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 :cite:p:`schuster2021deep,schuster2021manifold` and Prada and coworkers :cite:p:`prada2023n`. 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 :doc:`Installation ` section. Usage ----- The modules of bulkdgd can be imported and used to build customized scripts and pipelines. The :doc:`API reference section ` provides a detailed descriptions of bulkdgd's sub-packages and modules. However, we also provide a small :doc:`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 :doc:`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: * Prada-Luengo, Inigo, et al. "N-of-one differential gene expression without control samples using a deep generative model." *Genome Biology* 24.1 (2023): 263. * 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). References ---------- .. bibliography::