Metadata-Version: 1.1
Name: cytoflow
Version: 1.1
Summary: Python tools for quantitative, reproducible flow cytometry analysis
Home-page: https://github.com/cytoflow/cytoflow
Author: Brian Teague
Author-email: bpteague@gmail.edu
License: GPLv2
Description: 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 <http://jupyter.org/>`__ notebooks:
        
        -  `Basic flow cytometry
           analysis <https://github.com/cytoflow/cytoflow/blob/master/docs/examples-basic/Basic%20Cytometry.ipynb>`__
        -  `An small-molecule induction curve with
           yeast <https://github.com/cytoflow/cytoflow/blob/master/docs/examples-basic/Yeast%20Dose%20Response.ipynb>`__
        -  `Machine learning applied to flow cytometry
           data <https://github.com/cytoflow/cytoflow/blob/master/docs/examples-basic/Machine%20Learning.ipynb>`__
        -  `Reproduced analysis from a published
           paper <https://github.com/cytoflow/cytoflow-examples/blob/master/kiani/Kiani%20Nature%20Methods%202014.ipynb>`__
        -  `A multi-dimensional induction in
           yeast <https://github.com/cytoflow/cytoflow-examples/blob/master/yeast/Induction%20Timecourse.ipynb>`__
        -  `Calibrated flow
           cytometry <https://github.com/cytoflow/cytoflow-examples/blob/master/tasbe/TASBE%20Workflow.ipynb>`__
        
        or some `screenshots from the
        GUI <http://cytoflow.github.io/screenshots.html>`__
        
        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 <http://cytoflow.github.io/>`__ for easy
           analysis.
        
        -  **Python modules** to integrate into larger apps, automation, or for
           use in a `Jupyter notebook <http://jupyter.org/>`__
        
        -  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)[http://cytoflow.readthedocs.io/en/stable/new_modules.html) 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 <http://cytoflow.readthedocs.org/en/stable/INSTALL.html>`__ on
        `ReadTheDocs <http://cytoflow.readthedocs.org/>`__. Installation has
        been tested on Linux, Windows (x86_64) and Mac. Cytoflow is distributed
        as an `Anaconda <https://www.anaconda.com/>`__ package **(recommended)**
        as well as a `traditional Python
        package <https://pypi.org/project/cytoflow/>`__.
        
        Documentation
        ~~~~~~~~~~~~~
        
        Cytoflow’s documentation lives at
        `ReadTheDocs <http://cytoflow.readthedocs.org/>`__. Perhaps of most use
        is the `module
        index <http://cytoflow.readthedocs.org/en/latest/py-modindex.html>`__.
        The example `Jupyter <http://jupyter.org/>`__ notebooks, above,
        demonstrate how the package is intended to be used interactively.
        
Keywords: flow cytometry scipy
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: Environment :: X11 Applications :: Qt
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
