What does FlowJo do?

FlowJo should be the first step in the process of analyzing flow cytometry data.  FlowJo can analyze data generated by any Flow Cytometer from any manufacturer.  FlowJo has a number of different analysis platforms that let you not only perform standard analyses such as gating and statistics, but also specialized analyses such as DNA/Cell Cycle, Kinetics (Calcium flux), Proliferation, Calibration, and Statistical Comparison.  You may find that FlowJo's sophisticated tools for generating output (graphical or tabular) are sufficient for you to generate publication-quality material.... but if not, FlowJo makes it easy for you to copy any graphs, statistics, or other information into other programs for further analysis and presentation.

Experiments and workspaces

FlowJo is a program designed to analyze flow cytometry data. The basic concept behind this analysis is that of the "experiment." An experiment is a collection of samples which have a set of common attributes;  for instance, there are sets of tubes stained with the same antibodies, other sets of tubes which come from the same tissue sources, etc. An experiment can be a single collection of samples, or it can stretch across multiple runs over a period of months.

With FlowJo, you will organize the samples in a workspace. A Workspace is similar to a notebook: it references every sample that you are analyzing, and records the analyses (gates, statistics, graphs, tables) that you have done. You can close the workspace, and then reopen in the future and start where you left off. You can have as many workspaces as you want; the organization is up to you. We recommend that you have at least one workspace for every experiment.

Within a workspace, you can group samples by various attributes. For instance, you can make a group of all samples derived from a single individual (which may have different stains); you can also make groups of all samples with the same stains (which come from different individuals). Groups are really the powerful feature of FlowJo: when you perform an operation on a group, it performs the operation on every sample belonging to that group. Thus, you can apply a gate or a statistic to a group, and that gate or analysis will be automatically performed on all samples!

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Analyzing with FlowJo

You will find that your mode of operating FlowJo will probably be very similar to the following series of steps:

1. Load the samples into a workspace.
2. Group samples into a variety of different groups
3. Analyze a single ("prototype") sample in detail. Decide on gates, statistics, etc.
4. Apply the appropriate analyses to all the samples in a group.
5. Quickly check the samples in the group to make sure the gates are acceptable: they may need minor modifications to accommodate sample to sample variation.
6. Generate a graphical layout in which you can display particular graphs from all samples.
7. Generate a table in which you can generate particular statistics from all samples.

You can then save this workspace as a template... Next week, when you do another (similar) experiment, most of the work is already done for you! You simply have to import the new samples into the template workspace you created before:  all of the data files will be added to the appropriate groups, which means all of your previous analyses (gates & statistics, etc.) will be automatically applied to each sample. You need only regenerate the graphical layouts and the tables.

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The Gating Hierarchy

Another fundamental concept in FlowJo is that of the gating hierarchy. When you first generate a gate on a sample (for instance, a lymphocyte gate), FlowJo shows you this gate (subset) in the paradigm of a genealogical tree. In other words, the "Lymphocyte" subset is a "child" of the parent sample. It is shown in the workspace indented a single level from the sample, underneath the sample, to denote this. If you were to make another gate on the sample (for instance, monocytes), then FlowJo creates another new subset as another child of the sample; now, the lymphocyte and monocyte subsets (or gates) are siblings.

Any operation that you can perform on a sample can also be performed on a subset of the sample. Therefore, you can view the lymphocyte subset and create gates (subsets) of this population. If you create a T cell gate within lymphocytes, then the T cell subset becomes a child of the lymphocytes (and, by extension, a grandchild of the sample).

This paradigm is very important to the way in which FlowJo operates. When you copy analyses like gates, you can choose to copy only the single gate that you click on, your you can choose to copy it with all of its children (i.e., copy the entire analysis tree). As well, you can copy the gate with its parents. Remember, any subpopulation is a single gate. When you want to recreate the same subpopulation on another sample, you need to carry all of the gates (all of the ancestors of the subset) with the subpopulation when you copy it.

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Batch operations

Batch operations (repetitive analyses performed on multiple samples) are very simple in FlowJo. In general, batch operations are performed on all samples in the current group (therefore, you will want to create sample groups that will serve to help you generate batch outputs). Batch operations include copying gates and analyses, generating graphical layouts, and generating tables. In addition, there are some minor operations like opening a graph of the same subset as you are viewing for all samples in the group, unifying the analyses across samples within a group, deleting analyses, and so forth.

In this way, FlowJo is a program that doesn't just analyze individual data samples. Rather, the focus is on analyzing groups of samples, experiments, or even multiple experiments at once.

Compensation

In the world of increasing fluorescence parameters, it has become impossible to fully compensate samples at the time of collection. FlowJo provides an interface for computing the compensation matrix based on the collection of singly-stained samples. You can then apply this compensation matrix to samples that are uncompensated. At this point, you can view and analyze the compensated data.

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Derived Parameters

FlowJo lets you define new parameters for samples. These include the ability to add Time as a parameter (for performing kinetics analyses), to compute the ratio of two collected parameters, or to convert between log and linear scaling. Derived parameter definitions can be copied to entire groups of samples.

Kinetics (Ca++ flux) analyses

FlowJo provides a sophisticated platform designed to analyze kinetics data. The sample must have a parameter which corresponds to time (and is named "Time"); if it does not, FlowJo allows you to create a time parameter (assuming a constant event rate). From this platform, you can compute the maximal response time, the slope of a response, the fraction of responding cells, etc.

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Cell Cycle Analysis

The Cell Cycle Platform is easy to use, yet can view several different models simultaneously, constrain fitting parameters, and automatically calculate the percentage of cells in G1, S, or G2 peaks.  Fitting can be constrained in a number of ways, letting you generate reasonable interpretations of even very unusual cell cycle distributions.  All models can be copied between populations or samples, with the same easy drag and drop interface used to propagate other gates or analyses.

Calibration (Quantitation)

FlowJo has a unique Calibration Platform that allows you to calibrate any collected parameter. Most commonly, this is used to convert the scaling into absolute number of molecules (given a standard that converts between the fluorescence intensity collected on your instrument and absolute numbers of fluorophores). The platform can use a calibrated bead set as a standard, a stained sample as a reference, or numbers that you enter to define the calibration manually.

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Proliferation Analysis

The Proliferation Platform is used to model proliferation data obtained using cell tracking dyes such as CFSE. FlowJo presents a graphical display as well as information about each generation in the subset. The proliferation platform also provides information about the fraction of cells from the original population that have divided, and the number of times these cells have divided. In addition the FlowJo Proliferation Platform draws gates that separate each generation.

Population Comparison

The Population Comparison Platforms allow the direct comparison between different samples or subsets. FlowJo can take two or more samples and analyze them to determine how different the distributions are. Comparisons can be univariate or multivariate, and you can rank multiple samples by their similarity to controls. These platforms take advantage of the exciting new comparison algorithm - Probability Binning. This algorithm even allows the creation of a gate to display the difference between two populations.

Movies

Movies.  An analysis platform unique to FlowJo:  view your data dynamically.  Use the Movie Platform to generate graphs as a function of time (kinetic analyses), or to generate a graph of one or two parameters as a function of a third.  This unique visualization lets you uncover subtle relationships in your data that would be impossible to see otherwise.

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