁 scripts/
- 📄 characterize_clusters.py 7.7 KB
- 📄 cluster_validation.py 14.3 KB
- 📄 density_clustering.py 9.5 KB
- 📄 dimensionality_reduction.py 9.9 KB
- 📄 distance_metrics.py 7.6 KB
- 📄 export_results.py 12.6 KB
- 📄 hierarchical_clustering.py 7.7 KB
- 📄 hierarchical_clustering.R 7.0 KB
- 📄 kmeans_clustering.py 8.9 KB
- 📄 load_example_data.py 5.9 KB
- 📄 load_example_data.R 5.8 KB
- 📄 model_based_clustering.py 11.2 KB
- 📄 optimal_clusters.py 11.8 KB
- 📄 plot_cluster_heatmap.R 4.2 KB
- 📄 plot_clustering_results.py 13.3 KB
- 📄 prepare_data.py 8.4 KB
- 📄 stability_analysis.py 14.3 KB
📄 SKILL.md 25.7 KB
SKILL.md

---
id: bulk-omics-clustering
name: Bulk Omics Clustering Analysis
category: multi_omics
short-description: Cluster samples or features from bulk transcriptomics, proteomics, or metabolomics data.
detailed-description: Perform systematic clustering analysis on biological data matrices (samples or features) using multiple algorithms with rigorous validation and interpretation. Use when you need to identify natural groupings in expression data, discover biological subtypes, group genes by expression patterns, or compare clustering methods. Supports hierarchical, k-means, HDBSCAN, and Gaussian mixture models. Includes optimal cluster number determination, quality validation, stability testing, and comprehensive visualization. Best for bulk transcriptomics, proteomics, metabolomics, or any quantitative data matrix with 10+ samples/features.
starting-prompt: Cluster my samples by gene expression to identify biological subtypes . .
---

# Bulk Omics Clustering Analysis

Systematic workflow for clustering biological samples, features, or any quantitative data matrix. Implements multiple clustering algorithms with rigorous validation, comparison, and interpretation to identify meaningful data groupings.

## When to Use This Skill

Use clustering analysis when you need to:
- ✅ **Group biological samples** by gene expression profiles (bulk RNA-seq, proteomics)
- ✅ **Identify feature patterns** (genes/proteins with similar expression across conditions)
- ✅ **Discover subtypes** in disease or treatment response groups
- ✅ **Analyze trajectories** in time-series or developmental data
- ✅ **Quality control** by detecting batch effects or outliers
- ✅ **Compare methods** by systematically evaluating multiple clustering approaches

**Don't use this skill for:**
- ❌ Single-cell RNA-seq clustering → Use scrnaseq-scanpy-core-analysis or scrnaseq-seurat-core-analysis
- ❌ Gene co-expression network analysis → Use coexpression-network

**Key Concept:** Clustering reveals natural groupings in data without prior labels. Different algorithms make different assumptions—this workflow helps you choose and validate the right approach for your data.

**Language Support:** This skill supports both **Python** and **R** implementations. Choose based on your preference and existing analysis pipeline. Python offers scikit-learn ecosystem integration; R offers ComplexHeatmap and rich Bioconductor tools.

## Quick Start (5-Minute Example)

Test the workflow with the ALL (Acute Lymphoblastic Leukemia) dataset - 128 pediatric ALL patients with B-cell and T-cell subtypes:

**R (Recommended for ALL dataset):**
```r
# 1. Load example data (ALL dataset from Chiaretti et al. 2004)
source("scripts/load_example_data.R")
data_list <- load_example_clustering_data()
data <- data_list$data
sample_names <- data_list$sample_names
feature_names <- data_list$feature_names
metadata <- data_list$metadata

# 2. Run clustering
source("scripts/hierarchical_clustering.R")
result <- hierarchical_clustering(data, n_clusters = 2)
cluster_labels <- result$cluster_labels
hclust_obj <- result$clustering_object

# 3. Visualize
source("scripts/plot_cluster_heatmap.R")
plot_cluster_heatmap(
  data,
  cluster_labels,
  output_dir = "quick_test_results"
)

# 4. Export results
# Results automatically saved by plotting functions
cat("\n✓ Quick start complete!\n")
cat(sprintf("Cell types: B-cell ALL (n=%d), T-cell ALL (n=%d)\n",
            sum(metadata$cell_type == "B"),
            sum(metadata$cell_type == "T")))
```

