My project builds upon the study referenced below. I have proposed a potential follow-up experiment to extend the study’s findings. The project uses simulated data, structured and analyzed as if collected from a real experiment. The results are not scientific evidence; they demonstrate my skills in biological questioning, statistics, experimental design, and programming.

Primera actividad publicada en R de Métodos para el Análisis Económico del grupo teórico #1, "Ejemplos prácticos: Clase 1".

Assignment 5

Cancer datasets often contain thousands of measurements for each sample. For example, a single cell line or patient tumor may have expression measurements for thousands of genes. That creates an important problem: How can we see patterns in data with thousands of dimensions? Principal Component Analysis, or **PCA**, is one way to reduce a large dataset to a smaller number of summary variables while keeping as much of the original information as possible. In this activity, we will use PCA to explore the NCI-60 cancer cell line dataset.

An End-to-End Pipeline with Coarse-to-Fine Tuning and Meta-Ensembling By Jay Prakash (iamjai@live.com)

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?

Scatterplot with tooltips