Summary
This video provides a comprehensive introduction to hierarchical clustering and heat maps, focusing on the representation of genes and the ordering of rows based on similarity. It explains the process of clustering samples by identifying similarities between genes and arranging them accordingly. The calculation of gene similarity using Euclidean distance is demonstrated through an example, highlighting the importance of distance measures like Manhattan distance. This tutorial is a valuable resource for understanding how to visualize and analyze gene expression data through clustering and heat maps.
Introduction to Hierarchical Clustering and Heat Maps
Introduction to hierarchical clustering and heat maps, explaining the representation of different genes in heat maps and the ordering of rows based on similarity.
Clustering Samples in Heat Maps
Explaining the process of clustering samples in heat maps by identifying similarities between genes and clustering them accordingly.
Calculating Gene Similarity
Detailing the calculation of gene similarity using Euclidean distance, illustrated with an example of two samples and the Euclidean distance formula.
Different Methods of Distance Calculation
Discussing various methods of distance calculation in clustering, including Manhattan distance and the implications of choosing a specific distance measure.
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