In this video, we first discuss how to interpret a hierarchically clustered heat map and then demonstrate how to make it out of a real data spreadsheet.
We also show how to normalize the data and play with various parameters.
Clustered heatmaps are made of two components: heatmaps and hierarchical clusters.
Heatmaps employ color-coded cells to represent the magnitude of a variable (for example gene expression) in terms of a variable on the x-axis and another variable on the y-axis. Typically, the x-axis represents the column names in our datasheet and the y-axis represents the index of our datasheet.
On the other hand, hierarchical clustering algorithms re-arrange the rows and columns of the datasheet, based on some similarity measures, creating hierarchical structures that reveal underlying relationships. Therefore, the rows and columns that are most similar are clustered together and are brought together in the plot.
This visualization method boasts high data density and facilitates the identification of clusters more effectively compared to unordered heatmaps.
Link to CompuFlair Web Application to Visualize Your Data:
apps.compu-flair.com
Негізгі бет Hierarchically Clustered Heat Maps | How to Read Them? | How to Make Them? | No Code | Generative AI
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