🪞 Unsupervised Machine Learning Part 1: Clustering in multi-omics Analysis🪞

In the dynamic world of bioinformatics, clustering is an indispensable tool for uncovering patterns in high-dimensional data. Be it analyzing gene expression, identifying molecular subtypes, or grouping patient cohorts, clustering reveals critical biological insights. But how do we choose the optimal number of clusters?

🖼 Key Techniques in Clustering:

🛒 Distance Metrics:

Manhattan Distance: Sum of absolute differences.
Euclidean Distance: Root of sum of squared differences.
Correlation Distance: 1 minus Pearson correlation coefficient.

🛍 Hierarchical Clustering:

Builds a dendrogram to visualize relationships between clusters using methods like:
Complete Linkage: Maximizes inter-cluster distances.
Ward’s Method: Minimizes within-cluster variance, often yielding compact clusters.

🎁 Choosing the Optimal Number of Clusters:

Silhouette Analysis:

Measures cohesion and separation. Testing different k values can reveal the best balance for clustering patient cohorts, such as distinguishing between disease states and normal states.

Gap Statistic:

Compares observed variance with a reference distribution. In some analyses, k = 6 may emerge as optimal, reflecting the complexity of molecular subtypes.

NbClust Package:

Evaluates over 30 methods to determine the best k, providing a comprehensive approach to refining clustering decisions.

📬 Real-World Application:

In multi-omics data, clustering can highlight distinct gene expression patterns associated with disease subtypes, aiding in classification and prognosis.

🪄 Key Takeaways:

No Absolute Truth: Optimal k varies based on data granularity and biological complexity.
Biological Context Matters: Clustering must align with known biology while revealing potential novel subtypes.
Visual Assessment: Heatmaps and dendrograms ensure clusters maintain meaningful biological relationships.

By refining the clustering process, it is possible to enhance the understanding of molecular subtypes and their impact on patient outcomes, driving forward precision medicine and research.

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🔭 Want some more info? Read this:
Zhang X, Zhou Z, Xu H, Liu CT. Integrative clustering methods for multi-omics data. Wiley Interdiscip Rev Comput Stat. 2022 May-Jun;14(3):e1553. doi: 10.1002/wics.1553. Epub 2021 Feb 7. PMID: 35573155; PMCID: PMC9097984.
#Bioinformatics #Genomics #DataScience #Clustering #CancerResearch #SurvivalAnalysis #MachineLearning #MultiOmics #RNASeq #TCGA #NextFlow #PrecisionMedicine #DataVisualization