How to Generate Clustering Analysis Plots for Differential Metabolites?
Clustering analysis of differential metabolites is commonly visualized using heatmaps or cluster dendrograms. These two plot types are particularly useful for displaying the expression patterns of metabolites across various samples or conditions, along with their clustering relationships. Below is an overview of the typical steps involved in creating these visualizations:
1. Data Preparation
Begin by collecting metabolite data from all samples. To minimize the impact of varying measurement scales on the clustering results, standardization or normalization should be applied.
2. Clustering Analysis
Apply clustering algorithms, such as hierarchical clustering or K-means clustering, to group the metabolites or samples. Among these methods, hierarchical clustering is widely used, where metabolites or samples are clustered based on similarity or distance metrics (such as Euclidean or Manhattan distances).
3. Data Visualization: Heatmap Construction
Heatmaps can be created using the pheatmap function in R or the heatmap function in Python's seaborn library. In a heatmap, rows represent differential metabolites, and columns represent the samples. The color intensity reflects the expression level of each metabolite. A dendrogram is often included alongside the heatmap to show the clustering results.
4. Cluster Dendrogram Construction
In R, the hclust function is typically used for hierarchical clustering, and the plot function visualizes the dendrogram. In Python, similar tasks can be performed using the scipy.cluster.hierarchy module. The dendrogram highlights the hierarchical relationships between the clusters and provides a clear indication of similarities between metabolites or samples. It is often used in conjunction with the heatmap to offer a visual explanation of the clustering results.
These steps serve as a general guideline. In practice, adjustments should be made based on the specific data and research objectives, such as selecting different clustering methods, fine-tuning color schemes, or modifying other heatmap parameters.
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