In this project, we begin the analysis of the gene expression data on trigger point myofascial pain similar to fibromyalgia in clinical signs and symptoms for chronic pain. The genome data was used in a study of 5 healthy and 7 myofascial pain patients that helped the researchers understand how a drug that starts with 'dex' helps with chronic pain. It included fragments per kilo million and counts that were both normalized from the gene high throughput fastp data collected. This study is a stepping stone to connecting major illnesses associated with Epstein-Barr virus (EBV) such as multiple sclerosis, mononucleosis, Hodgkin's disease, and fibromyalgia. We can eventually understand how changes in the body of many people make their body undergo DNA transcription to make more of some genes and less of others when dealing with 58,000 genes in this study. Details in the document.

As of December 26, 2025

9-3-62

Aplicaciones livianas RMD y Shiny

Học ở BVTN

TECNICAS Y HERRAMIENTAS PARA LA VISUALIZACION DE DATOS, C6T1 - Documentos RMD

Ngày 1 (29/12/2025) thực hành trên R Markdown

Before administering a test, we often ask: **Does it work equally well across different groups, or is it biased?** For example, some items might favor students based on **gender**, **ethnicity**, or **language background**. This tutorial extends a basic Classical Test Theory (CTT) item analysis by introducing Differential Item Functioning (DIF) analysis using the Mantel–Haenszel (MH) method. The goal is to detect items that function differently across groups after conditioning on total test score. Item 5 will be artificially manipulated to demonstrate DIF. We are moving from classical item analysis to DIF detection because some items might favor one group over another even if overall ability is the same. MH DIF is a widely used method to detect this bias.