Getting started
bulkDGD is a Python package containing the bulkDGD generative model for the gene expression of human tissues from bulk RNA-Seq data described in the work of Schuster and Krogh [SK21a, SK21b] and Prada and coworkers [PLSL+23].
bulkDGD can be used, for instance, to find differentially expressed genes between normal human samples and diseased samples.
Installation
We provide detailed instructions to install bulkDGD in a Python virtual environment or in a conda environment in the Installation section.
Usage
The modules of bulkDGD can be imported and used to build customized scripts and pipelines.
The API reference section provides a detailed descriptions of bulkDGD’s sub-packages and modules.
However, we also provide a small command-line interface to automate some of the most common tasks for which the DGD model can be used for more bio-oriented audiences.
Tutorials
Our tutorials provide detailed, step-by-step explanations of how to perform different tasks using bulkDGD.
Issues, bugs, and questions
You can report to us any bug, issue, or question about the package by opening an issue in the issues section of our GitHub repository.
Citing
If you use our software for your research, please cite the following articles:
Schuster, Viktoria, and Anders Krogh. “A manifold learning perspective on representation learning: Learning decoder and representations without an encoder.” Entropy 23.11 (2021): 1403.
Schuster, Viktoria, and Anders Krogh. “The deep generative decoder: Using MAP estimates of representations.” arXiv preprint arXiv:2110.06672 (2021).
Prada-Luengo, Inigo, et al. “N-of-one differential gene expression without control samples using a deep generative model.” bioRxiv (2023): 2023-01.
References
Inigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, Thilde Terkelsen, Valentina Sora, and Anders Krogh. N-of-one differential gene expression without control samples using a deep generative model. bioRxiv, pages 2023–01, 2023.
Viktoria Schuster and Anders Krogh. A manifold learning perspective on representation learning: learning decoder and representations without an encoder. Entropy, 23(11):1403, 2021.
Viktoria Schuster and Anders Krogh. The deep generative decoder: using map estimates of representations. arXiv, 2021.