Utilizing the METABRIC breast cancer dataset to explore how gene expression data and clinical variables can be used to predict patient outcomes, such as time-to-death.

Differential expression analysis of SW480 and SW620 colon cancer cell lines from the PS-ON study using R. Focuses on gene expression, cell motility, and functional annotation to explore tumor progression and metastasis.

This analysis explores gene expression and motility data from the Physical Sciences in Oncology Cell Line Characterization Study, focusing on two breast cancer cell lines: MDA-MB-231 and T-47D. Using R, we perform exploratory data analysis, including expression matrix transformations, differential gene expression profiling, and biological interpretation. The goal is to understand how differences in gene expression relate to cell motility and cancer aggressiveness.

Gaining a clearer understanding of gene expression variability through utilizing boxplots to summarize the distribution of expression values for selected genes. Initially, the first 10 genes from the original expression matrix were examined, followed by the 10 most variable genes identified after reordering the matrix by variance. In accordance with common practices in gene expression analysis, we used a red-blue ("RdBu") color palette to enhance interpretability of heatmaps. To investigate potential associations between estrogen receptor (ER) status and expression patterns, we annotated samples in the reordered heatmap: samples with ER-positive status were labeled with a "+", while ER-negative samples were labeled with a ".".

Investigate the role of estrogen receptors in breast cancer by analyzing different features in the TCGA breast cancer cohort. Breast cancer cells are classified as estrogen receptor-positive (ER+) or estrogen receptor-negative (ER-) based on the presence of estrogen receptors, which can stimulate tumor growth when bound by estrogen.