Imaging flow cytometry (IFC) combines the high-throughput capabilities of conventional flow cytometry with single-cell imaging. CellProfiler can be used to analyze the resulting images from imaging flow cytometry, whether brightfield, darkfield, or fluorescence.

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This is the offical web page of Flowing Software - a FREE flow cytometry data analysis software. Flowing Software has three goals: To provide free, user friendly and effective analysis tool for flow cytometry community. To develop new innovations and ideas for the analysis. Users' feedback is. Jan 01, 2017 Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary.cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains over 30 open source modules covering methods from basic processing of FCS files to advanced algorithms for automated identification of cell populations, normalization and quality assessment.

We here provide an open-source IFC protocol described in Hennig et al. (2016). This protocol aims to enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye. Compensated data files from an imaging flow cytometer (the proprietary .cif file format) can be read and resulting image tiles are generated. The image tiles are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analyzed using cutting-edge machine learning and clustering approaches using user-friendly platforms such as CellProfiler Analyst or scripting languages such as R or Python.

Note: This is a more user-friendly and streamlined protocol as compared to Blasi et al. (2016), however, the former protocol is still available here.

Papers: H. Hennig, P. Rees, T. Blasi, L. Kamentsky, J. Hung, D. Dao, A.E. Carpenter, and A. Filby. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Method, in press (2016) [link to paper at Methods]
T. Blasi, H. Hennig, H.D. Summers, F.J. Theis, D. Davies, A. Filby, A.E. Carpenter, P. Rees. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Comm. 7, 10256 (2016). PMID: 26739115 [link to paper at Nature Communications]

Protocol: [download Protocol_README.txt]

Preparatory Step: Identify cell populations using gating in IDEAS software. Export population as cif file.

Step 1: Automatically generate tiles of 1000 single cell images per tile, using a python app (alternatively a Matlab script is available). The app reads a cif file and writes the tiles (which are tif image files) to the output folder.

[example input cif file] [python app for tiling] [Matlab script for tiling] [example output data]

Step 2: Segment images and extract features in CellProfiler. The example CellProfiler pipeline exports the features as csv files. The pipeline also generates a CellProfiler Analyst properties file for the machine learning in step 3.

[PDF Protocol] [example input data] [CellProfiler pipeline] [example output data]

Step 3: Use any programming language for supervised or unsupervised machine learning, such as python or R. A user-friendly option for machine learning is the softwareCellProfiler Analyst. For this, load the properties file in CellProfilerAnalyst.

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Welcome to a different style of flow cytometry analysis. Take a look at some example Jupyter notebooks:

or some screenshots from the GUI

What's wrong with other packages?

Packages such as FACSDiva and FlowJo are focused on primarily on identifyingand counting subpopulations of cells in a multi-channel flow cytometryexperiment. While this is important for many different applications, itreflects flow cytometry's origins in separating mixtures of cells based ondifferential staining of their cell surface markers.

Cytometers can also be used to measure internal cell state, frequently asreported by fluorescent proteins such as GFP. In this context, they functionin a manner similar to a high-powered plate-reader: instead of reporting thesum fluorescence of a population of cells, the cytometer shows you thedistribution of the cells' fluorescence. Thinking in terms of distributions,and how those distributions change as you vary an experimental variable, issomething existing packages don't handle gracefully.

What's different about Cytoflow?

A few things.

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Free and open-source. Use the software free-of-charge; modify it tosuit your own needs, then contribute your changes back so the rest of thecommunity can benefit from them.
A point-and-click interface foreasy analysis.
Python modules to integrate into larger apps, automation, or for use ina Jupyter notebook
An emphasis on metadata. Cytoflow assumes that you are measuringfluorescence on several samples that were treated differently: eitherthey were collected at different times, treated with varying levelsof 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 datais usually not terribly useful: you may gate out cellular debris andaggregates (using FSC and SSC channels), then compensate for channelbleed-through, and finally select only transfected cells before actuallylooking at the parameters you're interested in experimentally. Cytoflowimplements 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 onething and doing it well.
Good visualization. I don't know about you, but I'm getting reallytired of FACSDiva plots.
Versatile. Built on Python, with a well-defined library of operationsand visualizations that are well separated from the user interface. Need ananalysis that Cytoflow doesn't have? Export your workflow to a Jupyternotebook and use any Python module you want to complete your analysis. Datais stored in a pandas.DataFrame, which is rapidly becoming the standard forPython data analysis (and will make R users feel right at home.)
Extensible. (Adding a new analysis or visualization module)[http://cytoflow.readthedocs.io/en/latest/new_modules.html)is simple; the interface to implement is only two or three functions.
Quantitative and statistically sound. Ready access to useful>Installation
If you just want the point-and-click version (not the Python modules), youcan install it from http://bpteague.github.io/cytoflow/
See the installation noteson ReadTheDocs. Installation has been testedon Linux, Windows (x86_64) and Mac. Cytoflow is distributed as an Anacondapackage (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 packageis intended to be used interactively.

Python tools for quantitative, reproducible flow cytometry analysis

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What's wrong with other packages?

What's different about Cytoflow?

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Documentation

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