Skip to main content

Charting Critical Transient Gene Interactions in Disease Progression from Multi-modal Transcriptomics

Project description

CRISGI

Welcome to the official documentation of Charting CRItical tranSient Gene Interactions (CRISGI) in Disease Progression from Multi-modal Transcriptomics!

🗺️ Overview

Figure1

Critical transitions (CTs) in gene regulatory networks (GRNs) drive pivotal shifts in disease progression. While CT theory holds great promise for early disease detection, existing computational frameworks face major limitations. They rely on unsupervised ranking of CT signals at individual gene or gene-module level, apply unranked gene set enrichment analyses, and depend on manual inspection of signal trends to infer CT presence and onset within a single cohort. Additionally, multimodal transcriptomic data remain underutilized. These approaches limit mechanistic resolution and hinder clinical translation.

We present CRISGI, a novel CT framework designed to overcome these challenges. CRISGI enables phenotype-specific CT gene-gene interaction modeling, CT-rank enrichment analyses, automated CT presence and onset prediction, and supports bulk, single-cell, and spatial transcriptomic (ST) data.

🚀 Getting Started

Want to start using it immediately? Check out the Installation Guide and Usage Guide.

📥 Installation

CRISGI is a Python package that can be installed via pip. You can install it from PyPI or from the source.

Details on how to install CRISGI can be found in the Installation section.

🔧 Usage

CRISGI is designed to be user-friendly and easy to use. The package provides a set of functions that can be used to perform various tasks related to critical transitions in gene interactions. Detailed instructions on how to use CRISGI can be found in the following sections.

📖 Tutorials

Moreover, you can find some tutorials in the Tutorial section. These tutorials will guide you through the process of using CRISGI for your own data analysis.

📚 API Reference

For more detailed information, please refer to the API Reference.

📑 Citation

If you use CRISGI in your research, please cite the following paper:

APA format:

Lyu, C., Jiang, A., Ng, K. H., Liu, X., & Chen, L. (2025). Predicting Early Transitions in Respiratory Virus Infections via Critical Transient Gene Interactions. bioRxiv. https://doi.org/10.1101/2025.04.18.649619

BibTeX format:

@article{crisgi,
  title={Predicting Early Transitions in Respiratory Virus Infections via Critical Transient Gene Interactions},
  author={Lyu, Chengshang and Jiang, Anna and Ng, Ka Ho and Liu, Xiaoyu and Chen, Lingxi},
  journal={bioRxiv},
  year={2025},
  doi={10.1101/2025.04.18.649619},
  publisher={Cold Spring Harbor Laboratory}
}

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

crisgi-0.1.1.tar.gz (20.7 MB view details)

Uploaded Source

Built Distribution

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

crisgi-0.1.1-py3-none-any.whl (20.4 MB view details)

Uploaded Python 3

File details

Details for the file crisgi-0.1.1.tar.gz.

File metadata

  • Download URL: crisgi-0.1.1.tar.gz
  • Upload date:
  • Size: 20.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.11

File hashes

Hashes for crisgi-0.1.1.tar.gz
Algorithm Hash digest
SHA256 d78471f408ee37c69531830f3b72c40b2b062d2adfcc7057d24c783a34953655
MD5 ab5ca9127e673b35753c1d7227b0350f
BLAKE2b-256 726b7c2e4d1b9be7477aeae94d98749a02212dbc753473894d312e019f2583c5

See more details on using hashes here.

File details

Details for the file crisgi-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: crisgi-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 20.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.11

File hashes

Hashes for crisgi-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 1cc5ca731957849189221b732edb4bba4229af0b5388f71c5161c42d0c95e8a8
MD5 63df2ffdc0d94be317fd9e99859bd775
BLAKE2b-256 9ba50a3ab6109f848ae4b819ee6021cefba43bbfc21cb221557ecd6f1f74ece8

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