deconomix 1.0.0

Provides methods for cellular composition, hidden background and gene regulation estimation of omics bulk mixtures.

Deconomix

Overview

Deconomix is a Python library aimed at the bioinformatics community, offering methods to estimate cell type compositions, hidden background contributions and gene regulation factors of bulk RNA mixtures. Visit the documentation here.

Features

• Data Simulation: Generate artificial bulk mixtures from single-cell data in an efficient way to provide training data for your models.

• Gene Weighting: Learn gene weights from artifical bulk mixtures to optimize the cellular composition estimation of real bulk RNA mixtures.

• Cellular Composition: Estimate the cellular composition of your bulk RNA profiles or Spatial Transcriptomics spots.

• Background Estimation: Refine the composition estimation by estimate a hidden background contribution and profile, which cannot be explained by the cell types featured in the reference.

• Gene Regulation: Find out, how cell types in your bulk data is regulated in relation to your reference profiles, for instance in a disease context.

• Visualization: Visualize your results with predefined functions.

• Evaluation: Perform basic enrichment analysis for the estimated gene regulatory factors.

Installation

You can install the package using pip:

pip install deconomix

Or directly from the git repository:

pip install git+https://gitlab.gwdg.de/MedBioinf/MedicalDataScience/DeconomiX.git

Getting Started

For a detailed showcase of the standard workflow please visit our gitlab page and navigate to the example folder. There we provide a jupyter notebook with all neccesary steps to get started. We also encourage the user to take a look at our documentation.

Publications

Görtler, F. et al. (2020). Loss-Function Learning for Digital Tissue Deconvolution. Journal of Computational Biology, 27(3), 342–355. Görtler, F. et al. (2024). Adaptive digital tissue deconvolution. Bioinformatics, 40(Supplement 1), i100–i109 Mensching-Buhr, M. and Sterr T. et al. (2024) bioRxiv 2024.11.28.625894; doi: https://doi.org/10.1101/2024.11.28.625894 (Preprint)