FlowJo allows you to add new parameters to your data by performing operations on existing parameters. We call these “Derived Parameters“. Once created, they behave like parameters that were collected at acquisition. They can be used in the graph window, or in plots within the layout editor. To open the Derived Parameters interface, click the… Read more »

A more detailed explanation of the algorithms in the Population Comparison platform FlowJo’s comparison platforms support four different comparison algorithms. Two algorithms (Overton and SED) are used to calculate the percentage of positive cells found in the sample (not in the control). Two algorithms (Kolmogorov-Smirnov and Probability Binning) are used to determine the statistical difference between… Read more »

Probability binning is the algorithm used for partitioning data for the Chi Square test. For the Probability Binning (PB) algorithm, the distribution is divided into a number of bins of the same height. The number of events falling into each of these bins is compared between a test and a control sample, and a Chi Squared-like… Read more »

Further explanation of the metric used to assess differences in samples that have been binned using Probability Binning. T(X) is a statistic which provides an indication of the probability with which two distributions are different and also provides a metric by which multiple distributions can be ranked. The higher the value of T(X), the less… Read more »

Tips for Using Population Comparison. In FlowJo, the Population Comparison platform is used to compare single or multiple parameters from a test sample to a single control sample OR a composite of more than one control sample.  The control population is a different population (either another sample or another population within the same sample) with which the test… Read more »

Compensation FAQ This page will help acquaint you with the various ways to interact with Compensation Matrices in FlowJo. For your convenience, this document is broken into chapters. Note, Compensation should always be preceded by scaling your data appropriately. Poorly scaled data will effect downstream compensation, so get into the habit of adjusting transforms through… Read more »

This page describes the compensation window. To open this window, click on your Compensation group to select it, then click the tool menu tab and select Compensation: This will open the main Compensation Window: This is a complex window, let’s examine it one part at a time. The top part of the window deals with… Read more »

Flow cytometers have optical channels that detect a very specific wavelength range of emissions from the fluorochromes.  A sequence of filters reduce the ranges of transmitted light. The detectors are all the same.  The breadth of the measurement made by any detector in the cytometer is determined not by the detector itself, but rather by the… Read more »

The following papers are great to learn more about compensation: Spectral Compensation for Flow Cytometry: Visualization Artifacts, Limitations, and Caveat. Mario Roederer, Cytometry 45:194 –205 (2001) A practical approach to multicolor flow cytometry for immunophenotyping. Baumgarth, Roederer. Journal of Immunological Methods 243 (2000) 77–97 Seventeen-colour flow cytometry: unravelling the immune system. Perfetto, Chattopadhyay and Roederer. Nature Review, Immunology Vol. 4… Read more »

Starting with version 10 of FlowJo, there is a new interface for viewing compensation matrices – the Matrix Editor. This page describes the Matrix Editor. The matrix editor window can be accessed in two ways: 1) by double-clicking the compensation badge from the workspace window (any compensated sample has this badge next to the sample in… Read more »