We learned some basic R with the R data set `mtcars,` which contains data from Motor Trends Magazine. In this practice, we examined and manipulated a data set that contains physical measurements on carnivores and primates.
In previous modules, we gained familiarity with the TCGA Breast Cancer gene expression and patient data sets. In this activity, we represented the gene expression data matrix as a heatmap and used hierarchical clustering to find groups of samples that display similar gene expression patterns as well as sets of genes that behave similarly across samples. Ultimately, we discovered that the clusters have clinical significance.
Human cancer cell lines are often used as model systems to study cancer. In this activity, we applied many of the R skills we learned in the context of patient data to gene expression and cell speed data from the Physical Sciences in Oncology Cell Line Characterization Study.
In this activity, we determined which genes are most differentially expressed between two breast cancer cell lines: One that is used as a model for hormone receptor positive breast cancer and one that is used as a model for triple-negative breast cancer. Enrichment analysis provides insight into the function of these genes.