Comp 3182 labs

Differential Gene Expression (DGE) analysis identifies genes with significant expression changes across conditions using RNA-Seq data. This process involves normalization, statistical modeling, and significance testing. Three common R packages—DESeq2, edgeR, and limma-voom—use different approaches to detect differentially expressed genes (DEGs). DESeq2 applies a negative binomial model, edgeR utilizes the TMM method and GLM, and limma-voom transforms count data to log2 scale for linear modeling. Combining DEGs from multiple methods enhances robustness. Further analysis, such as Gene Ontology (GO) and KEGG pathway enrichment, helps interpret biological significance, ensuring reliable transcriptomic insights.

Probabilidad y estadística- Caso 04

Using small-area fair market rent data to explain using R's mutate() function, with a nested round() function.

This project applies the Apriori algorithm on the Instacart dataset to uncover frequent item associations. Using the arules package in R, we analyze transaction data to identify product combinations often purchased together. The findings provide insights for personalized recommendations and targeted marketing. Visualizations of the rules highlight strong associations, helping businesses optimize product placements and promotions.