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This page describes some of the basic concepts underlying FlowJo; it is meant just to help you familiarize yourself with some of the terminology and the features of FlowJo. As you navigate through the main documentation, you will get more details about all of these topics. In addition, you can follow links to some special topics regarding FlowJo: The all-important Credits page, where we have the opportunity to thank the many individuals who have spent much time and effort helping us make FlowJo the most sophisticated analysis package around, a "Frequently-Asked Questions" page (FAQ), listing some common questions & answers, a page about getting Help from within FlowJo itself, and a page detailing the history of revisions to FlowJo. 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 and Kinetics. 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! Analyzing with FlowJo You will find that your mode of operating FlowJo will probably be very similar to the following series of steps:
You can then save this workspace... 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 same workspace as you were using 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. 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. 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. Derived Parameters FlowJo lets you define new parameters for samples. These include the ability to add Time as a parameter (for performing kinetics analyses, or just to see how stable a sample was during the collection period), 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 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. 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. |
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