**Python users:** The Python implementation can use the same ALL dataset via rpy2, or use your own data. For ALL dataset examples in Python, you'll need `pip install rpy2` and R installed. See full workflow below.

**Expected output:** Dendrogram distinguishing B-cell vs T-cell ALL, PCA plots, silhouette plots, heatmaps in `quick_test_results/`

**Note:** For your own data, follow the full workflow below with the Clarification Questions.

## Installation

### Language Choice

This workflow supports both Python and R. Choose one based on:
- **Python**: Better for large-scale data (>10k samples), integration with machine learning pipelines, modern plotting (plotnine)
- **R**: Better for heatmap visualization (ComplexHeatmap), Bioconductor integration, traditional bioinformatics workflows

You can also mix: use R for visualization (heatmaps) and Python for clustering algorithms.

### Python Installation

### Required Software

| Software | Version | License | Commercial Use | Installation |
|----------|---------|---------|----------------|--------------|
| numpy | ≥1.20 | BSD-3-Clause | ✅ Permitted | `pip install numpy` |
| pandas | ≥1.3 | BSD-3-Clause | ✅ Permitted | `pip install pandas` |
| scikit-learn | ≥1.0 | BSD-3-Clause | ✅ Permitted | `pip install scikit-learn` |
| scipy | ≥1.7 | BSD-3-Clause | ✅ Permitted | `pip install scipy` |
| hdbscan | ≥0.8.28 | BSD-3-Clause | ✅ Permitted | `pip install hdbscan` |
| umap-learn | ≥0.5 | BSD-3-Clause | ✅ Permitted | `pip install umap-learn` |
| plotnine | ≥0.10 | MIT | ✅ Permitted | `pip install plotnine` |
| plotnine-prism | Latest | MIT | ✅ Permitted | `pip install plotnine-prism` |
| seaborn | ≥0.11 | BSD-3-Clause | ✅ Permitted | `pip install seaborn` |
| matplotlib | ≥3.4 | PSF-based | ✅ Permitted | `pip install matplotlib` |
| adjustText | ≥0.8 | MIT | ✅ Permitted | `pip install adjustText` |
| statsmodels | ≥0.13 | BSD-3-Clause | ✅ Permitted | `pip install statsmodels` |

**Optional:** gap-stat (Apache 2.0), yellowbrick (Apache 2.0) - both permitted for commercial use

**Minimum Python version:** Python ≥3.8

**License Compliance:** All software packages use BSD-3-Clause, MIT, or similar permissive licenses that allow commercial use in AI agent applications.

### R Installation

**Minimum R version:** R ≥4.0

**Required packages:** cluster, factoextra, NbClust, ComplexHeatmap, pheatmap, dendextend, dbscan, mclust, ggplot2, ggprism (all GPL/MIT licensed, commercial use permitted)

**Quick install:**
```r
install.packages(c('cluster', 'factoextra', 'NbClust', 'pheatmap',
                   'dendextend', 'dbscan', 'mclust', 'ggplot2', 'ggprism'))
if (!require('BiocManager')) install.packages('BiocManager')
BiocManager::install('ComplexHeatmap')
```

**Detailed R installation guide:** [references/r-quick-start.md#r-installation](references/r-quick-start.md#r-installation)

## Inputs

### Required Input

**1. Data Matrix** (CSV, TSV, Excel, or HDF5):
- **Sample clustering**: Rows = samples, Columns = features (genes/proteins)
- **Feature clustering**: Rows = features, Columns = samples
- Values should be normalized and comparable (TPM, FPKM, VST, Z-scores)
- Missing values: handle by imputation or removal before clustering

