This activity uses real clinical data from breast cancer patients in The Cancer Genome Atlas (TCGA) to explore how patient information can be analyzed and interpreted. Students examine the types of clinical data collected, summarize large datasets, identify missing or uncertain values, and look for patterns in patient and tumor characteristics. The activity also introduces the role of data analysis in biomedical research and provides a foundation for later connecting clinical information with gene expression data to better understand breast cancer.

Big idea: data can hide patterns Large biological datasets are often too complex to understand by simply 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 answer questions such as: Which samples look similar to each other? Which genes behave similarly across patients? Can visual patterns help us identify tumor subtypes? Today, we will learn this same concept using a small practice dataset that comes with R. Main idea: A heatmap turns numbers into colors, making hidden patterns and relationships easier to see.