In the previous heatmap activity, we used the small built-in `mtcars` dataset. That was useful because the dataset was small enough to see clearly. Now we will use real gene expression data from TCGA breast cancer samples. Gene expression data which tells us how many messenger RNAs (mRNAs) per gene are present in a patient sample. The amount of a gene's mRNA corresponds (roughly) to the amount of protein in the sample. This is more realistic, but also more challenging: - there are many genes - there are many patient samples - the data are noisy - not every plot gives a perfect, simple answer That is normal in real computational biology. Our goal is to use heatmaps to ask: Do breast tumors with similar gene expression patterns also share clinical features, such as estrogen receptor status?

Studiul este focalizat pe **analiza descriptiva** a programelor de master oferite de **FEAA** din cadrul **Universitatii Alexandrul Ioan Cuza** din Iasi in anul 2026 sesiunea iulie

Part 1 of Module 3 Workshop 4

This workbook uses R to explore real breast cancer clinical data from TCGA. You will practice viewing patient details, analyzing key features like age and receptor status, handling messy or missing data, and creating plots to spot trends like how cancer stage relates to lymph node spread.

Analisis de una cesta de la compra del sector seguros (aleatoria) de 30 productos y 1000 transacciones