Cluster Analysis in Biomedical Research: Types, Methods & How-To Guide

Contents

• What Is Cluster Analysis?
• Benefits of Cluster Analysis for Biomedical Researchers
• Types of Cluster Analysis
• Comparison of Clustering Methods
• How to Choose the Right Clustering Algorithm
• Step-by-Step: How to Perform Cluster Analysis on Biomedical Data
• Tools and Software for Cluster Analysis in Biomedical Research
• Challenges and Limitations of Cluster Analysis
• Conclusion
• Frequently Asked Questions

Cluster analysis is one of the most powerful unsupervised machine learning techniques available to biomedical researchers. Whether you’re trying to uncover hidden disease subtypes, identify biomarker patterns, stratify patients for clinical trials, or make sense of high-dimensional genomics data, cluster analysis can reveal structure that would otherwise remain invisible. This guide walks you through what cluster analysis is, the most important algorithms used in biomedical research, how to choose the right one, how to run an analysis step by step, and how to validate your results.

What Is Cluster Analysis?

Cluster analysis is a statistical technique that groups data points into clusters based on their similarity. Objects within the same cluster are more like each other than they are to objects in other clusters. Unlike supervised machine learning, cluster analysis does not rely on predefined labels but instead it discovers structure from the data itself.

In biomedical research, this makes it especially valuable. You rarely know in advance how many disease subtypes exist in a cohort, or which gene expression patterns are biologically meaningful. Cluster analysis lets the data tell that story.

Benefits of Cluster Analysis for Biomedical Researchers

• Pattern discovery: Identifies hidden groupings in patient cohorts, genomics datasets, and clinical records that are not apparent through conventional analysis
• Disease subtyping: Reveals molecular or clinical subtypes within a disease, as seen in breast cancer where distinct subtypes have direct implications for prognosis and treatment
• Biomarker identification: Groups samples or variables with similar expression or measurement profiles, pointing toward candidate biomarkers
• Data reduction: Simplifies high-dimensional datasets by representing them as a smaller number of meaningful clusters
• Hypothesis generation: Suggests relationships and associations that can be tested in downstream experimental or clinical studies
• Quality control: Detects outliers and anomalous samples in laboratory data before they distort your analysis

Types of Cluster Analysis

There are several clustering approaches, each suited to different data structures and research questions. The most commonly used in biomedical research are described below.

Hierarchical Clustering

Hierarchical clustering builds a tree-like diagram called a dendrogram that shows how data points are progressively merged (agglomerative, bottom-up) or split (divisive, top-down) into clusters. Researchers can cut the dendrogram at any level to define a desired number of clusters.

Example:

Classifying patients based on gene expression profiles to identify disease subtypes. This approach was used by Sadeghi et al. (2021) to evaluate COVID-19 pandemic preparedness across countries.

Partitional Clustering (K-Means, K-Medoids, PAM)

Partitional methods divide data into a fixed number of non-overlapping clusters. K-Means assigns each data point to the nearest cluster centroid and iterates until stable. K-Medoids and Partitioning Around Medoids (PAM) are more robust variants that use actual data points as cluster centers, making them less sensitive to outliers.

Example

Grouping patients by disease severity across multiple clinical parameters. Leis et al. (2023) used K-medoids clustering to identify distinct clusters among influenza patients.

Density-Based Clustering (DBSCAN)

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters as dense regions of data points, separated by areas of lower density. It can find arbitrarily shaped clusters and is naturally robust to noise and outliers: it simply labels sparse points as noise rather than forcing them into a cluster.

Example

Identifying spatially distributed disease clusters or mapping regions of higher infectious disease incidence. Rangaprakash et al. (2020) applied DBSCAN to identify individuals at different stages of cognitive impairment.

Fuzzy Clustering (Fuzzy C-Means)

Unlike hard clustering methods where each data point belongs to exactly one cluster, fuzzy clustering assigns each point a membership score for every cluster. This is biologically realistic in that a cell or patient may display characteristics of more than one subtype.

Example

Neuroimaging studies where brain regions show partial activation. Krasnov et al. (2023) reviewed how Fuzzy C-Means can automate breast tumor segmentation in mammograms.

Distribution-Based Clustering (Gaussian Mixture Models)

Gaussian Mixture Models (GMM) assume that the data arises from a mixture of Gaussian probability distributions. Each cluster corresponds to one distribution, and data points are assigned probabilistically based on how well they fit each distribution.

Example:

Modeling the distribution of patient responses to a treatment to identify distinct responder profiles in clinical trials. Liu et al. (2022) used GMM to detect bimodal gene expression patterns across cancer samples.

Self-Organizing Maps (SOM)

SOM is a neural network-based technique that maps high-dimensional data onto a low-dimensional grid while preserving the topological structure of the original data. It is particularly effective for visualizing complex, high-dimensional biomedical datasets.

Example:

Analyzing gene expression variation in populations or developing ligand-based virtual screening approaches for drug discovery, as demonstrated by Jayaraj et al. (2022).

