Skip to main content

Integrating transcriptional data to decipher the tumor microenvironment with the graph frequency domain model

Project description

Overview

CytoBulk aims to Integrate transcriptional and image data to depict the tumor microenvironment accurately with the graph frequency domain model

Documentation

To install and use CytoBulk, please visit https://kristaxying.github.io/CytoBulk/

System requirements

We have tested the package on the following systems:

  • Linux: Ubuntu 20 (GPU 3080)
  • Windows: Windows 11 Enterprise (CPU)

Installation Guide

Follow the steps below to install and set up CytoBulk.


Setting Up the Environment for Python and R

The CytoBulk package is developed based on the pytorch framework and can be implemented on both GPU and CPU. We recommend running the package on GPU. Please ensure that pytorch and CUDNN are installed correctly.

Option 1: Set Python and R Together

conda config --append channels conda-forge
conda create --name cytobulk python=3.10 r-base=4.4
conda activate cytobulk
pip install cytobulk

This approach is suitable for users who want all dependencies managed within the same Conda environment. However, it might not work reliably on Windows due to potential issues with R configuration in Conda.

Option 2: Set Only Python and Specify R Path Separately

conda create --name cytobulk python=3.10
conda activate cytobulk
pip install cytobulk

Then, before running the main program, you need to specify the path to your locally installed R. This can be done using Python by setting the R_HOME environment variable. Add the following lines at the beginning of your Python script:

import os
# Set the R installation path (adjust the path based on your R installation)
os.environ['R_HOME'] = r_path

Install required R packages

To run CytoBulk, make sure all the following prerequisites are installed.

R 4.4.0 or higher and the following packages

Run demo

Please visit Examples section at https://kristaxying.github.io/CytoBulk/.

Maintainer

WANG Xueying xywang85-c@my.cityu.edu.hk

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cytobulk-0.1.19.tar.gz (59.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

CytoBulk-0.1.19-py3-none-any.whl (65.7 kB view details)

Uploaded Python 3

File details

Details for the file cytobulk-0.1.19.tar.gz.

File metadata

  • Download URL: cytobulk-0.1.19.tar.gz
  • Upload date:
  • Size: 59.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.10.12

File hashes

Hashes for cytobulk-0.1.19.tar.gz
Algorithm Hash digest
SHA256 c13c0d6560ecf9a70ca5f9797d625d85b0bc95cf166f88ff569fc6652e2a4129
MD5 a47c9a51be24fb373a8c0762451645c4
BLAKE2b-256 2302024f80023faea25588adb791114c12d579210e5e04053aab11cb9f302db4

See more details on using hashes here.

File details

Details for the file CytoBulk-0.1.19-py3-none-any.whl.

File metadata

  • Download URL: CytoBulk-0.1.19-py3-none-any.whl
  • Upload date:
  • Size: 65.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.10.12

File hashes

Hashes for CytoBulk-0.1.19-py3-none-any.whl
Algorithm Hash digest
SHA256 ca568c8c934464770e99f0721cfad791a19b7b23f74b22b02f5a1893487553b7
MD5 090794ba9646b0b42d664d6987564653
BLAKE2b-256 d855136485fe4e5cbaa85ace182d37871d0cad1ab5788926dafe4724803f7ee3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page