#FLOWJO PANEL ANALYSIS MANUAL#
#FLOWJO PANEL ANALYSIS FULL#
#FLOWJO PANEL ANALYSIS SOFTWARE#

To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts.ĭespite the phenomenal advances in Single Cell Data Science (SCDS) methodology and an ever-growing collection of algorithms and open-source packages, it is an open secret that the day-to-day analysis of cytometric data in flow laboratories and core facilities is still predominantly performed using traditional software, especially FlowJo.

#FLOWJO PANEL ANALYSIS SOFTWARE#

There are good reasons for this - traditional software such as FlowJo excels at the visual manipulation and analysis of data, and human analysis is inherently more adaptable than any fully automated workflow. For example, domain experts are typically better at removing debris, dead cells, and cell aggregates by gating than automated approaches.

#FLOWJO PANEL ANALYSIS MANUAL#

However, there are also severe limitations to a purely manual workflow for data analysis, especially the poor scalability to high-volume workflows and limitations of visual discovery for high-dimensional data sets. We developed FlowKit to bridge the gap between manual and automated workflows. Specifically, we wanted to develop a robust basis for foundational cytometry operations, provide a straightforward interface to SCDS algorithms, and facilitate the integration of manual and automated analysis.

#FLOWJO PANEL ANALYSIS FULL#

To ensure that foundational cytometry operations are supported, we checked for full compliance with Gating-ML 2.0 ( 1), hence ensuring that compensation, transformation, and gating operations were all implemented correctly. To interface with data science, machine learning and computer vision algorithms, we developed FlowKit so that analytic results and event data could be exported as a generic pandas DataFrame, a standard unit for analysis in Python scientific workflows and interoperable with the R, Spark, and SQL frameworks. To allow integrative manual and automated analysis, we worked with FlowJo developers to ensure that FlowKit could read and write FCS and FlowJo workspace files, allowing the round-tripping of data and analytic results to and from FlowJo. We chose Python as the implementation language for pragmatic reasons - Python is the dominant language for data science, machine learning and computer vision the two main deep learning frameworks, TensorFlow and Torch, use Python as their de facto language Python is now often taught as a first programming language in many quantitative disciplines and Python has a robust ecosystem for scalable workflows and system integration. While R has a rich set of libraries for cytometric data analysis, we believe that FlowKit provides a complementary alternative with access to state-of-the-art data science and machine learning frameworks for Python developers and will be welcomed by the cytometry bioinformatics community. We anticipate and hope that the Python bioinformatics community will build advanced tools for quality control (QC), analysis, visualization, and even graphical user interfaces (GUI) on top of the foundational features provided by FlowKit. OK: Confirms changes and closes the dialog.Ĭancel: Cancels actions and changes are not applied.The data used in this manuscript were generated by the External Quality Assurance Program Oversight Laboratory (EQAPOL) Flow Cytometry Program. Reset: Resets all preferences to default settings. Help (?): Launch a web browser with context sensitive documentation (e.g. Tools: A host of options which help optimize your FlowJo experience.Platforms: Change your settings for the Panel Wizard, Compensation, and other special analysis platforms.Output: Set the default settings for graph visualizations, output from table and layout editors, and export/saving.Workspace: Change the display and options for the central hub of analysis in FlowJo.The Preferences dialogue is divided into four sections: Select the Preferences icon or the Shortcut Key: Ctrl+Y to open the Preferences Dialog. This document focuses on the Preferences panel. Usually preferences are set from the main Preferences panel. You can save default settings like the number of significant digits, linear or log scaling, and font sizes, among others.

Preferences can be used to fine-tune FlowJo to your specific analysis habits.

Flowjo panel analysis