Gene Expression Cancer RNA-Seq Data Set

I use unsupervised learning methods (PCA, hierarchical clustering, and k-means clustering) to subgroup 801 cancer samples into one of 5 clusters based on their gene expression profiles, 20, 531 gene expression measurements.

Code: cancerRNA_markdown.Rmd

Project Presentation: cancerRNA_clustering.pdf

Data Set

Source: UCI Machine learning repository.

There are 801 cancer samples, 5 types of cancer, each sample has 20,531 gene expression measurements as its features.