Hierarchical clustering - gene clustering
The ability to gather genome-wide expression data is a computationally complex task. The human brain with its limitations cannot solve the problem. However, data can be fine-grained to an easily comprehensible level by subdividing the genes into a smaller number of categories and then analyzing them.
The goal of clustering is to subdivide a set of genes in such a way that similar items fall into the same cluster, whereas dissimilar items fall into different clusters. The important questions to be considered are decisions on similarity and usage for the items that have been clustered. Here we shall explore clustering genes and samples using the photoreceptor time series for the two genotypes.
Getting ready
In order to perform Hierarchical clustering, we shall be using a dataset collected on mice.
Step 1 - collecting and describing data
The datasets titled GSE4051_data and GSE4051_design shall be used. These are available in the CSV format titled GSE4051_data...
