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-0.4.1.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-0.4.1-py3-none-any.whl (27.6 kB view details)

Uploaded Python 3

File details

Details for the file summarizedpy-0.4.1.tar.gz.

File metadata

  • Download URL: summarizedpy-0.4.1.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-0.4.1.tar.gz
Algorithm Hash digest
SHA256 29818b183bedd6b8d312918fa3c368d7aa98842a3cb63223845bb34c544038ca
MD5 52c807e6737b546dc640b4ab5e6b31e3
BLAKE2b-256 ce7f596268c4e568ee7ddf338bb86e3b7f9ef5e49e32991d3c884626e95f6c4f

See more details on using hashes here.

File details

Details for the file summarizedpy-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: summarizedpy-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 27.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for summarizedpy-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 27aeb1ba4cb0295bf24dde0202f274b8c193fdecfb5c835720a8abc708303245
MD5 7ffc85fe047d1b2aebdd46dcdd35beef
BLAKE2b-256 761eb09b027ccad09d6c1c23d4326ead553293d7d54daa415452b90d0dd9dca8

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