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meld

Quantifying experimental perturbations at single cell resolution

https://github.com/krishnaswamylab/meld

Science Score: 46.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: nature.com, zenodo.org
  • ✓
    Committers with academic emails
    2 of 12 committers (16.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary

Keywords

machine-learning scrna-seq-analysis
Last synced: 7 months ago · JSON representation

Repository

Quantifying experimental perturbations at single cell resolution

Basic Info
  • Host: GitHub
  • Owner: KrishnaswamyLab
  • License: other
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 17.9 MB
Statistics
  • Stars: 109
  • Watchers: 5
  • Forks: 10
  • Open Issues: 11
  • Releases: 6
Topics
machine-learning scrna-seq-analysis
Created over 8 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

MELD

Quantifying the effect of experimental perturbations at single-cell resolution

Latest PyPi version GitHub Actions Coverage Status Read the Docs Article Twitter GitHub stars

Tutorials

For a quick-start tutorial of MELD in Google CoLab, check out this notebook from our Machine Learning Workshop: * MELD Quick Start - Zebrafish data

If you're looking for an in-depth tutorial of MELD and VFC, start here: * Guided tutorial in Python - Zebrafish data.

If you'd like to see how to use MELD without VFC, start here: * Tutorial using MELD without VFC - T cell data.

Introduction

MELD is a Python package for quantifying the effects of experimental perturbations. For an in depth explanation of the algorithm, please read the associated article:

Quantifying the effect of experimental perturbations at single-cell resolution. Daniel B Burkhardt*, Jay S Stanley*, Alexander Tong, Ana Luisa Perdigoto, Scott A Gigante, Kevan C Herold, Guy Wolf, Antonio J Giraldez, David van Dijk, Smita Krishnaswamy. Nature Biotechnology. 2021.

The goal of MELD is to identify populations of cells that are most affected by an experimental perturbation. Rather than clustering the data first and calculating differential abundance of samples within clusters, MELD provides a density estimate for each scRNA-seq sample for every cell in each dataset. Comparing the ratio between the density of each sample provides a quantitative estimate the effect of a perturbation at the single-cell level. We can then identify the cells most or least affected by the perturbation.

You can also watch a seminar explaining MELD given by @dburkhardt: Video

Installation

pip install meld

Requirements

MELD requires Python >= 3.6. All other requirements are installed automatically by pip.

Usage example

``` import numpy as np import meld

# Create toy data nsamples = 500 ndimensions = 100 data = np.random.normal(size=(nsamples, ndimensions)) samplelabels = np.random.choice(['treatment', 'control'], size=nsamples)

# Estimate density of each sample over the graph sampledensities = meld.MELD().fittransform(data, sample_labels)

# Normalize densities to calculate sample likelihoods samplelikelihoods = meld.utils.normalizedensities(sample_densities) ```

Owner

  • Name: Krishnaswamy Lab
  • Login: KrishnaswamyLab
  • Kind: organization
  • Email: smita.krishnaswamy@yale.edu
  • Location: New Haven, CT

GitHub Events

Total
  • Watch event: 5
  • Fork event: 1
Last Year
  • Watch event: 5
  • Fork event: 1

Committers

Last synced: almost 2 years ago

All Time
  • Total Commits: 362
  • Total Committers: 12
  • Avg Commits per committer: 30.167
  • Development Distribution Score (DDS): 0.37
Past Year
  • Commits: 4
  • Committers: 1
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Daniel Burkhardt b****b@g****m 228
Scott Gigante s****e@g****m 69
jay j****y@y****u 14
Jay Stanley j****3@g****m 12
Alex Tong a****v@g****m 11
dvdijk d****k@g****m 10
Alexander Strzalkowski a****i@g****m 8
Daniel Burkhardt d****t@y****u 4
Jay Stanley j****y@p****n 3
dburkhardt d****t@c****m 1
Jay Stanley s****s 1
Scott Gigante s****e 1
Committer Domains (Top 20 + Academic)
yale.edu: 2 cellarity.com: 1 pop-os.localdomain: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 26
  • Total pull requests: 43
  • Average time to close issues: 6 months
  • Average time to close pull requests: 8 days
  • Total issue authors: 21
  • Total pull request authors: 5
  • Average comments per issue: 2.35
  • Average comments per pull request: 0.14
  • Merged pull requests: 39
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • scottgigante (4)
  • nickhir (3)
  • wupeng2 (1)
  • mugpeng (1)
  • bjoaofelipe (1)
  • DonyorM (1)
  • JBreunig (1)
  • dburkhardt (1)
  • paolabc (1)
  • Baboon61 (1)
  • paulstumpges (1)
  • eL-Gene (1)
  • Hrovatin (1)
  • michallipinski (1)
  • GabrielBaldissera (1)
Pull Request Authors
  • dburkhardt (36)
  • scottgigante (4)
  • atong01 (1)
  • stanleyjs (1)
  • bjoaofelipe (1)
Top Labels
Issue Labels
enhancement (2) bug (2) question (1) batch correction (1) FAQ (1) documentation (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 670 last-month
  • Total docker downloads: 98,940
  • Total dependent packages: 2
  • Total dependent repositories: 159
  • Total versions: 9
  • Total maintainers: 1
pypi.org: meld

MELD

  • Versions: 9
  • Dependent Packages: 2
  • Dependent Repositories: 159
  • Downloads: 670 Last month
  • Docker Downloads: 98,940
Rankings
Docker downloads count: 1.1%
Dependent repos count: 1.2%
Dependent packages count: 3.2%
Average: 5.4%
Stargazers count: 7.3%
Downloads: 9.0%
Forks count: 10.5%
Maintainers (1)
dburkhardt
Last synced: 8 months ago

Dependencies

doc/source/requirements.txt pypi
  • future *
  • graphtools >=0.1.8.1
  • numpy >=1.14.0
  • pandas >=0.25
  • scipy >=1.1.0
  • scprep *
  • sphinx >=2.2
  • sphinxcontrib-napoleon *
requirements.txt pypi
  • graphtools >=1.5.0
  • numpy >=1.14.0
  • pandas >=0.25
  • phate >=0.3.0
  • pygsp *
  • scipy >=1.1.0
  • scprep >=1.0
setup.py pypi
  • graphtools >=1.5.0
  • numpy >=1.14.0
  • pandas >=0.25
  • pygsp *
  • scikit-learn *
  • scipy >=1.1.0
  • scprep >=1.0
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  • pypa/gh-action-pypi-publish master composite
.github/workflows/publish-to-test-pypi.yml actions
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  • pypa/gh-action-pypi-publish master composite
.github/workflows/run_tests.yml actions
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  • actions/upload-artifact master composite
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