# Corto Metabolomics Analysis Pipeline

A Python implementation of the corto algorithm for analyzing metabolomics and gene expression data, translated from the original R codebase. This project provides tools for preprocessing multi-omics data and performing network analysis to identify relationships between metabolites and gene expression.

## Background

The original corto algorithm was implemented in R for analyzing gene expression data and identifying master regulators. This project extends and modernizes the implementation by:

1. Translating core functionality to Python
2. Adding support for metabolomics data
3. Implementing memory-efficient processing for large datasets
4. Adding parallel processing capabilities
5. Providing a robust command-line interface

## Code Translation Overview

### Original R Components:

The project translates code from several R source files:
- `corto.R`: Core algorithm implementation
- `functions.R`: Utility functions and statistical analysis
- `mra.R`: Master Regulator Analysis functionality
- `gsea.R`: Gene Set Enrichment Analysis components

### Python Implementation:

The functionality has been reorganized into two main Python scripts:

1. `corto-data-prep-final.py`:
- Data loading and validation
- Preprocessing pipeline
- CNV correction
- Quality control metrics

2. `corto-matrix-combination-final.py`:
- Network analysis implementation
- Correlation calculations
- Bootstrap analysis
- Results generation

## Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/corto-metabolomics.git

# Install required packages
pip install -r requirements.txt
```

## Usage

### Data Preparation

```bash
python corto-data-prep-final.py \
    --metabolomics_file data/metabolomics.csv \
    --expression_file data/expression.txt \
    --cnv_file data/cnv.csv \
    --normalization standard \
    --outlier_detection zscore \
    --imputation knn
```

### Network Analysis

```bash
python corto-matrix-combination-final.py \
    --mode corto \
    --expression_file prepared_expression.csv \
    --metabolomics_file prepared_metabolomics.csv \
    --p_threshold 1e-30 \
    --nbootstraps 100 \
    --nthreads 4 \
    --verbose
```

## Key Features

### Data Preprocessing
- Zero-variance feature removal
- CNV correction
- Outlier detection
- Missing value imputation
- Sample alignment
- Quality control metrics

### Network Analysis
- Two analysis modes:
  - 'corto': Original approach keeping matrices separate
  - 'combined': Matrix combination approach for higher-order relationships
- Parallel processing for bootstraps
- Memory-efficient chunked processing
- Comprehensive result reporting

## Output Files

The pipeline generates several output files:

1. Preprocessed Data:
- `prepared_metabolomics.csv`
- `prepared_expression.csv`
- `prepared_metrics.txt`

2. Network Analysis:
- `corto_network_{mode}.csv`: Network edges and statistics
- `corto_regulon_{mode}.txt`: Regulon object with relationship details