Generating Distinct Heatmaps
To gain comprehensive insights into your dataset, we generate three distinct heatmaps, each based on a different distance metric. These heatmaps visually represent the relationships and patterns within your data.
To analyze these heatmaps you must know below 6 points:
- Understanding the Color Scale: Heatmaps use color gradients to represent data values. Warmer colors (e.g., red) typically signify higher values, while cooler colors (e.g., blue) represent lower values. This color scale helps interpret the intensity or magnitude of data.
- Identifying Clusters: Look for groups of similar elements within rows and columns, often indicated by dendrogram branches.
- Interpreting Dendrograms: Examine dendrograms to understand hierarchical relationships and dissimilarity levels between clusters.
- Spotting Patterns: Identify consistent color patterns, revealing similarities or differences in data behavior.
- Comparing Heatmaps: If using multiple distance metrics, compare heatmaps to gain insights into data characteristics.
- Applying Domain Knowledge: Utilize domain-specific expertise to decipher biological or contextual significance, especially in fields like gene expression analysis.
Creating Heatmaps with Hierarchical Clustering
Before diving into our actual topic, let’s have an understanding of Heatmaps and Hierarchical Clustering.
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