**2. Sample/Feature Metadata** (CSV or TSV, optional but recommended):
- IDs matching data matrix rows/columns
- Annotations for validation (tissue type, condition, known groups)
- Used to interpret and validate clustering results

### Data Requirements

- **Minimum samples/features**: 10+ (20+ recommended for robust clustering)
- **Normalization**: Required—use appropriate method for data type
- **Batch effects**: Remove or regress out before clustering
- **Outliers**: Identify and consider removing extreme outliers
- **Dimensionality**: High-dimensional data (>1000 features) benefits from PCA first

### Supported Data Types

- **Transcriptomics**: Bulk RNA-seq (normalized counts, TPM, FPKM)
- **Proteomics**: Protein abundance data
- **Metabolomics**: Metabolite concentrations
- **Multi-omics**: Integrated features from multiple assays
- **Clinical**: Patient feature matrices
- **Any quantitative matrix**: General-purpose application

## Outputs

**Cluster assignments:**
- `clustering_assignments.csv` - Sample/feature IDs with cluster labels
- `clustering_statistics.csv` - Size, centroids, and characteristics per cluster

**Validation metrics:**
- `clustering_validation_metrics.json` - Silhouette, Davies-Bouldin, Calinski-Harabasz scores
- `stability_results.csv` - Bootstrap resampling stability scores (if run)

**Characterization:**
- `clustering_cluster*_features.csv` - Top distinguishing features for each cluster (ANOVA/Kruskal-Wallis)
- `clustering_all_cluster_features.csv` - Combined features for all clusters

**Analysis objects (pickle/RDS):**
- `clustering_analysis_object.pkl` - Complete clustering object for downstream use
  - Load with: `import pickle; obj = pickle.load(open('clustering_analysis_object.pkl', 'rb'))`
  - Contains: linkage matrix (hierarchical), fitted model (k-means, GMM, HDBSCAN)
  - Required for: Advanced analysis, re-cutting dendrograms, model inspection
- `clustering_data_matrix.csv` - Normalized/transformed data matrix used for clustering

**Visualizations (PNG + SVG at 300 DPI):**
- Dendrogram (hierarchical clustering)
- PCA/UMAP scatter plots with cluster colors
- Silhouette plots
- Cluster heatmaps
- Cluster size barplots
- Optimal k determination plots (if run)

**Supporting data:**
- `clustering_parameters.json` - All parameters used in analysis

## Clarification Questions

Before starting analysis, gather the following information:

### 1. **Input Files** (ASK THIS FIRST):
   - Do you have specific gene expression or omics data file(s) to cluster?
   - If you've uploaded a file, is it the data matrix (samples × features or features × samples) you'd like to analyze?
   - Expected file types: CSV, TSV, Excel (.xlsx), HDF5 (.h5), or RDS (R data objects)
   - **Or use ALL (Acute Lymphoblastic Leukemia) example data?** (Real patient data: 128 pediatric ALL samples with B-cell and T-cell subtypes, 1000 most variable genes. Dynamically loads from Bioconductor in ~30 seconds via [scripts/load_example_data.R](knowledge_base/scripts/bulk-omics-clustering/load_example_data.R). Citation: Chiaretti et al. 2004. **R recommended** for this dataset.)

### 2. **Which programming language do you prefer?**
   - **Python**: Modern ecosystem with scikit-learn, better for large datasets (>10k samples), machine learning integration
   - **R**: Rich Bioconductor tools, excellent heatmap visualization (ComplexHeatmap), traditional bioinformatics workflows
   - **Both**: Use Python for clustering algorithms, R for heatmap visualization (recommended for best of both worlds)
   - **No preference**: Will use Python (default, more comprehensive scripts available)

### 3. **What are you clustering?**
   - **Samples** (rows = samples, columns = features like genes)
     - Example: Group patients by gene expression profiles
   - **Features** (rows = features, columns = samples)
     - Example: Group genes by expression patterns across conditions
   - **Both** (explore both clustering directions)

