summarizedpy 0.1.0.post1

A differential expression analysis package for proteomics data, which leverages transcriptomics tools.

DEPy

A differential expression analysis package for bulk proteomics (and metabolomics) data, which leverages transcriptomics tools. Bringing the power of R packages like DEP to Python. All you need is a matrix of features and their intensity values.

• PyPI package: https://pypi.org/project/summarizedpy/
• GitHub: SebastianDHA/DEPy
• Free software: MIT License

Features

• SummarizedPY: A container for your -omics data, much like SummarizedExperiment or DEP in R.
• Filtering and subsetting your samples and features
• Missing value filtering
• Imputation using ImputeLCMD (many methods)
• Transforming (log, centering, standardizing, vsn)
• Leverage surrogate variable analysis (sva) to adjust for latent batch effects
• Use the flexibility and power of limma-trend to improve your DEA results and accommodate mixed effects
• Limma arrayWeights to adjust variable sample quality (often an issue in human and animal datasets)
• Visualize your DEA results with elegant volcano plots

Credits

This package leverages amazing packages from the R and Bioconductor community, including limma, vsn, sva, ImputeLCMD, and Tidyverse. This package was created with Cookiecutter and the audreyfeldroy/cookiecutter-pypackage project template.