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Deployment & Documentation & Stats

Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing large datasets presents challenges due to the identification of nonlinear gene–gene associations and the need to process an ever-growing number of gene pairs and their potential network connections. These challenges mean that some experiments are discarded because the techniques do not support these intense workloads. This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments. To do this, pyEnGNet harnesses CPU and multi-GPU parallel computing resources, efficiently handling large datasets. These implementations have optimised memory management and processing, delivering timely results. We have used synthetic datasets to prove the runtime and intensive workload improvements. In addition, pyEnGNet was used in a real-life study of patients after allogeneic stem cell transplantation with invasive aspergillosis and was able to detect biological perspectives in the study.

pyengnet is featured for:

• Unified APIs, detailed documentation, and interactive examples available to the community.
• Complete coverage for reconstruction of massive gene co-expression networks.
• Optimized models to generate results in the shortest possible time.
• Optimization of a High-Performance Computing (HPC) and Big Data ecosystem, using cuda and multiprocess.

API Demo:

import os
from pyengnet.File import File
from pyengnet.Engnet import Engnet

if __name__ == "__main__":

   # Load dataset
   dataset = File.load(path=os.getcwd()+"/datasets/Spellman.csv", separator=",", nmi_th=0.6, spearman_th=0.7, kendall_th=0.7, readded_th=0.7, hub_th = 3)

   # Run pyEnGNet on CPUs
   graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True)

   # Run pyEnGNet on GPU devices
   # graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True, numGpus = 2, computeCapability = 61)

   # Save gene co-expression networks and additional information
   File.saveFile(path='/home/principalpc/Escritorio/graphComplete.csv',graph=infoGraphComplete) # Full network
   File.saveFile(path='/home/principalpc/Escritorio/graphFiltered.csv',graph=infoGraphFiltered) # Filtered network

   # Print gene co-expression networks
   File.showGraph(graph=graphComplete,title='Complete graph') # Full network
   File.showGraph(graph=graphFiltered,title="Filtered graph") # Filtered network

Citing pyEnGNet:

pyEnGNet paper is published in The Journal of Supercomputing. If you use pyEnGNet in a scientific publication, we would appreciate citations to the following paper:

López-Fernández, A., Gómez-Vela, F. A., del Saz-Navarro, M., Delgado-Chaves, F. M., & Rodríguez-Baena, D. S. (2024). Optimized Python library for reconstruction of ensemble-based gene co-expression networks using multi-GPU. The Journal of Supercomputing, 1-35.

Key Links and Resources:

• View the latest codes on Github
• View the documentation & API
• View all examples

It is recommended to use pip for installation. Please make sure the latest version is installed, as pyengnet is updated frequently:

pip install pyengnet            # normal install
pip install --upgrade pyengnet  # or update if needed
pip install --pre pyengnet      # or include pre-release version for new features

Alternatively, you could clone and run setup.py file:

git clone https://github.com/aureliolfdez/pyEnGNet.git
pip install .

Required Dependencies:

• Python>=3.10
• numpy>=1.24.0
• tqdm>=4.64.0
• multiprocess>=0.70.14
• pandas>=1.5.3
• matplotlib>=3.6.3
• networkx>=3.0
• scipy>=1.10.0

• pyengnet.File: Class used to manage file I/O operations and data visualization.
• pyengnet.File.load(): Load dataset from a txt or csv file.
• pyengnet.File.saveFile(): Save network to file (can be used to store full and/or pruned networks)
• pyengnet.File.showGraph(): Display a specific network

• pyengnet.Engnet: Class in charge of controlling the execution of the EnGNet algorithm.
• pyengnet.Engnet.process(): Function that runs the EngNet algorithm. Depending on the parameters of this function, the algorithm will be executed in parallel with CPU processors or GPU devices.
• pyengnet.Kendall: Kendall measurement is coded in a parallel ecosystem with CPUs.
• pyengnet.NMI: NMI measurement is coded in a parallel ecosystem with CPUs.
• pyengnet.Spearman: Spearman measurement is coded in a parallel ecosystem with CPUs.
• pyengnet.src.correlations: Execution of Kendall, NMI, and Spearman measures under a parallel multi-GPU ecosystem (CUDA). In addition, it detects those pairs of genes that exceed the threshold for major voting.

All implemented modes are associated with examples, check "pyEnGNet examples" for more information.

"tests/test_integration/test_cpu.py" demonstrates the basic API for the generation of co-expression gene networks using CPUs.

1. Load gene co-expression dataset from input file

   from pyengnet.File import File
   
   dataset = File.load(path=os.getcwd()+"/datasets/Spellman.csv", separator=",", nmi_th=0.6, spearman_th=0.7, kendall_th=0.7, readded_th=0.7, hub_th = 3)

2. Run pyEnGNet based on CPUs.

   from pyengnet.Engnet import Engnet
   
   graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True)

3. Save gene co-expression networks output (optional)

   from pyengnet.File import File
   
   File.saveFile(path='/home/user/Desktop/graphComplete.csv',graph=infoGraphComplete)
   File.saveFile(path='/home/user/Desktop/graphFiltered.csv',graph=infoGraphFiltered)

4. Print gene co-expression networks output (optional)

   from pyengnet.File import File
   
   File.showGraph(graph=graphComplete,title='Complete graph')
   File.showGraph(graph=graphFiltered,title="Filtered graph")

"tests/test_integration/test_gpu.py" demonstrates the basic API for the generation of co-expression gene networks using GPU devices.

1. Load gene co-expression dataset from input file

   from pyengnet.File import File
   
   dataset = File.load(path=os.getcwd()+"/datasets/Spellman.csv", separator=",", nmi_th=0.6, spearman_th=0.7, kendall_th=0.7, readded_th=0.7, hub_th = 3)

2. Run pyEnGNet based on CPUs.

   from pyengnet.Engnet import Engnet
   
   graphFiltered, infoGraphFiltered, graphComplete, infoGraphComplete = Engnet.process(dataset, saveComplete = True, numGpus = 2, computeCapability = 61)

3. Save gene co-expression networks output (optional)

   from pyengnet.File import File
   
   File.saveFile(path='/home/user/Desktop/graphComplete.csv',graph=infoGraphComplete)
   File.saveFile(path='/home/user/Desktop/graphFiltered.csv',graph=infoGraphFiltered)

4. Print gene co-expression networks output (optional)

   from pyengnet.File import File
   
   File.showGraph(graph=graphComplete,title='Complete graph')
   File.showGraph(graph=graphFiltered,title="Filtered graph")