Biclustering

Biclustering simultaneously clusters both rows and columns of a data matrix, identifying subsets of samples and features (e.g., genes and patients) that co-vary together. This is distinct from standard clustering, which only clusters along one dimension.

Example:

Identifying subsets of genes that are co-regulated across specific patient subgroups. Xie et al. (2019) provide a comprehensive evaluation of biclustering algorithms and tools available in the public domain.

Comparison of Clustering Methods

How to Choose the Right Clustering Algorithm

Selecting the right algorithm depends on the nature of your data and your research question. Use the following decision guide:

• Do you know how many clusters to expect? If yes, K-Means, K-Medoids, or GMM are natural starting points. If no, hierarchical clustering or DBSCAN allow you to explore structure without committing upfront.
• Is your data noisy or does it contain outliers? DBSCAN and K-Medoids handle noise better than K-Means. Avoid K-Means on datasets with many outliers.
• Do your data points belong cleanly to one group? If biological ambiguity is expected (e.g., transitional cell states, mixed phenotypes), use Fuzzy C-Means.
• Is your dataset high-dimensional (e.g., RNA-seq, proteomics)? Consider SOM or biclustering, or apply dimensionality reduction (PCA, UMAP) before clustering.
• Do you need to cluster both samples and features simultaneously? Use biclustering for gene-patient co-analysis.
• Is your data spatially distributed? DBSCAN is the strongest option for geographic or spatial disease mapping.

Step-by-Step: How to Perform Cluster Analysis on Biomedical Data

Here is a practical walkthrough using a common scenario: you have RNA-seq data from 200 cancer patients and want to identify molecular subtypes.

Step 1: Define Your Research Question

Clarify what you are clustering (samples, genes, or both) and what the clusters should represent biologically. For this scenario, you are clustering patient samples to find disease subtypes based on gene expression profiles.

Step 2: Prepare and Preprocess Your Data

Raw data almost never goes straight into a clustering algorithm. Standard preprocessing steps include:

• Normalization: Adjust for sequencing depth (e.g., TPM, CPM, or DESeq2 normalization for RNA-seq data) so that differences in library size don’t drive clustering
• Log transformation: Compress the dynamic range of expression values to reduce the influence of extreme values
• Batch correction: If samples were processed in multiple experimental batches, use tools like ComBat to remove batch effects before clustering
• Missing value handling: Impute or remove missing values, as most clustering algorithms cannot handle them natively
• Scaling: Standardize each feature to have mean 0 and standard deviation 1, unless your algorithm (like K-Medoids on mixed data) benefits from raw scales

Step 3: Reduce Dimensionality (Recommended for High-Dimensional Data)

For RNA-seq datasets with thousands of genes, apply dimensionality reduction before clustering:

• PCA (Principal Component Analysis): Reduces to the top components explaining the most variance; computationally fast
• UMAP: Preserves local and global structure; excellent for visualization and pre-clustering
• t-SNE: Powerful for visualization but less suitable as a direct precursor to clustering due to its stochastic nature

Retaining the top 10–50 principal components before running K-Means or hierarchical clustering is a common and effective practice.

Step 4: Select and Run Your Clustering Algorithm

For patient subtyping from RNA-seq data, hierarchical clustering or K-Means are frequently used first passes. Run the algorithm using your chosen software (see the Tools section below). At this stage, you may try several algorithms in parallel to compare results.

Step 5: Determine the Optimal Number of Clusters

If your algorithm requires specifying the number of clusters (k), use these methods to guide that decision:

• Elbow method: Plot within-cluster sum of squares against k; look for the “elbow” where improvement plateaus
• Silhouette analysis: Measures how similar a point is to its own cluster versus neighboring clusters; higher scores indicate better-defined clusters
• Gap statistic: Compares the observed clustering quality to a null reference distribution
• Calinski-Harabasz index: Higher values indicate denser, better-separated clusters

Step 6: Validate Your Clusters

Clustering produces results: but are those results biologically meaningful? Validation is essential:

• Internal validation: Use silhouette scores, Davies-Bouldin index, and Calinski-Harabasz index to assess cluster quality without external labels
• External validation: If you have any ground truth (e.g., known clinical outcomes, survival data), check whether your clusters align with them using survival analysis or clinical correlation
• Stability analysis: Re-run the algorithm with bootstrapped subsamples of the data. Stable clusters will re-emerge consistently; unstable ones likely reflect noise
• Biological validation: Perform gene ontology enrichment or pathway analysis on the genes driving each cluster to confirm biological coherence

Step 7: Interpret and Visualize Results

Effective visualization is critical for communicating clustering results:

• Heatmaps with dendrograms: Standard for gene expression cluster visualization
• UMAP or t-SNE plots: Color-coded by cluster assignment to show separation in reduced-dimensional space
• Kaplan-Meier survival curves: Overlay cluster labels to show whether clusters have distinct clinical outcomes
• Boxplots or violin plots: Compare clinical variables (age, tumor grade, biomarker levels) across clusters