### 4. **What is your data format and normalization status?**
   - **Bulk RNA-seq**: TPM, FPKM, VST, rlog
   - **Proteomics**: TMT/iTRAQ normalized intensities, LFQ
   - **Metabolomics**: Normalized peak areas
   - **Already normalized**: Z-scores, scaled values
   - **Raw data**: Needs normalization (specify data type)

### 5. **What clustering approach do you prefer?**
   - **Hierarchical (recommended for exploration)**: Creates a tree-like dendrogram showing relationships at all scales. You can cut the tree at any height to get different numbers of clusters after seeing the structure. Best for visualization and when you don't know k. Deterministic (same results every run). Works well for small-medium datasets (<5k samples).
   - **K-means**: Fast partitioning method that requires specifying k upfront. Best for large datasets (>5k samples) with spherical/compact clusters. Results may vary between runs, so use high n_init.
   - **Density-based (HDBSCAN)**: Automatically finds k by identifying dense regions. Can detect arbitrary cluster shapes and marks outliers. No need to specify k. Best when clusters have varying densities or you want outlier detection.
   - **Model-based (GMM)**: Probabilistic soft clustering where samples have membership probabilities for each cluster. Useful when clusters overlap or you need uncertainty estimates. Requires specifying k.
   - **Compare all methods**: Systematic comparison (recommended for exploratory analysis)

### 6. **Do you know the expected number of clusters (k)?**
   - **Yes, k = [number]**: Will validate this choice with quality metrics
   - **No, exploratory**: Will test multiple k values (2-15) and help determine optimal k
   - **Approximate range**: k between [min] and [max]
   - **Not needed**: Hierarchical clustering lets you choose k later; HDBSCAN finds k automatically

### 7. **What distance metric is most appropriate?**
   - **Euclidean**: Default for most continuous data
   - **Correlation**: For gene expression (focuses on pattern similarity)
   - **Manhattan**: Robust to outliers
   - **Cosine**: For high-dimensional sparse data
   - **Unsure**: Will use Euclidean (default)

### 8. **Should we apply dimensionality reduction first?**
   - **Yes, use PCA**: Recommended for >1000 features
   - **Yes, use UMAP**: For visualization and manifold learning
   - **No**: Keep original feature space (<100 features)
   - **Both**: PCA for clustering, UMAP for visualization

### 9. **How should cluster quality be validated?**
   - **Internal metrics only**: Silhouette, Davies-Bouldin, Calinski-Harabasz
   - **Known labels available**: Compare to known groups (adjusted Rand index)
   - **Stability analysis**: Bootstrap resampling to test robustness
   - **Biological validation**: Check for differential features between clusters

## Standard Workflow

> **Note:** Run from the OmicsClaw root directory and add the workflow scripts to `sys.path`:
> ```python
> import sys; import os; sys.path.insert(0, os.path.abspath('knowledge_base/scripts/bulk-omics-clustering'))
> ```

🚨 **MANDATORY: USE SCRIPTS EXACTLY AS SHOWN - DO NOT WRITE INLINE CODE** 🚨

**Note:** This skill requires decisions (algorithm, distance metric, k value) - adapt parameters to your data using guidance in [references/decision-guide.md](references/decision-guide.md).

**Step 1 - Load data:**

**For ALL example data (R recommended):**
```r
source("scripts/load_example_data.R")
data_list <- load_example_clustering_data()
data <- data_list$data
sample_names <- data_list$sample_names
feature_names <- data_list$feature_names
metadata <- data_list$metadata
```

**For your own data (Python or R):**
```python
# Python
from prepare_data import load_and_prepare_data
data, sample_names, feature_names = load_and_prepare_data(
    "your_data.csv",
    normalize_method="zscore"
)
```

```r
# R
source("scripts/prepare_data.R")
data_list <- load_and_prepare_data("your_data.csv", normalize_method = "zscore")
data <- data_list$data
```

