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.
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Stem Cell Subtypes Predictor
Identify stem cell subtypes — Pluripotent, Multipotent, or Unipotent — from single-cell stem cell transcriptomics data. -
Deep Learning-based Deconvoluter
Deconvolute bulk RNA-seq data into meaningful components using cutting-edge deep learning techniques.
Install ACSCeND using pip:
pip install ACSCeND
Comprehensive documentation is available at:
ACSCeND Documentation
from ACSCeND import Predictor # Example usage predictor = Predictor() subtypes = predictor(input_data)
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.
We welcome issues! If you find any bugs or have problems when you are using ACSCeND, feel free to raise issues.
@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年11月26日.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} }