zahrasattari-hub/volcano_plot_analysis

1. This repository is a single-purpose R analysis project for differential protein abundance in secretome samples, centered on generating a volcano plot from hormone-treated vs untreated mammary epithelial cell cultures.

2. The main executable artifact is Volcano-Plot-Analysis.R, which contains the end-to-end analysis workflow. There is no multi-service or layered application structure; the architecture is a linear data-analysis pipeline.

3. The primary input data is the Excel workbook mer_proteins.xlsx, which contains the proteomics abundance measurements and the sample metadata needed to define treatment groups. That workbook is the external data boundary for the analysis.

4. The workflow begins by importing the protein abundance matrix and using metadata to separate samples into experimental conditions. This is the point where raw experimental data is transformed into analysis-ready inputs.

5. A design matrix is then created to represent the treatment structure. This matrix is the key intermediate artifact that connects the sample metadata to the statistical modeling step.

6. Differential abundance is computed using the limma framework. Protein-wise linear models are fitted, followed by empirical Bayes moderation to stabilize variance estimates across proteins, which is the core statistical engine of the repository.

7. The statistical outputs include log2 fold changes, raw p-values, and multiple-testing adjusted p-values for each detected protein. These results form the main analytical output table that downstream classification and visualization depend on.

8. Proteins are categorized into upregulated, downregulated, or not significantly changed groups using predefined fold-change and significance thresholds. This classification step creates the biological interpretation layer on top of the model results.

9. The visualization subsystem produces a volcano plot, using the differential abundance statistics to place proteins by effect size and significance. The plot is the primary presentation artifact for the repository.

10. A small annotation step labels selected proteins of biological interest directly on the figure, making the plot both quantitative and interpretive rather than purely statistical.

11. The only notable project-level tooling visible here is the R project file volcano_plot_analysis.Rproj, indicating the repository is meant to be opened and run in RStudio or another R-aware IDE. The image reference in the README shows the expected plot output, Rplot.jpeg, as the final deliverable.

12. In architecture terms, the repository is best drawn as: Excel input + metadata → R/limma statistical pipeline → classified differential protein results → annotated volcano plot output. There are no backend APIs, databases, or interactive components beyond the analysis script and generated figure.