README.md

GRN_FDR

Overview

This package provides tools for preprocessing gene expression data, computing gene regulatory networks (GRNs), calculating distance matrices, clustering genes, and performing False Discovery Rate (FDR) calculations.

Installation

To install the package, you can use pip:

pip install -r requirements.txt

Make sure you have Python version 3.12.2 installed.

Requirements

Here are the required packages for this project:

python==3.12.2
pandas>=1.0
numpy>=1.18
dask[distributed]>=2022.02.0
scipy>=1.5
scanpy>=1.8.0
pybiomart>=0.2.6
arboreto>=0.1.0
statsmodels>=0.12.0
scikit-learn>=0.24.0

Usage

Below are some examples of how to use the package's functionalities:

Preprocessing Data

This section shows how to preprocess the expression matrix.

import pandas as pd
from preprocessing import preprocess_data

# Load or define an expression matrix
expression_matrix = pd.DataFrame({
    'Gene1': [0, 1, 3, 0, 2],
    'Gene2': [5, 2, 0, 0, 3],
    'Gene3': [0, 0, 0, 1, 0]
})

# Preprocess the expression matrix
preprocessed_matrix = preprocess_data(expression_matrix)
print(preprocessed_matrix)

Computing Gene Regulatory Networks (GRNs)

This section shows how to compute GRNs from the preprocessed expression matrix.

import pandas as pd
from grn_computation import compute_grn

# Load preprocessed expression matrix and TF names
expression_matrix = pd.read_csv('preprocessed_expression_matrix.csv')
tf_names_file = 'genenametfs.tsv'
output_dir = '/path/to/output/directory'

# Compute the GRN
grn_df = compute_grn(expression_matrix, tf_names_file, output_dir)
print(grn_df.head())

Calculating Distance Matrices

This section demonstrates how to compute distance matrices using Wasserstein distances.

import pandas as pd
from distance_matrix import compute_wasserstein_distances_rna_hexa_split

# Load the filtered expression matrix
expression_matrix = pd.read_csv('filtered_expression_matrix.csv')

# Compute the Wasserstein distance matrix
distance_matrix_df = compute_wasserstein_distances_rna_hexa_split(
    expression_matrix, batch_size=5000, n_workers=10, memory_limit='150GB'
)
print(distance_matrix_df.head())
print(f"Computation Time: {computation_time} seconds")

Clustering Genes

This section explains how to cluster genes based on the expression data.

import pandas as pd
from clustering import cluster_genes

# Preprocessed expression data
expression_matrix = pd.DataFrame(...)  # Replace with your data
tf_names_file_path = '/path/to/tfnames.tsv'
output_dir = '/path/to/output'

# Cluster genes
hclust_gene_mapping, clusters, total_time = cluster_genes(expression_matrix, tf_names_file_path, output_dir)

FDR Calculation

This section covers different methods for calculating False Discovery Rates (FDR).

Classical Method

import pandas as pd
from grn_computation import compute_grn
from fdr_calculation import classical_fdr

# Load preprocessed expression matrix and TF names
expression_matrix = pd.read_csv('preprocessed_expression_matrix.csv')
tf_names_file = 'genenametfs.tsv'
output_dir = '/path/to/output/directory'

# Compute the GRN
grn = compute_grn(expression_matrix, tf_names_file, output_dir)

# Perform classical FDR calculation
final_grn = classical_fdr(expression_matrix, tf_names_file, grn, output_dir)
print(final_grn.head())

Centroid Method

import pandas as pd
from grn_computation import compute_grn
from fdr_calculation import fdr_centroid

# Load preprocessed expression matrix and TF names
expression_matrix = pd.read_csv('preprocessed_expression_matrix.csv')
tf_names_file = 'genenametfs.tsv'
output_dir = '/path/to/output/directory'

# Compute the GRN
grn = compute_grn(expression_matrix, tf_names_file, output_dir)

# Perform FDR calculation using the centroid method
final_grn = fdr_centroid(expression_matrix, tf_names_file, grn, output_dir)
print(final_grn.head())

Rotation Method

import pandas as pd
from grn_computation import compute_grn
from fdr_calculation import fdr_rotation;

# Load preprocessed expression matrix and TF names
expression_matrix = pd.read_csv('preprocessed_expression_matrix.csv')
tf_names_file = 'genenametfs.tsv'
output_dir = '/path/to/output/directory';

# Compute the GRN
grn = compute_grn(expression_matrix, tf_names_file, output_dir);

# Perform FDR calculation using the rotation method
final_grn = fdr_rotation(expression_matrix, tf_names_file, grn, output_dir);
print(final_grn.head());

Contributing

Contributions are welcome! If you would like to contribute to this package, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

Contact

For any inquiries or feedback, please contact:

• Souptik Sen - souptik.sen@fau.de
• GitHub: My GitHub Profile