Plugins add a wealth of features to FlowJo adding on to its analysis base.
These plugins can be downloaded from the FlowJo Exchange on our website. The FlowJo plugins are constantly being updated with new tools designed by the research community, allowing users to stay on the cutting edge of analysis. Each plugin has a unique set of functions that it adds to FlowJo. They also each have different steps for installation. The following videos explain those new features and lead users through the basic steps necessary to use plugins.
An Overview of Plugins
The following video demonstrates how to install plugins and provides an overview of how to use some of the most popular Plugins available in FlowJo. Flow scientist Luthy describes the various functions of plugins and compares some of the pros and cons of the analysis these plugins offer. The scientist then shows how to use several plugins together to generate a robust flow analysis.
The iCellR plugin is built on the R package ‘iCellR’ by Alireza “Reza” Khodadadi-Jamayran, a Senior Bioinformatics Software Engineer at NYU. The iCellR tool is designed to analyze high parameter single-cell data in R. The iCellR plugin by BD Life Science – Informatics extends this functionality to users who work with data from scRNA-seq data in SeqGeq, or even flow cytometry data in FlowJo. Users can perform: clustering (from the nbClust R package), tSNE, UMAP, and PCA analyses – simultaneously – and view the results in an interactive 3D plot using GoogleChrome. In SeqGeq, there is additional functionality to perform Differential Expression Analysis on a categorical clustering parameter based on an unsupervised clustering algorithm, or SampleID, at the click of a button. Available for both FlowJo and SeqGeq, with some dependencies in R.
FlowSOM is a state of the art (see review: https://www.ncbi.nlm.nih.gov/pubmed/27992111) clustering and visualization technique, which analyzes flow or mass cytometry data using self-organizing maps. With two-level clustering and star charts, the algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. The method has been implemented as a R/BioConductor library and published in Van Gassen et al., Cytometry A, 2015: https://www.ncbi.nlm.nih.gov/pubmed/25573116.
Violin and box plots are popular ways of illustrating expression patterns between genes or proteins of interest and across different populations or samples. This updated version of ViolinBoxPlots now includes Raincloud Plots, an updated take on ViolinBoxPlots. These plots include a ‘split-half violin’, jittered data points, and a standard visualization of central tendency and error via the boxplot. In FlowJo and SeqGeq these features have been implemented as a plugin with some dependencies in R. The plugin was based off the R library developed by Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. 2018. Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints 6:e27137v1 https://doi.org/10.7287/peerj.preprints.27137v1
The CytoNorm algorithm has been developed and implemented as an R package by Sofie Van Gassen PhD, from the Saeys Lab out of the University of Belgium, in Ghent. The purpose of the tool is to normalize batch effects in flow cytometric datasets collected in different batches, based on a similar set of controls run with each batch. CytoNorm works best if a control sample is provided for each batch. These control samples are used to normalize each batch to a common FlowSOM ‘spline’. (1) The CytoNorm plugin requires that each sample – both controls and different batches – contain a common parameter set named exactly the same for each FCS file. Also see “CytoNorm: A Normalization Algorithm for Cytometry Data” Van Gassen S, Gaudilliere B, Angst MS, Saeys Y, Aghaeepour N Cytometry A. 2019
If you have any questions about plugins reach out: email@example.com