Skip to main content

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

Project description

DEPy

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

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
  • Highly-variable feature selection
  • PCA plots
  • Saving & loading SummarizedPy objects to & from disk

Installation

conda

This is the best way to install DEPy.

conda env create -f environment.yml

Note that DEPy (summarizedpy) must be run within the depy conda environment or a cloned version of it. This is because summarizedpy needs an isolated environment to run R in due to the complex loading behavior of Bioconductor packages.

Using pip

pip install summarizedpy

Quick start

import depy as dp

sp = dp.SummarizedPy()
sp = sp.import_from_delim_file(path="path/to/pgroup.tsv", delim="\t")

See the full tutorial for more.

Documentation

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

summarizedpy-0.4.0.tar.gz (2.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

summarizedpy-0.4.0-py3-none-any.whl (27.4 kB view details)

Uploaded Python 3

File details

Details for the file summarizedpy-0.4.0.tar.gz.

File metadata

  • Download URL: summarizedpy-0.4.0.tar.gz
  • Upload date:
  • Size: 2.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for summarizedpy-0.4.0.tar.gz
Algorithm Hash digest
SHA256 cc38bcecf9171f32c45d35a2b6ba5f2d106851b9cab293b81e4b8a5d9b802849
MD5 c6d661e3b4317221207aa877ab47de81
BLAKE2b-256 246e08617dbff0c36847f77588022561269a8ba21f261f85ca80d92407a2eb70

See more details on using hashes here.

File details

Details for the file summarizedpy-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: summarizedpy-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 27.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for summarizedpy-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 883558a42080eb6f60cbfe57f975960581dae6c94a3ad1aaefa285f9a0d2aed3
MD5 02ad677fa9f41054e3ebb9964e739887
BLAKE2b-256 90d7da694c02a4262677c300f463a8d0fd65cec3a6b58bdd21c3c5df59867fb6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page