cytoflow
A Python toolbox for quantitative, reproducible flow cytometry analysis
Science Score: 36.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Academic publication links
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6 of 14 committers (42.9%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (13.9%) to scientific vocabulary
Keywords
Repository
A Python toolbox for quantitative, reproducible flow cytometry analysis
Basic Info
Statistics
- Stars: 200
- Watchers: 12
- Forks: 54
- Open Issues: 60
- Releases: 23
Topics
Metadata Files
README.md
Cytoflow
Python tools for quantitative, reproducible flow cytometry analysis
Welcome to a different style of flow cytometry analysis. Take a look at some example Jupyter notebooks:
- Basic flow cytometry analysis
- An small-molecule induction curve with yeast
- Machine learning applied to flow cytometry data
- Reproduced analysis from a published paper
- A multi-dimensional induction in yeast
- Calibrated flow cytometry
or some screenshots from the GUI
Cytoflow is developed sporadically.
My 'day job' is teaching at a regional comprehensive college, so during the semester I may not have a huge amount of time to respond to bugs and feature requests. I'm still activately developing Cytoflow, so please continue to file bugs!
Cytoflow's development isn't free -- your support is appreciated.
I pay several hundred dollars a year to sign the code so it can be installed on Windows and Mac systems. If you have found it useful, consider contributing a few dollars on Ko-Fi?
What's wrong with other packages?
Packages such as FACSDiva and FlowJo are focused on primarily on identifying and counting subpopulations of cells in a multi-channel flow cytometry experiment. While this is important for many different applications, it reflects flow cytometry's origins in separating mixtures of cells based on differential staining of their cell surface markers.
Cytometers can also be used to measure internal cell state, frequently as reported by fluorescent proteins such as GFP. In this context, they function in a manner similar to a high-powered plate-reader: instead of reporting the sum fluorescence of a population of cells, the cytometer shows you the distribution of the cells' fluorescence. Thinking in terms of distributions, and how those distributions change as you vary an experimental variable, is something existing packages don't handle gracefully.
What's different about Cytoflow?
A few things.
Free and open-source. Use the software free-of-charge; modify it to suit your own needs, then contribute your changes back so the rest of the community can benefit from them.
A point-and-click interface for easy analysis.
Python modules to integrate into larger apps, automation, or for use in a Jupyter notebook
An emphasis on metadata. Cytoflow assumes that you are measuring fluorescence on several samples that were treated differently: either they were collected at different times, treated with varying levels of inducers, etc. You specify the conditions for each sample up front, then use those conditions to facet the analysis.
Cytometry analysis conceptualized as a workflow. Raw cytometry data is usually not terribly useful: you may gate out cellular debris and aggregates (using FSC and SSC channels), then compensate for channel bleed-through, and finally select only transfected cells before actually looking at the parameters you're interested in experimentally. Cytoflow implements a workflow paradigm, where operations are applied sequentially; a workflow can be saved and re-used, or shared with your coworkers.
Easy to use. Sane defaults; good documentation; focused on doing one thing and doing it well.
Good visualization. I don't know about you, but I'm getting really tired of FACSDiva plots.
Versatile. Built on Python, with a well-defined library of operations and visualizations that are well separated from the user interface. Need an analysis that Cytoflow doesn't have? Export your workflow to a Jupyter notebook and use any Python module you want to complete your analysis. Data is stored in a
pandas.DataFrame, which is rapidly becoming the standard for Python data analysis (and will make R users feel right at home.)Extensible. Adding a new analysis or visualization module is simple; the interface to implement is only two or three functions.
Quantitative and statistically sound. Ready access to useful data-driven tools for analysis, such as fitting 2-dimensional Gaussians for automated gating and mixture modeling.
Installation
If you just want the point-and-click version (not the Python modules), you can install it from http://cytoflow.github.io/
See the installation notes
on ReadTheDocs. Installation has been
testedon Linux, Windows (x86_64) and Mac (both Intel and Apple Silicon).
Cytoflow is distributed as an Anaconda package
(recommended) as well as a traditional Python package.
Documentation
Cytoflow's documentation lives at ReadTheDocs. Perhaps of most use is the module index. The example Jupyter notebooks, above, demonstrate how the package is intended to be used interactively.
Owner
- Name: cytoflow
- Login: cytoflow
- Kind: organization
- Repositories: 3
- Profile: https://github.com/cytoflow
GitHub Events
Total
- Create event: 21
- Release event: 4
- Issues event: 59
- Watch event: 28
- Delete event: 12
- Issue comment event: 66
- Push event: 146
- Pull request event: 2
- Fork event: 7
Last Year
- Create event: 21
- Release event: 4
- Issues event: 59
- Watch event: 28
- Delete event: 12
- Issue comment event: 66
- Push event: 146
- Pull request event: 2
- Fork event: 7
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Brian Teague | b****e@g****m | 2,923 |
| kristjaneerik | e****k@m****u | 20 |
| Debra Van Egeren | d****e@m****u | 15 |
| Guillermo Vargas | g****s@m****u | 4 |
| brian | b****n@g****a | 4 |
| brian | b****n@c****a | 4 |
| unknown | B****n@T****u | 3 |
| ejayawant | 4****t | 2 |
| dvanegeren | d****n@g****m | 2 |
| Brian Teague | b****n@t****t | 2 |
| Demarcus Briers | d****s@b****u | 1 |
| brian | b****n@c****o | 1 |
| Brian Teague | b****n@n****u | 1 |
| Brian Teague | b****n@B****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 122
- Total pull requests: 19
- Average time to close issues: 12 months
- Average time to close pull requests: over 1 year
- Total issue authors: 53
- Total pull request authors: 10
- Average comments per issue: 1.55
- Average comments per pull request: 1.21
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 32
- Pull requests: 2
- Average time to close issues: about 1 month
- Average time to close pull requests: N/A
- Issue authors: 10
- Pull request authors: 1
- Average comments per issue: 0.69
- Average comments per pull request: 0.5
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bpteague (47)
- benweinberg89 (9)
- NicholasDeLateur (4)
- protist (3)
- crife-pact (3)
- dkraut01 (2)
- deemishra (2)
- maaikesangster (2)
- AlFontal (2)
- heesters (2)
- IsaacUtah1379 (2)
- NessaNoctiluca (2)
- alefrol638 (2)
- fbastok (1)
- Jsakkos (1)
Pull Request Authors
- kristjaneerik (4)
- dependabot[bot] (3)
- ejayawant (2)
- michaelosthege (2)
- CaRniFeXeR (2)
- akshayMpatel (2)
- mpiersonsmela (1)
- drpeterfoster (1)
- tim-tx (1)
- pintomollo (1)
Top Labels
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Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 245 last-month
- Total dependent packages: 0
- Total dependent repositories: 3
- Total versions: 20
- Total maintainers: 1
pypi.org: cytoflow
Python tools for quantitative, reproducible flow cytometry analysis
- Homepage: https://github.com/cytoflow/cytoflow
- Documentation: https://cytoflow.readthedocs.io/
- License: GPLv2
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Latest release: 1.1.1
published almost 5 years ago