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src=\"https://raw.githubusercontent.com/SebastianDHA/DEPy/main/docs/images/depy_banner.svg\"\u003e\n\nA differential expression analysis package for bulk proteomics (and metabolomics) data, which leverages transcriptomics tools.\nInspired by R tools like DEP and SummarizedExperiment, it brings the power of Bioconductor to Python.\nAll you need is a matrix of features and their intensity values.\n\n* PyPI package: https://pypi.org/project/summarizedpy/\n* GitHub: [SebastianDHA/DEPy](https://github.com/SebastianDHA/DEPy)\n* Free software: MIT License\n\n## Features\n\n* SummarizedPY: A container for your -omics data, much like SummarizedExperiment or DEP in R.\n* Filtering and subsetting your samples and features\n* Missing value filtering\n* Imputation using ImputeLCMD (many methods)\n* Transforming (log, centering, standardizing, vsn)\n* Leverage surrogate variable analysis (sva) to adjust for latent batch effects\n* Use the flexibility and power of limma-trend to improve your DEA results and accommodate mixed effects\n* Limma arrayWeights to adjust variable sample quality (often an issue in human and animal datasets)\n* Visualize your DEA results with elegant volcano plots\n* Highly-variable feature selection\n* PCA plots\n* Saving \u0026 loading SummarizedPy objects to \u0026 from disk\n\n## Installation\n### conda\nThis is the best way to install DEPy.\n```Sh\nconda env create -f environment.yml\n```\nNote that DEPy (summarizedpy) must be run within the [depy conda environment](environment.yml) or a cloned version of it.\nThis is because summarizedpy needs an isolated environment to run R in due to the complex loading behavior of Bioconductor packages.\n\n## Using pip\n```Sh\npip install summarizedpy\n```\n\n## Quick start\n```Py\nimport depy as dp\n\nsp = dp.SummarizedPy()\nsp = sp.import_from_delim_file(path=\"path/to/pgroup.tsv\", delim=\"\\t\")\n```\nSee the full [tutorial](docs/usage.md) for more.\n\n## Documentation\n- [GitHub pages](https://sebastiandha.github.io/DEPy/)\n- [ReadTheDocs](https://depy.readthedocs.io/en/latest/)\n\n## Citation\nDEPy and its theoretical foundations are described in the following paper:\n```\nDohm-Hansen, S., et al. (2026).\nExpanding the Proteomics and Metabolomics Toolkit with Methods for Differential Expression Analysis from Transcriptomics.\nJournal of Proteome Research Article ASAP.\nhttps://doi.org/10.1021/acs.jproteome.5c00719\n```\n\n## Credits\nThis package leverages amazing packages from the R and Bioconductor community, including [limma](https://bioconductor.org/packages/3.20/bioc/html/limma.html), [vsn](https://bioconductor.org/packages/release/bioc/html/vsn.html), [sva](https://bioconductor.org/packages/release/bioc/html/sva.html), [ImputeLCMD](https://cran.r-project.org/package=imputeLCMD), and [Tidyverse](https://www.tidyverse.org/).\nThis package was created with [Cookiecutter](https://github.com/audreyfeldroy/cookiecutter) and the [audreyfeldroy/cookiecutter-pypackage](https://github.com/audreyfeldroy/cookiecutter-pypackage) project template.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebastiandha%2Fdepy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsebastiandha%2Fdepy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebastiandha%2Fdepy/lists"}