**DO NOT write custom data loading code. Use the provided functions.**

**Step 2 - Run clustering analysis:**

```python
# Choose ONE clustering method and run it:

# Option A: Hierarchical clustering (recommended for exploration)
from hierarchical_clustering import hierarchical_clustering
linkage_matrix, cluster_labels = hierarchical_clustering(
    data,
    n_clusters=2,  # For ALL dataset: B-cell vs T-cell; adjust for your data
    linkage_method="ward",
    distance_metric="euclidean"
)
clustering_object = linkage_matrix

# Option B: K-means clustering (fast, for large datasets)
from kmeans_clustering import kmeans_clustering
cluster_labels, kmeans_model = kmeans_clustering(
    data,
    n_clusters=2,  # For ALL dataset: B-cell vs T-cell; adjust for your data
    n_init=50
)
clustering_object = kmeans_model

# Option C: HDBSCAN (automatic k detection)
from density_clustering import hdbscan_clustering
cluster_labels, hdbscan_model = hdbscan_clustering(
    data,
    min_cluster_size=10
)
clustering_object = hdbscan_model

# Validate clustering quality
from cluster_validation import validate_clustering
validation_results = validate_clustering(data, cluster_labels, metrics="all")
print(f"Silhouette score: {validation_results['silhouette']:.3f}")
```
**DO NOT write custom clustering code. Use one of the provided methods.**

**Step 3 - Generate visualizations:**

```python
# Generate comprehensive plots
from plot_clustering_results import plot_all_results
from dimensionality_reduction import apply_pca, apply_umap

# Optional: compute PCA/UMAP for visualization
pca_data = apply_pca(data, n_components=2)
umap_data = apply_umap(data, n_components=2)

# Create all plots
plot_all_results(
    data=data,
    cluster_labels=cluster_labels,
    sample_names=sample_names,
    feature_names=feature_names,
    pca_data=pca_data,
    umap_embedding=umap_data,
    linkage_matrix=clustering_object if 'linkage_matrix' in locals() else None,
    output_dir="clustering_results"
)
```
🚨 **DO NOT write inline plotting code (matplotlib.pyplot.savefig, seaborn.clustermap, etc.). Use plot_all_results().** 🚨

**The script handles PNG + SVG export with graceful fallback for optional dependencies.**

**Step 4 - Export results:**

```python
# Export all results including analysis objects for downstream use
from export_results import export_all

export_all(
    data=data,
    cluster_labels=cluster_labels,
    sample_names=sample_names,
    feature_names=feature_names,
    validation_results=validation_results,
    clustering_object=clustering_object,
    output_dir="clustering_results"
)
```
**DO NOT write custom export code. Use export_all().**

**✅ VERIFICATION - You should see:**
- After Step 1: `"✓ Data loaded successfully!"`
- After Step 2: `"✓ Clustering completed successfully!"`
- After Step 3: `"✓ Plots saved to clustering_results"`
- After Step 4: `"=== Export Complete ==="`

**❌ IF YOU DON'T SEE THESE:** You wrote inline code. Stop and use the scripts as shown.

⚠️ **CRITICAL - DO NOT:**
- ❌ **Write inline clustering algorithms** → **STOP: Use provided clustering functions**
- ❌ **Write inline plotting code (matplotlib.pyplot.savefig, seaborn.clustermap, etc.)** → **STOP: Use `plot_all_results()`**
- ❌ **Write custom export code** → **STOP: Use `export_all()`**
- ❌ **Try to install plotting dependencies manually** → scripts handle fallback automatically

**⚠️ IF SCRIPTS FAIL - Script Failure Hierarchy:**
1. **Fix and Retry (90%)** - Install missing package, re-run script
2. **Modify Script (5%)** - Edit the script file itself, document changes
3. **Use as Reference (4%)** - Read script, adapt approach, cite source
4. **Write from Scratch (1%)** - Only if genuinely impossible, explain why

