In this module, explore the METABRIC breast cancer dataset, focusing on visualizing gene expression and clinical data to uncover potential patterns and relationships.

This module will introduce you to loading and manipulating the data from the breast cancer METABRIC dataset, visualizing and working with gene expression measurements, and building predictive models based on the expression of many different genes (and clinical data too).

In the earlier module, Breast_Cancer_Expression_Data, we examined the mRNA levels for 18,351 genes across 1,082 breast cancer patients. In another activity, Heatmaps, we worked with the smaller data set `mtcar` and saw how the function `heatmap()` can reorder objects in our data set to reveal patterns in the data: Objects and features in the same clusters are more similar to each other than to those in other clusters. Here, we apply these concepts and skills to the TCGA clinical and gene expression features.

We will load and examine a data frame that contains clinical information from over 1,000 breast cancer patients from The Cancer Genome Atlas (TCGA).

Heatmaps are a way to colorize, visualize, and organize a data set with the goal of intuiting relationships among observations and features. We will use heatmaps in this course to find patterns in the gene expression data for the 1K breast cancer patients from The Cancer Genome Atlas. Here, we focus on what heatmaps are and how to create them by practicing with a small dataset.

We work with data from experiments with human cancer cell lines from the Physical Sciences in Oncology (PS-ON) Cell Line Characterization Study. Cancer cell lines are cancer cells that keep dividing and growing over time, under certain conditions in a laboratory. Cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. The PS-ON Study includes imaging- and microscopy-based measurements of physical properties of the cells, such as morphology (shape) and motility (movement). We will examine: -- the expression levels of genes, and -- how fast the cells move.

We will load and examine a data frame that contains clinical information from over 1,000 breast cancer patients from The Cancer Genome Atlas (TCGA).