--- Sample README.md for Bioinformatics Project ---

"""

Project Title: [Descriptive title of your analysis/project]

Overview

This project investigates [brief description of the biological context and goals].

Project Structure

your_project/
├── data/            # raw and processed data
├── scripts/         # analysis scripts (numbered)
├── figures/         # generated plots and visuals
├── results/         # intermediate result files
├── notebooks/       # exploratory Jupyter/RMarkdown notebooks
├── docs/            # documentation and manuscript text
├── README.md        # this file
├── environment.yml  # reproducible environment

Setup Instructions

conda env create -f environment.yml
conda activate EMAdown

Pipeline Summary

1. 01_Preprocess_LuCa_and_AUCell_macs.ipynb – Load and QC the input data. Then, run AUCell for mac subtypes.
2. 02_macs_predictions.R – Create HieFIT cell type prediction model.
3. 03_inspect_ct_composition.R – Inspect cell type compositions across samples.
4. 04_PB-and-DESeq2.ipynb – Perform DEG on pseudo-bulked profiles.
5. 05_PB-DEG_inspect_results.R - Inspect DEG expressions across macrophages.
6. 06_create_signature_matrix.R - Create cell type signature matrix for deconvolution with CiberSortX.
7. 07_prepare_bulk-data.R - Fetch TCGA lung cancer bulk datasets and prepare.
8. 08_deconvolute_bulk-data.R - Estimate macrophage composition of bulk RNAseq data.
9. 09_run_survival_analysis.R - Correlate macrophage compositons with prognosis using Kaplan-Meier survival analysis.

to run sccoda scripts: conda activate sccoda-gpu

Figures

All key plots are saved in figures/. The naming follows the manuscript figure order (e.g., Figure1_PCA.png, Figure2_Volcano.pdf).

Reproducibility

• All scripts use fixed random seeds.
• Software versions are logged via sessionInfo() or pip freeze.

Citation

If you use this project, please cite: [Your paper or bioRxiv link] """