**NEVER skip directly to writing inline code without trying the script first.**

**For detailed parameter guidance and decision-making**, see:
- Algorithm selection: [references/decision-guide.md#algorithm](references/decision-guide.md#algorithm)
- Distance metrics: [references/decision-guide.md#distance](references/decision-guide.md#distance)
- Optimal k: [references/decision-guide.md#optimal-k](references/decision-guide.md#optimal-k)

## Common Issues

| Error | Cause | Solution |
|-------|-------|----------|
| `ValueError: n_clusters exceeds n_samples` | Too many clusters requested for dataset size | Reduce k or ensure data has ≥10 samples |
| `Ward linkage requires euclidean distance` | Incompatible linkage method and distance metric | Use euclidean distance with ward, or switch to average/complete linkage |
| `ModuleNotFoundError: No module named 'plotnine_prism'` | Missing optional visualization dependency | Install: `pip install plotnine-prism` |
| Silhouette score < 0.25 | Poor cluster separation, k may be wrong | Try different k values, check data quality, use optimal k methods |
| HDBSCAN returns only noise (-1 labels) | min_cluster_size too large for dataset | Reduce min_cluster_size (try 5-10 for small datasets) or increase min_samples |
| `ValueError: Input contains NaN` | Missing values in data matrix | Impute or remove NaN values before clustering |
| Clusters of very unequal sizes | Algorithm bias or true biological structure | Validate with biology; try HDBSCAN for density-based approach |
| Different runs give different clusters (k-means) | Random initialization varies | Increase n_init (50-100) for stable results, or use hierarchical clustering |

**Debugging checklist:**
1. Check data shape and missing values: `data.shape`, `np.isnan(data).sum()`
2. Verify normalization: Data should be comparable across features (z-scores, scaled)
3. Check cluster labels: Should range from 0 to k-1 (or include -1 for noise in HDBSCAN)
4. Validate with metrics: Silhouette >0.3 is decent, >0.5 is good
5. Inspect visually: PCA/UMAP plots should show clear groupings

## Decision Guide

Make three critical decisions before clustering:

| Decision | Quick Guide | Detailed Reference |
|----------|-------------|-------------------|
| **Algorithm** | Hierarchical (<5k samples, exploration), K-means (>5k, speed), HDBSCAN (unknown k, outliers), GMM (soft clustering) | [decision-guide.md#algorithm](references/decision-guide.md#algorithm) |
| **Distance** | Correlation (gene expression), Euclidean (normalized data), Manhattan (outliers), Cosine (sparse) | [decision-guide.md#distance](references/decision-guide.md#distance) |
| **Cluster # (k)** | Use multiple metrics (elbow, silhouette, gap), prioritize silhouette, consider biology | [decision-guide.md#optimal-k](references/decision-guide.md#optimal-k) |

**Comprehensive decision guidance:** [references/decision-guide.md](references/decision-guide.md)

## Common Patterns

**Pattern 1:** Sample Subtype Discovery - [references/common-patterns.md#pattern-1](references/common-patterns.md#pattern-1)
**Pattern 2:** Gene Co-clustering - [references/common-patterns.md#pattern-2](references/common-patterns.md#pattern-2)
**Pattern 3:** Method Comparison - [references/common-patterns.md#pattern-3](references/common-patterns.md#pattern-3)
**Additional:** QC/outlier detection, stability testing, batch validation - [references/common-patterns.md](references/common-patterns.md)

## Suggested Next Steps

After completing clustering:

1. **Differential Analysis** - Use bulk-rnaseq-counts-to-de-deseq2 to identify cluster-specific signatures
2. **Functional Enrichment** - Use functional-enrichment-from-degs to interpret cluster biology
3. **Refinement** - If unclear: try different metrics, adjust k, remove outliers, use variable features subset

## Related Skills

**Prerequisite:** bulk-rnaseq-counts-to-de-deseq2 (normalize RNA-seq data)
**Alternatives:** scrnaseq-scanpy-core-analysis, scrnaseq-seurat-core-analysis (single-cell), coexpression-network
**Downstream:** de-results-to-gene-lists, functional-enrichment-from-degs, de-results-to-plots

