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 compare two breast cancer cell lines: - **T-47D** - **MDA-MB-231** We will ask: > Do differences in cell movement correspond to differences in gene expression? This is a core idea in systems biology: we connect a measurable behavior, or **phenotype**, to molecular data.

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. That creates an important problem: How can we see patterns in data with thousands of dimensions? Principal Component Analysis, or **PCA**, is one way to reduce a large dataset to a smaller number of summary variables while keeping as much of the original information as possible. In this activity, we will use PCA to explore the NCI-60 cancer cell line dataset.

In the previous heatmap activity, we used the small built-in `mtcars` dataset. That was useful because the dataset was small enough to see clearly. 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. This is more realistic, but also more challenging: - there are many genes - there are many patient samples - the data are noisy - not every plot gives a perfect, simple answer That is normal in real computational biology. Our goal is to use heatmaps to ask: Do breast tumors with similar gene expression patterns also share clinical features, such as estrogen receptor status?

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.