UBDS3 2026: R-intro project

Expression data

• Load the data from the file seOnco-UBDS3-2026.rds. It contains log2-expression data for donors, some of whom have cancer, others are healthy controls, and others for whom no diagnosis has yet been made. Among the genes measured, some are oncogenes known to promote the development of cancer. What are the dimensions of your data? How many donors are represented in each donor class? How many oncogenes are there?

• Calculate, for each donor, the log2 fold-change between the oncogenes and other genes. We will call these values ​​onco fold-change, or oncoFC for short. Save these values ​​in the starting object.

• Visualise the distributions of oncoFC by highlighting the patients, healthy controls, and donors without a diagnosis. Interpret your figure.

• Calculate the average, minimum, and maximum values ​​of the oncoFC for healthy donors and patients with cancer. Based on these values, propose a criterion for making a diagnosis.

• Apply the criterion proposed above to donors without a diagnosis and save the results in the starting object. Then display the identifiers and oncoFCs of the newly diagnosed donors (positive and negative diagnoses).

Disparities in medical data

You are going to analyse data from Racial and ethnic disparities in reproductive medicine in the United States: a narrative review of contemporary high-quality evidence published in the American Journal of Obstetrics and Gynecology in January 2025.

The data is available in the file article_dat.csv and has already been partially cleaned. Load and familiarise yourself with the data. We will focus on the following variables:

variable	class	description
journal	character	Journal full name
year	double	Year the article was published
race1	character	Race category that is used as the ‘Referent’ group (*)

(*) if no Referent group, that is presented first in the first table in the article, worded exactly as in the publication including letter case (e.g., capitalization)

• Visualise the number of articles published per year in the different scientific journals.

• Display the number of articles published for the different classes of the variable race1 in descending order. Then visualize the data.

• Identify annotation problems in the race1 variable. We asks you to clean up some of these problems by transforming the text in uppercase with the casefold() function, to remove the commas and replace hyphens with spaces. Repeat then the figure above.

Report

You are asked to answer the questions in a Rmd report. Please ensure its structure and clarity. Make sure it is possible to identify the packages and versions used.