Bulk Omics Clustering Analysis

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):

# 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")))