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.

Suggestions

We welcome suggestions! If you have any ideas or feedback to improve ACSCeND, please reach out to Shreyansh Priyadarshi.