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. Learning goals By the end of this activity, you should be able to: - load cell line motility and gene expression data - inspect data frames and matrices - subset a data frame using biological criteria - match sample IDs across two datasets - calculate log-transformed expression values - calculate differential gene expression between two cell lines - interpret genes in the tails of a distribution - connect gene expression patterns to cell behavior

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?

R is a programming language and free software environment for statistical computing and graphics. It’s not only a powerful statistical programming language but also the go-to data analysis tool for many computational genomics experts. We will explore how high-dimensional genomics datasets can be analyzed with core R packages and functions.

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? Later in DREAM-High, we will connect this type of clinical information to **gene expression data**. That is where computational biology becomes especially powerful: we can ask how molecular patterns relate to patient and tumor characteristics.

A hands-on coding activity that introduces how to use heat maps to find hidden structures in data. This demonstrates how turning numbers into colors allows us to visualize large datasets and identify groups that behave similarly.