Creating Heatmaps and Clustering in R

Explore cluster statistics for the eclust method by comparing within- and between-cluster similarities using t-tests, and report min, max, mean, and standard deviation with p-values for five clusters.

Learn to normalize a data matrix to 0–1 and customize heatmaps in R using the heatmap command, including color palettes, labels, margins, and various clustering methods.

Run matrix normalization and generate a heatmap in R, converting data to a 0–1 scale, apply a white–yellow–orange–red palette, cluster with Ward D, and save as a high-resolution jpeg.

Explore statistics describing heatmap-derived clusters, compute p values and t-tests to assess significance, and generate cluster statistics for meaningful biological groupings in R.

Explore how the pheatmap command generates heatmaps from an M-by-N expression matrix, and learn how color panels, legends, row or column scaling, and zero-centered normalization shape the visualization.

Learn how to run the pheatmap command on a gene expression matrix, visualize heat maps with gene-centered normalization, and compare scaling options to interpret clustering and sample relationships.

Load the cluster libraries, run PAM clustering with seven clusters on the data matrix, and visualize results with oval and convex-hull plots using color coding to interpret cluster membership.