violin plot with ggplot2 to visualize Sepal Length distribution across Iris species.

Here's a two-line description for your RPub: --- This R script visualizes the correlation matrix of numeric variables in a dataset using `ggplot2` and `reshape2`. The function creates a heatmap to easily interpret the strength and direction of correlations between variables.

Stock time series exercise

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.