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 the associated clinical data. The METABRIC dataset contains more patient survivial information than we had with the TCGA data.

In this activity, we will consider data on colon cancer cell lines. 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.

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 R dataframe objects that contain data from over 1,000 breast cancer (BRCA) patients from The Cancer Genome Atlas (TCGA).

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). TCGA characterized over 20,000 cancer samples spanning 33 cancer types with genomics. In the next few activities, we will examine some of the different data types and the computational analyses that were performed to decipher breast cancer data from TCGA.