cytodataframe

An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.

https://github.com/cytomining/cytodataframe

Science Score: 77.0%

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

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .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 3 committers (33.3%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (18.1%) to scientific vocabulary

Keywords

dataframes image-analysis image-based-profiling single-cell

Keywords from Contributors

profiles cytomine interactive way-lab morphological-profiling cellprofiler carpenter-lab microscopy-image-analysis network-simulation hacking
Last synced: 4 months ago · JSON representation ·

Repository

An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.

Basic Info
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 2
  • Open Issues: 23
  • Releases: 14
Topics
dataframes image-analysis image-based-profiling single-cell
Created about 1 year ago · Last pushed 5 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

CytoDataFrame

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

CytoDataFrame extends Pandas functionality to help display single-cell profile data alongside related images.

CytoDataFrame is an advanced in-memory data analysis format designed for single-cell profiling, integrating not only the data profiles but also their corresponding microscopy images and segmentation masks. Traditional single-cell profiling often excludes the associated images from analysis, limiting the scope of research. CytoDataFrame bridges this gap, offering a purpose-built solution for comprehensive analysis that incorporates both the data and images, empowering more detailed and visual insights in single-cell research.

CytoDataFrame is best suited for work within Jupyter notebooks. With CytoDataFrame you can:

  • View image objects alongside their feature data using a Pandas DataFrame-like interface.
  • Highlight image objects using mask or outline files to understand their segmentation.
  • Adjust image displays on-the-fly using interactive slider widgets.

📓 Want to see CytoDataFrame in action? Check out our example notebook for a quick tour of its key features.

✨ CytoDataFrame development began within coSMicQC - a single-cell profile quality control package. Please check out our work there as well!

