Plugins for SeqGeq add a great deal of new features and improved functionality. Download the full range of features from the FlowJo Exchange!
Plugins can be downloaded from the FlowJo Exchange. These plugins come with documentation describing the install and setup required for each. Read this documentation closely for the instructions for
Plugins come in the form of JAR files, which must be placed into a common folder known as the “SeqGeq Plugins” folder, the name given to this folder by a researching is not important. Many plugins require a connection too and dependencies within the ‘R’ statistical software. The plugin connections will need to be specified through the Diagnostic section of SeqGeq’s preferences:
Note: These file paths are not the same in every user’s machine, so you’ll need to specify the appropriate path for your particular install.
To run a plugin in SeqGeq, simply select a population on which you’d like to run the plugin, go to the Plugins section within the Workspace tab in SeqGeq’s workspace, and select the plugin of interest:
Note: R will not run if the file path leading to your GeqZip workspace contains any space characters. Make sure to remove the spaces from your GeqZip file path before attempting to run plugins that rely on R.
We encourage researchers utilizing R packages, or designing their own algorithms to publish plugins for FlowJo, in order to share their important work with the world. To get these power-users started, we offer couple of resources for developers:
Plugins for SeqGeq that get our blood pumping include (but are NOT limited to):
The Seurat pipeline plugin, which utilizes open source work done by researchers at the Satija Lab, NYU. This powerful analysis tool does a whole set of machine learning steps from a single dialog including: some quality control filtering, dimensionality reduction, KNN unbiased clustering, and differential expression analysis of those clusters.
This plugin often makes for a great first start when analyzing heterogeneous data-sets:
The Monocle plugin for pseudotime prediction, developed by the Cole Trapnell Lab. This algorithm attempts to predict biological pseudotime by investigating hypothetical trajectories leading to differentiation and terminal states. We’ve found this tool is particularly useful for investigating subsets within relatively homogeneous data, such as subpopulations of interest, allowing researchers to get the most in terms of depth from their analyses:
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