Inclusion of irrelevant parameters can add background noise in the calculation without contributing to the signal. Do not include parameters that may have been collected, but were not utilized in the staining panel and leave out any common parameters that do not vary across the sample population you are investigating.

If your data set is fluorescence-based, select only compensated parameters (Comp-Parameter::Stain Reagent).

Parameter Selection – In addition to choosing which events to use in your tSNE calculation it is also important to choose appropriate parameters.

In addition, gate to include only the cells of interest (e.g., gating on CD3+ if T cells are of primary interest). This step reduces noise in the data and can improve the tSNE algorithm output.

Cleaning up your data – The best analyses begin with cleaning up raw data to exclude doublets, debris, and dead cells.

This section will briefly cover a few key points in the area of preparing your data. While tSNE is a powerful visualization technique, running the algorithm is computationally expensive, and the output is sensitive to the input data.

The native platforms in FlowJo (such as tSNE) do not require R.

It can be accessed and run through the Populations menu (Workspace tab –> Populations band).

įlowJo v10 has an extremely powerful native platform for running tSNE. Please see the references section for more details on the tSNE algorithm and its potential applications. It can be used independently to visualize an entire data file in an exploratory manner, as a preprocessing step in anticipation of clustering, or in other related workflows. Importantly, tSNE can be used as a piece of many different workflows. The tSNE-generated parameters are optimized in such a way that observations/data points which were close to one another in the raw high dimensional data are close in the reduced data space. T he tSNE platform computes two new derived parameters from a user defined selection of cytometric parameters. TSNE is an unsupervised nonlinear dimensionality reduction algorithm useful for visualizing high dimensional flow or mass cytometry data sets in a dimension-reduced data space. Here is a collection of external links about Compensation.Īll of the pages linked above, also link back here for your convenience.T-Distributed Stochastic Neighbor Embedding (tSNE) is an algorithm for performing dimensionality reduction, allowing visualization of complex multi-dimensional data in fewer dimensions while still maintaining the structure of the data.

When you are ready to proceed to a more complete overview of the new compensation work flow, please see this document.Īlso, consider the Compensation Tutorial. One other document you will likely find useful is:Īpplying/Renaming – the matrix, a small overview. The Matrix editor – used to view/adjust matrix numbers with a preview of the changes.The Compensation window – used to define a compensation matrix from single stain controls.The Compensation group – in the workspace and tied to compensation Preferences.Single stained controls used to compensate in FlowJo will need to be scaled correctly ahead of time.įor a more complete overview of the theory and principles of compensation, please check out this page.įor version 10, there are three main components to the user interface for Compensation: Single stain controls must be run to determine the amount of secondary fluorescence contributed to each channel. Therefore, in multi-color flow cytometry, each channel will detect the primary fluorescence, but also secondary fluorescence arising from other overlapping fluorophores. However, the fluorophores used in flow cytometry do not adhere to the exact range of emission detected by the instrument. The detectors, or channels, in the instrument are designed to detect a very specific range of emissions. Compensation has undergone a major redesign for FlowJo Version 10!Ĭompensation in flow cytometry is the process of correcting for fluorescence spillover emissions.

Flowjo tutorial compensation