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AI-based Cancer Stem Cell profiler and Neoplasia Deconvoluter (ACSCeND)

ACSCeND is a Python package designed to analyze and process stem cell transcriptomics data. It includes two core modules for predicting stem cell subtypes and deconvoluting bulk RNA-seq data using deep learning.

Features

  1. Stem Cell Subtypes Predictor
    Identify stem cell subtypes — Pluripotent, Multipotent, or Unipotent — from single-cell stem cell transcriptomics data.

  2. Deep Learning-based Deconvoluter
    Deconvolute bulk RNA-seq data into meaningful components using cutting-edge deep learning techniques.

Installation

Install ACSCeND using pip:

pip install ACSCeND

Documentation

Comprehensive documentation is available at:
ACSCeND Documentation

Usage

Stem Cell Subtypes Predictor

from ACSCeND import Predictor

# Example usage
predictor = Predictor()
subtypes = predictor(input_data)

Deep Learning-based Deconvoluter

from ACSCeND import Deconvoluter

# Example usage
real_freq, real_gep = Deconvoluter(pseudo_data, sig_matrix, pseudo_freq, real_data, normalized=False)

For detailed examples and API reference, visit the documentation.

Issues

We welcome issues! If you find any bugs or have problems when you are using ACSCeND, feel free to raise issues.

Citation

@article {ACSCeND,
	author = {Chowdhury*, Debojyoti and Priyadarshi*, Shreyansh and Biswas, Sayan and Neekhra, Bhavesh and Gupta, Debayan and Haldar, Shubhasis},
	title = {Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network},
	elocation-id = {2024.11.26.625418},
	year = {2024},
	doi = {10.1101/2024.11.26.625418},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/12/01/2024.11.26.625418},
	eprint = {https://www.biorxiv.org/content/early/2024/12/01/2024.11.26.625418.full.pdf},
	journal = {bioRxiv}
}