{ "info": { "author": "Brian Teague", "author_email": "bpteague@gmail.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Environment :: MacOS X", "Environment :: Win32 (MS Windows)", "Environment :: X11 Applications :: Qt", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v2 (GPLv2)", "Natural Language :: English", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "Cytoflow\n========\n\nPython tools for quantitative, reproducible flow cytometry analysis\n-------------------------------------------------------------------\n\nWelcome to a different style of flow cytometry analysis. Take a look at\nsome example `Jupyter `__ notebooks:\n\n- `Basic flow cytometry\n analysis `__\n- `An small-molecule induction curve with\n yeast `__\n- `Machine learning applied to flow cytometry\n data `__\n- `Reproduced analysis from a published\n paper `__\n- `A multi-dimensional induction in\n yeast `__\n- `Calibrated flow\n cytometry `__\n\nor some `screenshots from the\nGUI `__\n\nWhat\u2019s wrong with other packages?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPackages such as FACSDiva and FlowJo are focused on primarily on\nidentifying and counting subpopulations of cells in a multi-channel flow\ncytometry experiment. While this is important for many different\napplications, it reflects flow cytometry\u2019s origins in separating\nmixtures of cells based on differential staining of their cell surface\nmarkers.\n\nCytometers can also be used to measure internal cell state, frequently\nas reported by fluorescent proteins such as GFP. In this context, they\nfunction in a manner similar to a high-powered plate-reader: instead of\nreporting the sum fluorescence of a population of cells, the cytometer\nshows you the *distribution* of the cells\u2019 fluorescence. Thinking in\nterms of distributions, and how those distributions change as you vary\nan experimental variable, is something existing packages don\u2019t handle\ngracefully.\n\nWhat\u2019s different about Cytoflow?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA few things.\n\n- **Free and open-source.** Use the software free-of-charge; modify it\n to suit your own needs, then contribute your changes back so the rest\n of the community can benefit from them.\n\n- A `point-and-click interface `__\n for easy analysis.\n\n- **Python modules** to integrate into larger apps, automation, or for\n use in a `Jupyter notebook `__\n\n- An emphasis on **metadata**. Cytoflow assumes that you are measuring\n fluorescence on several samples that were treated differently: either\n they were collected at different times, treated with varying levels\n of inducers, etc. You specify the conditions for each sample up\n front, then use those conditions to facet the analysis.\n\n- Cytometry analysis conceptualized as a **workflow**. Raw cytometry\n data is usually not terribly useful: you may gate out cellular debris\n and aggregates (using FSC and SSC channels), then compensate for\n channel bleed-through, and finally select only transfected cells\n before actually looking at the parameters you\u2019re interested in\n experimentally. Cytoflow implements a workflow paradigm, where\n operations are applied sequentially; a workflow can be saved and\n re-used, or shared with your coworkers.\n\n- **Easy to use.** Sane defaults; good documentation; focused on doing\n one thing and doing it well.\n\n- **Good visualization.** I don\u2019t know about you, but I\u2019m getting\n really tired of FACSDiva plots.\n\n- **Versatile.** Built on Python, with a well-defined library of\n operations and visualizations that are well separated from the user\n interface. Need an analysis that Cytoflow doesn\u2019t have? Export your\n workflow to a Jupyter notebook and use any Python module you want to\n complete your analysis. Data is stored in a ``pandas.DataFrame``,\n which is rapidly becoming the standard for Python data analysis (and\n will make R users feel right at home.)\n\n- **Extensible.** (Adding a new analysis or visualization\n module)[http://cytoflow.readthedocs.io/en/latest/new_modules.html) is\n simple; the interface to implement is only two or three functions.\n\n- **Quantitative and statistically sound.** Ready access to useful\n data-driven tools for analysis, such as fitting 2-dimensional\n Gaussians for automated gating and mixture modeling.\n\nInstallation\n~~~~~~~~~~~~\n\n**If you just want the point-and-click version (not the Python modules),\nyou can install it from http://bpteague.github.io/cytoflow/**\n\nSee the `installation\nnotes `__ on\n`ReadTheDocs `__. Installation has\nbeen tested on Linux, Windows (x86_64) and Mac. 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