The identification of differentially expressed genes (DEGs) is a critical step in understanding the molecular mechanisms underlying various biological conditions and diseases. The limma (Linear Models for Microarray Data) package in R is a widely used tool for this purpose, offering a robust statistical framework for analyzing gene expression data. This process involves several key steps: preprocessing the data, including normalization to adjust for technical variability; constructing a design matrix to model the experimental conditions; fitting linear models to the expression data; and applying empirical Bayes methods to moderate the estimates of variance. The final output includes lists of DEGs with associated statistics, such as fold changes and adjusted p-values, which are used to infer biological significance. Limma's flexibility and statistical rigor make it an invaluable tool for researchers exploring gene expression changes across different conditions or treatments.

Healthcare Data Analysis

Esmeralda Morales

glm(EmotionalFunction1 ~ Gender + ADL+ BMI +Age+ EducationLevel1 + MainIncomeSource1 + FoodChopper + NowLivingType1 + SpouseAsNursing + ChildrenAndAsNursing + BabysitterAndAsNursing + group

Lecture taken for SGPGI

Make a simple map.