## References

**Reference documentation:**
- [references/decision-guide.md](references/decision-guide.md) - Comprehensive decision guidance (algorithm, distance, k)
- [references/common-patterns.md](references/common-patterns.md) - Additional use case patterns
- [references/clustering_methods_comparison.md](references/clustering_methods_comparison.md) - Algorithm selection guide
- [references/validation_metrics_guide.md](references/validation_metrics_guide.md) - Metric interpretation
- [references/distance_metrics_guide.md](references/distance_metrics_guide.md) - Distance metric selection
- [references/parameter_guide.md](references/parameter_guide.md) - Parameter tuning details
- [references/best_practices.md](references/best_practices.md) - Workflow best practices

**Python scripts:**
- [scripts/prepare_data.py](knowledge_base/scripts/bulk-omics-clustering/prepare_data.py) - Data loading and preprocessing
- [scripts/distance_metrics.py](knowledge_base/scripts/bulk-omics-clustering/distance_metrics.py) - Distance calculations
- [scripts/dimensionality_reduction.py](knowledge_base/scripts/bulk-omics-clustering/dimensionality_reduction.py) - PCA, UMAP
- [scripts/hierarchical_clustering.py](knowledge_base/scripts/bulk-omics-clustering/hierarchical_clustering.py) - Hierarchical methods
- [scripts/kmeans_clustering.py](knowledge_base/scripts/bulk-omics-clustering/kmeans_clustering.py) - K-means variants
- [scripts/density_clustering.py](knowledge_base/scripts/bulk-omics-clustering/density_clustering.py) - DBSCAN, HDBSCAN
- [scripts/model_based_clustering.py](knowledge_base/scripts/bulk-omics-clustering/model_based_clustering.py) - GMM
- [scripts/optimal_clusters.py](knowledge_base/scripts/bulk-omics-clustering/optimal_clusters.py) - Optimal k determination
- [scripts/cluster_validation.py](knowledge_base/scripts/bulk-omics-clustering/cluster_validation.py) - Quality metrics
- [scripts/stability_analysis.py](knowledge_base/scripts/bulk-omics-clustering/stability_analysis.py) - Bootstrap validation
- [scripts/characterize_clusters.py](knowledge_base/scripts/bulk-omics-clustering/characterize_clusters.py) - Feature importance
- [scripts/plot_clustering_results.py](knowledge_base/scripts/bulk-omics-clustering/plot_clustering_results.py) - Visualizations
- [scripts/export_results.py](knowledge_base/scripts/bulk-omics-clustering/export_results.py) - Export functions

**R scripts:**
- [scripts/hierarchical_clustering.R](knowledge_base/scripts/bulk-omics-clustering/hierarchical_clustering.R) - Hierarchical clustering with dendextend
- [scripts/plot_cluster_heatmap.R](knowledge_base/scripts/bulk-omics-clustering/plot_cluster_heatmap.R) - ComplexHeatmap visualization (recommended for publication figures)
- Additional R implementations available upon request

**Evaluation:**
- [eval/complete_example_analysis.py](eval/complete_example_analysis.py) - Full workflow example

**Online resources:**

*Python:*
- [scikit-learn clustering](https://scikit-learn.org/stable/modules/clustering.html)
- [HDBSCAN documentation](https://hdbscan.readthedocs.io/)

*R:*
- [factoextra clustering](https://rpkgs.datanovia.com/factoextra/)
- [ComplexHeatmap vignettes](https://jokergoo.github.io/ComplexHeatmap-reference/book/)
- [dendextend tutorial](https://cran.r-project.org/web/packages/dendextend/vignettes/dendextend.html)

**Key papers:**
- Rousseeuw (1987) - Silhouettes for cluster validation
- Tibshirani et al. (2001) - Gap statistic
- McInnes et al. (2017) - HDBSCAN algorithm
- D'haeseleer (2005) - Clustering in genomics
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Bulk Omics Clustering Analysis

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