TRC Analysis Pipeline

Overview

This repository contains a professional bioinformatics pipeline for the analysis and integration of Transcriptomics (TRC), regular RNA-Seq (TPM), and Proteomics data. The pipeline encompasses differential expression analysis, correlation analyses between multi-omics layers, and time-shift/half-life analysis, primarily applied to time-course experimental data.

Project Structure

A clean, modular architecture allows for reproducible runs:

analysis_trc/
├── analysis_trc.Rproj   # RStudio project file
├── config.R             # Global paths and experimental parameters
├── utils.R              # Common helper functions
├── environment.yml      # Conda environment definition for reproducibility
├── README.md            # Project documentation
├── LICENSE              # MIT License
├── pipeline/            # Core analysis pipeline scripts
│   ├── 01_DESeq2_RNA_Seq.R
│   ├── 02_Limma_DEG_Analysis.R
│   ├── 03_Correlation_TPM_vs_Protein.R
│   ├── 04_Correlation_TRC_vs_Protein.R
│   ├── 05_Compare_Correlations.R
│   ├── 06_Correlation_With_Shifts.R
│   └── 07_Time_Shift_Analysis.R
├── analyses/                      # Specialized and auxiliary analyses modules
├── data/                          # [Ignored] Raw and processed datasets
├── results/                       # [Ignored] Analysis outputs (TSV, RDA)
├── figures/                       # [Ignored] Output figures (PDF, PNG)
└── archive/                       # Historical script versions

Installation & Setup

We highly recommend using Conda to manage your dependencies to ensure reproducibility.

1. Clone the repository:

   git clone https://github.com/jochotecoa/analysis_trc.git
   cd analysis_trc

2. Create the Conda environment: We have provided an environment.yml to automatically install R, Bioconductor packages, and CRAN dependencies.

   conda env create -f environment.yml

3. Activate the environment:

   conda activate analysis_trc

Configuration

Before running the pipeline, update config.R with the correct paths for your local machine or high-performance computing (HPC) environment. You need to adjust variables like:

• BASE_ANALYSIS_PATH
• BASE_NGS_PATH
• PROTEOMICS_BASE_PATH
• SALMON_INPUT_PATH
• TRC_OUTPUT_PATH

Pipeline Execution

The scripts are designed to be run sequentially from 01 to 07.

1. Differential Expression Analysis
   • Run pipeline/01_DESeq2_RNA_Seq.R and pipeline/02_Limma_DEG_Analysis.R to compute differentially expressed genes/transcripts.
2. Correlation Profiling
   • Run 03 and 04 scripts in the pipeline/ directory to measure basic correlation coefficients between RNA layers and Proteome layers.
3. Advanced Time-Shift Modeling
   • Run 05, 06, and 07 in pipeline/ to analyze how long it takes for a transcriptomic shift to reflect at the proteomic level, integrating half-life calculations.

(You can run them in RStudio via analysis_trc.Rproj or from the terminal using Rscript pipeline/01_DESeq2_RNA_Seq.R, etc.)

Output

All generated tables, lists of DEGs/DEPs, and intermediate .Rda files are saved into a specified results/ folder (configured in config.R). All visualizations and plots are placed into figures/. (Note: These folders are ignored by git to keep the repository lightweight).