Cluster analysis of big biomedical data: A how-to guide

If you’ve ever felt lost in the vast wilderness of a biomedical dataset, you’ve got a trusty compass in cluster analysis. Whether you're seeking to uncover hidden disease subtypes, identify biomarker patterns, or tailor treatments to patient profiles, cluster analysis can be a powerful tool. In this blogpost, we’ll explore different types of cluster analysis, its real-world applications, and its pros and cons as a tool to navigate the complex landscape of biomedical data.

What is Cluster Analysis?

Cluster analysis is a statistical technique that helps us discover hidden patterns and group similar items together. Think of it as sorting the pieces of a puzzle into distinct piles based on their similarities.

The primary goal of cluster analysis is to group data points or objects into clusters, where objects within the same cluster are more similar to each other than to those in other clusters. These clusters can reveal insights and structure in seemingly chaotic datasets.

Benefits of Cluster Analysis for Biomedical Researchers

Let’s now look at how cluster analysis can be a useful statistical tool during your research journey:

1. Pattern Discovery: Cluster analysis helps uncover hidden patterns and structures in biomedical data, aiding in the identification of disease subtypes, biomarkers, and treatment response groups.

2. Data Reduction: It simplifies complex datasets by grouping similar data points together, making it easier to interpret and visualize large amounts of information.
3. Hypothesis Generation: It serves as a hypothesis-generating tool, suggesting potential relationships and associations that can be further explored in experimental studies.
4. Quality Control: In research laboratories, cluster analysis can identify outliers or anomalies in experimental data, assisting in quality control processes.

Types of Cluster Analysis

There are several approaches to cluster analysis, each with its unique methods and applications. The most popular ones in biomedical research are as follows:

1. Hierarchical Clustering: This method creates a tree-like structure (dendrogram) that illustrates how data points are grouped. It can be either agglomerative (bottom-up) or divisive (top-down), allowing for a visual representation of hierarchical relationships among clusters. Biomedical researchers might use hierarchical clustering to classify patients based on gene expression profiles. This can help identify subtypes of diseases, as seen in breast cancer classification where distinct molecular subtypes have clinical implications. You can also take a look at how Sadeghi et al. (2021) used hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance across countries.

2. Partitional Clustering: Partitional clustering methods divide data into non-overlapping clusters. Common algorithms include K-Means, K-Medoids, and Partitioning Around Medoids (PAM). K-Means clustering can group patients by disease severity based on multiple clinical parameters. See how Leis et al. (2023) used K-medoids clustering to identify specific clusters of influenza patients.
3. Density-Based Clustering: These methods identify clusters based on data point density. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular example that forms clusters around regions of high data point density. DBSCAN is valuable for identifying spatially distributed disease clusters, such as identifying areas with higher incidence of infectious diseases like COVID-19. Similarly, Rangaprakash et al. (2020) used DBSCAN to identify individuals with different stages of cognitive impairment.
4. Fuzzy Clustering: Unlike traditional clustering where data points belong exclusively to one cluster, fuzzy clustering allows data points to have partial membership in multiple clusters. Fuzzy C-Means is a common technique in this category. Fuzzy C-Means can be used in neuroimaging to determine brain regions that exhibit partial activation in response to stimuli, helping map complex neural processes. Krasnov et al. (2023) provide a useful review of how Fuzzy C-Means can be used to segment breast tumors in mammograms and thus automate cancer detection.
5. Distribution-Based Clustering: These methods model data points as coming from various probability distributions. Gaussian Mixture Models (GMM) is a well-known example, assuming data arises from a mixture of Gaussian distributions. GMM can model the distribution of patient responses to a treatment, aiding in the identification of distinct response profiles in clinical trials. Liu et al. (2022) explore how GMM can be used to to detect and characterize bimodal gene expression patterns across cancer samples.
6. Self-Organizing Maps (SOM): SOM is a neural network-based clustering technique that maps high-dimensional data onto a low-dimensional grid, preserving the topological relationships among data points. Biomedical scientists use SOMs to analyze high-dimensional data like gene expression data. For instance, they might use SOMs to explore genetic variations in populations and identify disease-related patterns. Jayaraj et al. (2022) also used SOM to develop a novel ligand-based virtual screening method for drug discovery.
7. Biclustering: Biclustering methods aim to simultaneously cluster rows and columns of a data matrix, revealing subsets of data that exhibit similar behavior across both dimensions. This is particularly useful in gene expression analysis. In genomics, biclustering can identify subsets of genes and samples that exhibit specific expression patterns across multiple conditions. This helps understand how genes are co-regulated in different biological contexts. Xie et al. (2019) provide a comprehensive evaluation of available biclustering algorithms and tools in public domain.

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Challenges Associated with Cluster Analysis

Cluster Analysis is a valuable tool, but it’s not without its challenges.

1. Subjectivity: Selecting appropriate clustering algorithms and parameters can be subjective, leading to potential bias in results and the need for expert judgment.

2. Sensitivity to Data: Clustering results can vary depending on the choice of distance metrics and preprocessing steps, making it sensitive to data transformations.
3. Overfitting: In some cases, clusters may not correspond to biologically meaningful groups, leading to overinterpretation of results.
4. Interpretability: Complex clustering results can be challenging to interpret, requiring domain expertise and making them less accessible to non-experts.

Choosing the right clustering algorithm, dealing with noisy data, and determining the optimal number of clusters can be tricky. However, the insights it provides can be invaluable for making informed decisions in biomedical research.

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Conclusion

Cluster analysis is your trusty companion for exploring complex datasets in biomedical research. It can help you find order in chaos, revealing patterns and relationships that might otherwise remain hidden. Whether you’re using distribution-based clustering to uncover probability distributions or fuzzy-based clustering to embrace ambiguity, these techniques expand the horizons of what you can discover in your data. So, the next time you face a massive dataset, consider giving cluster analysis a try – it might just help you make exciting discoveries!

Need help deciding the best type of cluster analysis for your dataset and research question? Running cluster analysis for the first time? Consult an expert biostatistician under Editage’s Statistical Analysis & Review Services.