Plugins add a wealth of features to SeqGeq, and in doing so compliment its rich base of native analysis functionality.
These plugins can be downloaded from the FlowJo Exchange on our website. The SeqGeq 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 may have slightly different steps for installation. The following videos explain these plugins and lead users through the basic steps necessary to use a few of the plugins.
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.
BatchLR implements a method for batch correction of single-cell (RNA sequencing) data. Batch effects refer to differences between data sets that are often an unavoidable product of differences in samples we wish to compare, including: time-points, laboratories, and even sequencing pipelines. The fastMNN method implemented in this plugin is based on detecting mutually nearest neighbors as well as simple sparsity-preserving translation of the population means. This approach does not rely on equal population compositions across batches, instead it requires that only a subset of the population be shared between batches. This allows the method to be scaled to large numbers of cells. The algorithm returns a matrix of corrected principal components that can be used for downstream analyses such as visualization and clustering, without altering the underlying data matrices. MNN batch effect correction has been implemented as an R/BioConductor library and published: Haghverdi L, Lun ATL, Morgan MD, Marioni JC (2018). “Batch effects in single cell RNA-sequencing data are corrected by matching mutual nearest neighbors.” Nat. Biotechnol., 36(5), 421–427. https://www.nature.com/articles/nbt.4091.
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
As you have questions about plugins and their associated workflows, please reach out to the team at FlowJo, we appreciate all your feedback: email@example.com