COMPENSATION IN FLOWJO MANUAL
COMPENSATION IN FLOWJO SOFTWARE

To demonstrate the effect of gate placement I’m using FlowJo’s automated compensation tool, though this should be applicable to any comparable software. Thus, the accuracy of the compensation is dependent on a subjective gate placement. The flaw of automated compensation tools is that the user usually determines where to set the MFI for each population by placing gates on the positive and negative populations. Compensation determined to be correct when the median fluorescence intensity (MFI) of the positive population is in line with the MFI of the negative population (Figure 1). Setting Up the Automated Compensation Toolīefore I discuss gate placement I want to quickly go over how autocompensation tools work. Gate placement is a critical component in determining the outcome of an automated compensation tool. Yes, some of the tools do have automated gate placement, but that feature often fails to generate appropriate gates. The part that is often overlooked is that automated compensation tools are not fully automated – they require users to subjectively set gates on positive and negative cells within each control. In this post I’d like to demonstrate that poor controls are not always the cause. Why is it difficult to get accurate results from automated tools? The only explanation that I’ve heard from the flow cytometry experts is that suboptimal results are caused by poor quality compensation controls.

COMPENSATION IN FLOWJO MANUAL

However, people often struggle to get good results from the automated compensation tools and will turn to manual compensation to fix any errors. When calculating compensation, automated tools are the gold standard. For more tips on spectral unmixing specifically, see this video. It saves you time, and generally provides better results. Learn more about AutoSpill here.Įdit October 21, 2021: It should be noted that the tips for gate placement described in this post can be applied to spectral unmixing as well, just replace the term “compensation” with “unmixing”.

In essence, AutoSpill uses iterations of linear regressions to get the best compensation or unmixing matrices, and removes the need for gating the positive and negative fractions of your controls. introduced AutoSpill, which resolved the gating issue raised in this blogpost.

COMPENSATION IN FLOWJO SOFTWARE

The webinar concludes with a step-by-step demo of using AutoSpill in FlowJo™ Software and a comparison to the traditional approach.Edit January 19, 2023: The 2021 Nature paper by Carlos P. John Quinn, director of science and product development, BD Life Sciences–Biosciences (Informatics), introduces AutoSpill, beginning with the basics of compensation, then walks through the math behind Autospill. AutoSpill can be used for either traditional or spectral cytometry, can be used to remove autofluorescence, and is run iteratively to optimize the outcome every time. The AutoSpill algorithm uses the same single-stain controls used by standard compensation calculations but uses robust linear regression to estimate spillover, alleviating the need to identify positive and negative populations and working effectively even in the absence of perfect controls. AutoSpill is a new approach to compensation and unmixing that simplifies one of the most complex aspects of a flow cytometry experiment.

Compensation in flowjo