In this activity, we will explore real clinical data from breast cancer patients in The Cancer Genome Atlas (TCGA). The goal is not only to learn R. The goal is to use R to ask scientific questions: - What information is collected from cancer patients? - How do we summarize a large clinical dataset? - What kinds of missing or uncertain values appear in real biomedical data? - How can clinical features help us understand breast cancer subtypes?

Big idea: data can hide patterns Large biological datasets are often too big to understand by reading numbers in a table. In DREAM-High, we will eventually use heatmaps to look for patterns in breast cancer gene expression data from patients in The Cancer Genome Atlas. A heatmap can help us ask questions such as: - Which samples look similar to each other? - Which genes behave similarly across patients? - Can visual patterns help us discover tumor subtypes? Today we will learn the same basic idea using a small practice dataset that comes with R. Main idea: A heatmap turns numbers into colors so that hidden structure becomes easier to see.