In previous DREAM-High activities, we studied cancer patient data from TCGA. Here we switch to a different kind of cancer model: **human cancer cell lines**. Cancer cell lines are cells that can grow in the laboratory. Researchers use them to study cancer biology and to test hypotheses about cancer behavior.

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?

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.

Cancer datasets often contain thousands of measurements for each sample. For example, a single cell line or patient tumor may have expression measurements for thousands of genes.

Now we will use real gene expression data from TCGA breast cancer samples. Gene expression data which tells us how many messenger RNAs (mRNAs) per gene are present in a patient sample. The amount of a gene's mRNA corresponds (roughly) to the amount of protein in the sample.

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