cosmicqc

Single cell Morphology Quality Control (coSMicQC)

https://github.com/cytomining/cosmicqc

Science Score: 59.0%

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

  • CITATION.cff file
  • codemeta.json file
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  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
    1 of 4 committers (25.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.0%) to scientific vocabulary

Keywords

image-based-profiling microscopy-image-analysis quality-control

Keywords from Contributors

mesh profiles sequences networks microscopy interactive hacking network-simulation
Last synced: 6 months ago · JSON representation

Repository

Single cell Morphology Quality Control (coSMicQC)

Basic Info
Statistics
  • Stars: 8
  • Watchers: 2
  • Forks: 3
  • Open Issues: 19
  • Releases: 15
Topics
image-based-profiling microscopy-image-analysis quality-control
Created almost 2 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Single cell Morphology Quality Control

PyPI - Version Build Status Coverage Status Ruff Poetry Software DOI badge

Navigate the cosmos of single-cell morphology with confidence coSMicQC keeps your data on course!

coSMicQC is a Python package to evaluate converted single-cell morphology outputs from CytoTable.

It can be challenging to get "perfect" single-cell compartment segmentation across large high-throughput screens when performing object detection in CellProfiler (or similar software). Technical artifacts can arise during segmentation, leading to issues such as under-segmentation, over-segmentation, or the erroneous segmentation of background noise, smudges, or bright artifacts.

In single-cell analysis, intriguing phenotypes often emerge by examining morphological differences. However, technical outliers in the data can obscure these biological insights, compromising the validity of the findings.

By utilizing specific morphological features extracted with CellProfiler, particularly AreaShape features, you can identify technically incorrect segmentations. These can then be labeled or removed before further preprocessing steps, such as those performed with pycytominer.

Check out our blog post on this for a greater understanding of the background and how coSMicQC can help you!

Installation

Install coSMicQC from PyPI or from source:

```shell

install from pypi

pip install coSMicQC

install directly from source

pip install git+https://github.com/cytomining/coSMicQC.git ```

Contributing, Development, and Testing

Please see our contributing documentation for more details on contributions, development, and testing.

References

Owner

  • Name: cytomining
  • Login: cytomining
  • Kind: organization

GitHub Events

Total
  • Issues event: 6
  • Watch event: 1
  • Delete event: 8
  • Issue comment event: 14
  • Push event: 22
  • Pull request review comment event: 21
  • Pull request event: 30
  • Pull request review event: 34
  • Create event: 7
Last Year
  • Issues event: 6
  • Watch event: 1
  • Delete event: 8
  • Issue comment event: 14
  • Push event: 22
  • Pull request review comment event: 21
  • Pull request event: 30
  • Pull request review event: 34
  • Create event: 7

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 140
  • Total Committers: 4
  • Avg Commits per committer: 35.0
  • Development Distribution Score (DDS): 0.436
Past Year
  • Commits: 130
  • Committers: 4
  • Avg Commits per committer: 32.5
  • Development Distribution Score (DDS): 0.392
Top Committers
Name Email Commits
dependabot[bot] 4****] 79
Dave Bunten d****n@c****u 56
Jenna Tomkinson 1****n 4
Vincent Rubinetti v****i@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 5
  • Total pull requests: 103
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 days
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.73
  • Merged pull requests: 88
  • Bot issues: 0
  • Bot pull requests: 80
Past Year
  • Issues: 5
  • Pull requests: 103
  • Average time to close issues: N/A
  • Average time to close pull requests: 2 days
  • Issue authors: 3
  • Pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.73
  • Merged pull requests: 88
  • Bot issues: 0
  • Bot pull requests: 80
Top Authors
Issue Authors
  • jenna-tomkinson (3)
  • MikeLippincott (1)
Pull Request Authors
  • dependabot[bot] (80)
  • d33bs (21)
  • jenna-tomkinson (2)
Top Labels
Issue Labels
Pull Request Labels
dependencies (80) python (79) github_actions (1)

Dependencies

.github/workflows/run-tests.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • actions/setup-python v4 composite
  • pre-commit/action v3.0.1 composite
poetry.lock pypi
  • colorama 0.4.6
  • exceptiongroup 1.2.1
  • iniconfig 2.0.0
  • numpy 1.26.4
  • packaging 24.0
  • pandas 2.2.2
  • pluggy 1.5.0
  • pyarrow 16.0.0
  • pytest 8.2.0
  • python-dateutil 2.9.0.post0
  • pytz 2024.1
  • scipy 1.13.0
  • six 1.16.0
  • tomli 2.0.1
  • tzdata 2024.1
pyproject.toml pypi
  • pytest ^8.2.0 develop
  • pandas ^2.2.2
  • pyarrow ^16.0.0
  • python >=3.9,<3.13
  • scipy ^1.13.0
.github/workflows/draft-release.yml actions
  • release-drafter/release-drafter v6 composite
.github/workflows/publish-pypi.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v4 composite
  • pypa/gh-action-pypi-publish release/v1 composite