Installation

Install CytoDataFrame from source using the following:

```shell

install from pypi

pip install cytodataframe

or install directly from source

pip install git+https://github.com/cytomining/CytoDataFrame.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

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
---
cff-version: 1.2.0
title: CytoDataFrame
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: David
    family-names: Bunten
    orcid: 'https://orcid.org/0000-0001-6041-3665'
  - given-names: Jenna
    family-names: Tomkinson
    orcid: 'https://orcid.org/0000-0003-2676-5813'
  - given-names: Vincent
    family-names: Rubinetti
    orcid: 'https://orcid.org/0000-0002-4655-3773'
  - given-names: Gregory
    family-names: Way
    orcid: 'https://orcid.org/0000-0002-0503-9348'
repository-code: 'https://github.com/cytomining/CytoDataFrame'
identifiers:
  - description: Software DOI
    type: doi
    value: "10.5281/zenodo.14797074"
abstract: >-
  An in-memory data analysis format for single-cell profiles alongside their corresponding images and segmentation masks.
keywords:
  - python
  - single-cell-analysis
  - profiling
  - dataframes
  - data-analysis
  - way-lab
license: BSD-3-Clause
references:
  - authors:
      - name: "Way Lab CFReT_data Team"
    date-accessed: "2024-05-13"
    title: Way Lab CFReT_data CytoTable Data
    type: data
    repository-code: "https://github.com/WayScience/CFReT_data"
    url: "https://github.com/WayScience/CFReT_data/blob/main/3.process_cfret_features/data/converted_profiles/localhost231120090001_converted.parquet"
    scope: "localhost231120090001_converted.parquet"
    notes: >-
      Data from CFReT_data project is used to help validate
      expected results. Data is generated from CellProfiler
      and CytoTable.
    identifiers:
      - description: "Github Link with Contributors"
        type: url
        value: "https://github.com/WayScience/CFReT_data/graphs/contributors"
  - authors:
      - name: "Way Lab NF1_cellpainting_data Team"
    date-accessed: "2024-06-28"
    title: Way Lab NF1_cellpainting_data CytoTable Data
    type: data
    repository-code: "https://github.com/WayScience/nf1_cellpainting_data"
    notes: >-
      Data from NF1_cellpainting_data project is used to help validate
      expected results. Data is generated from CellProfiler
      and CytoTable. We use the following files from the repository:
      - "Plate_2_nf1_analysis.sqlite"
      - "Plate_2.parquet"
    identifiers:
      - description: "Github Link with Contributors"
        type: url
        value: "https://github.com/WayScience/nf1_cellpainting_data/graphs/contributors"
  - title: >-
      Plate 2 (Cell Painting images from Plate 2 for NF1_cellpainting_data project)
    type: data
    url: https://figshare.com/articles/dataset/Plate_2/22233700
    notes: >-
      Image data for related NF1_cellpainting_data parquet sqlite.
    authors:
    - family-names: Tomkinson
      given-names: Jenna
    - family-names: Mattson-Hoss
      given-names: Michelle
    - family-names: Sarnoff
      given-names: Herb
    - family-names: Way
      given-names: Gregory
    date-published: "2023-04-12"
    identifiers:
    - type: doi
      value: 10.6084/m9.figshare.22233700.v4
  - authors:
      - name: "Way Lab and Alexander Lab Nuclear Speckles Collaboration"
    date-accessed: "2024-09-04"
    title: Way Lab and Alexander Lab Nuclear Speckles Collaboration Data
    type: data
    repository-code: https://github.com/WayScience/nuclear_speckle_image_profiling
    notes: >-
      Data from a collaborative project focusing on nuclear speckles
      with the Way Lab and Alexander Lab s used to help validate
      expected results. Parquet data is generated from CellProfiler
      and CytoTable. Images courtesy of Katherine Alexander
      and the Alexander Lab.
    identifiers:
      - description: "Github Link with Contributors"
        type: url
        value: "https://github.com/WayScience/nuclear_speckle_image_profiling/graphs/contributors"
  - authors:
      - family-names: Chandrasekaran
        given-names: Srinivas Niranj
      - family-names: Cimini
        given-names: Beth A.
      - family-names: Goodale
        given-names: Amy
      - family-names: Miller
        given-names: Lisa
      - family-names: Kost-Alimova
        given-names: Maria
      - family-names: Jamali
        given-names: Nasim
      - family-names: Doench
        given-names: John G.
      - family-names: Fritchman
        given-names: Briana
      - family-names: Skepner
        given-names: Adam
      - family-names: Melanson
        given-names: Michelle
      - family-names: Kalinin
        given-names: Alexandr A.
      - family-names: Arevalo
        given-names: John
      - family-names: Haghighi
        given-names: Marzieh
      - family-names: Caicedo
        given-names: Juan C.
      - family-names: Kuhn
        given-names: Daniel
      - family-names: Hernandez
        given-names: Desiree
      - family-names: Berstler
        given-names: James
      - family-names: Shafqat-Abbasi
        given-names: Hamdah
      - family-names: Root
        given-names: David E.
      - family-names: Swalley
        given-names: Susanne E.
      - family-names: Garg
        given-names: Sakshi
      - family-names: Singh
        given-names: Shantanu
      - family-names: Carpenter
        given-names: Anne E.
    date-accessed: "2024-08-21"
    title: >-
      Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations
    type: article
    issn: 1548-7105
    issue: 6
    journal: Nature Methods
    pages: 1114-1121
    volume: 21
    url: https://doi.org/10.1038/s41592-024-02241-6
    date-published: "2024-06-01"
    identifiers:
      - type: doi
        value: 10.1038/s41592-024-02241-6
    notes: >-
      JUMP (cpg0000-jump-pilot) was used to help demonstrate CytoDataFrame performance
      with large data. See here for more information:
      https://github.com/broadinstitute/cellpainting-gallery

GitHub Events

Total
  • Create event: 3
  • Release event: 1
  • Issues event: 9
  • Watch event: 2
  • Delete event: 2
  • Issue comment event: 11
  • Push event: 21
  • Pull request review comment event: 28
  • Pull request review event: 31
  • Pull request event: 17
  • Fork event: 1
Last Year
  • Create event: 3
  • Release event: 1
  • Issues event: 9
  • Watch event: 2
  • Delete event: 2
  • Issue comment event: 11
  • Push event: 21
  • Pull request review comment event: 28
  • Pull request review event: 31
  • Pull request event: 17
  • Fork event: 1

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 65
  • Total Committers: 3
  • Avg Commits per committer: 21.667
  • Development Distribution Score (DDS): 0.4
Past Year
  • Commits: 65
  • Committers: 3
  • Avg Commits per committer: 21.667
  • Development Distribution Score (DDS): 0.4
Top Committers
Name Email Commits
dependabot[bot] 4****] 39
Dave Bunten d****n@c****u 25
Jenna Tomkinson 1****n 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 32
  • Total pull requests: 68
  • Average time to close issues: 24 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.44
  • Average comments per pull request: 0.72
  • Merged pull requests: 60
  • Bot issues: 0
  • Bot pull requests: 41
Past Year
  • Issues: 31
  • Pull requests: 68
  • Average time to close issues: 24 days
  • Average time to close pull requests: 2 days
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 0.39
  • Average comments per pull request: 0.72
  • Merged pull requests: 60
  • Bot issues: 0
  • Bot pull requests: 41
Top Authors
Issue Authors
  • d33bs (33)
  • jenna-tomkinson (5)
Pull Request Authors
  • dependabot[bot] (40)
  • d33bs (35)
  • jenna-tomkinson (1)
  • vincerubinetti (1)
Top Labels
Issue Labels
enhancement (16) bug (9)
Pull Request Labels
dependencies (40) python (39) github_actions (1)

Dependencies

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