Tools and Software for Cluster Analysis in Biomedical Research

R Packages

• cluster: Core functions for K-Medoids (PAM), hierarchical clustering, and cluster validation
• factoextra: Visualization of clustering results, elbow plots, silhouette plots
• Seurat: The standard toolkit for single-cell RNA-seq clustering
• ConsensusClusterPlus: Robust consensus clustering with stability assessment, widely used in cancer genomics
• mclust: Gaussian Mixture Model clustering

Python Libraries

• scikit-learn: Comprehensive implementations of K-Means, DBSCAN, GMM, agglomerative clustering, and validation metrics
• scipy: Hierarchical clustering and dendrogram plotting
• scanpy: Single-cell analysis in Python, including graph-based clustering (Leiden, Louvain)
• hdbscan: An improved, hierarchical extension of DBSCAN

Specialized Tools

• WEKA: GUI-based platform suitable for researchers without programming experience
• GenePattern: Web-based platform with clustering modules for gene expression data
• Cluster 3.0 + Java TreeView: Classic combination for hierarchical clustering and heatmap visualization

Challenges and Limitations of Cluster Analysis

Cluster analysis is a powerful tool, but it has important limitations that every researcher should understand before interpreting results.

• Subjectivity in algorithm and parameter choice: There is no universally correct clustering method. Different algorithms applied to the same dataset can yield very different results. Expert judgment is essential.
• Sensitivity to preprocessing decisions: The choice of normalization method, distance metric, and scaling approach can substantially influence which clusters emerge. Always document and justify these decisions.
• Determining the optimal number of clusters is not trivial: Quantitative metrics (silhouette, elbow) help, but the final decision often requires biological interpretation, not just statistical guidance.
• Risk of overfitting and overinterpretation: Clustering algorithms will always produce clusters, even in random data. Results must be validated: biological plausibility and stability testing are non-negotiable.
• Interpretability in high dimensions: When clusters are derived from thousands of features, explaining what drives the separation between clusters requires careful downstream analysis (e.g., differential expression, pathway enrichment).
• Scalability: Some algorithms (particularly hierarchical clustering) scale poorly to very large datasets. For datasets with tens of thousands of samples, approximate methods or graph-based clustering (Leiden algorithm) are preferred.

Conclusion

Cluster analysis is an indispensable tool in the biomedical researcher’s statistical toolkit. From identifying molecular subtypes of cancer to mapping patient response profiles in clinical trials, it enables discovery-driven science in datasets too complex for traditional hypothesis-driven approaches. The key to success lies in thoughtful preprocessing, informed algorithm selection, rigorous validation, and biologically grounded interpretation. Used carefully, cluster analysis does not just organize data: it generates the hypotheses that drive the next wave of biomedical discovery.

Frequently Asked Questions

What is the difference between hierarchical and K-Means clustering?

Hierarchical clustering builds a tree of nested groupings without requiring you to specify the number of clusters in advance. K-Means requires you to specify k upfront and assigns each data point to exactly one cluster by minimizing distance to cluster centroids. Hierarchical clustering is better for exploratory analysis and smaller datasets; K-Means is faster and better suited to large cohorts.

How do I decide how many clusters to use in K-Means?

Use a combination of the elbow method (plotting within-cluster sum of squares vs. k), silhouette analysis (measuring cluster cohesion and separation), and the gap statistic. No single metric is definitive: cross-reference quantitative guidance with biological interpretation. If k=3 and k=4 produce similar scores but k=3 maps onto known disease stages, the biologically interpretable solution is often preferred.

What is the best clustering method for gene expression data?

It depends on the data type and research goal. For bulk RNA-seq patient subtyping, hierarchical clustering and ConsensusClusterPlus are widely used and well-validated. For single-cell RNA-seq, graph-based methods (Leiden or Louvain algorithms, implemented in Seurat or scanpy) are the current standard. For identifying gene-patient co-expression patterns, biclustering is the most appropriate approach.

Can cluster analysis be used for clinical trial data?

Yes. Cluster analysis is valuable in clinical trials for identifying patient subgroups with distinct treatment responses (responders vs. non-responders), which supports post-hoc subgroup analysis and can inform patient stratification in future trials. Gaussian Mixture Models and K-Medoids are commonly used for this purpose. Results should be treated as hypothesis-generating and validated in independent cohorts.

How do I know if my clustering results are statistically valid?

There is no single significance test equivalent for clustering. A multi-pronged validation approach is recommended: assess internal quality using silhouette scores and the Davies-Bouldin index; test stability by re-clustering bootstrapped subsamples of the data; validate externally by checking whether clusters correlate with known clinical outcomes or biological annotations; and perform biological validation through pathway enrichment analysis to confirm that the gene signatures driving each cluster are coherent and interpretable.

References

1. Nikolic S, et al. (2025). Cluster Analysis: Theory, Methodology, and Applications. https://www.seejph.com/index.php/seejph/article/view/4515
2. KNIME (2026). Cluster analysis: What it is, types & how to apply the technique without code. https://www.knime.com/blog/what-is-clustering-how-does-it-work
3. Xu R, Wunsch DC (2010). Clustering algorithms in biomedical research: a review. IEEE Rev Biomed Eng. 2010:3:120-54. doi: 10.1109/RBME.2010.2083647.