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The following code examples demonstrate part of such an R session using flowFlowJo to analyze a set of cytometric data.
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These objects can then be used in a more detailed event-level analysis than would be possible from simple summary statistics alone.įigure 1 illustrates how the major components of the flowFlowJo package are related in typical data analysis sessions. Obtain a set of ordered lists of FCS file paths, spillover matrices, and flowCore S4 filter objects identical with that created by the researcher using FlowJo.
Flowjo 10 sample name preference full#
The flowFlowJo package provides the ability to work with both the raw data and the gating information in a powerful analysis environment that makes full use of the existing open source community efforts. The goal of flowFlowJo is to make it easy, in R, to use compensation and gating information that has been produced using FlowJo. The package flowFlowJo can produce R data structures with either summary statistics or fully flowCore compliant objects representing the various gates, compensation matrices, and other related information embedded in FlowJo sessions. We chose to use FlowJo because it is amongst the most commonly used flow cytometry programs and it stores its session information in an open format. To address this problem, we have built a package that provides a way to extract data from one such commercial package, FlowJo ( ), into the publicly accessible analysis platform R/Bioconductor.
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This becomes problematic when dealing with complex problems and large data sets.
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Commonly these gates are defined in a commercial flow cytometry analysis package that is used, along with “cut-and-paste” and simple analysis packages such as Excel or Prism, to provide results. In the analysis of flow cytometry data it is important to be able to work with the gates that have been manually defined. These packages which include flowCore, flowQ, flowViz, flowUtil, flowStats, flowClust and others all operate on a common set of core methods and classes for reading, transforming, gating and otherwise manipulating flow cytometry data. Similar to tools developed for microarrays, a set of packages is evolving in the Bioconductor community that holds great promise for flow cytometry data analysis. The first two of these tasks tend to be application- and lab-specific, while the latter two lend themselves well to the development of shared tools for all those faced with complex flow cytometry analyses. All of these components are related and, done well, serve to reinforce each other. (1) acquisition of high-quality data, (2) tools for data organization, annotation, and query, (3) tools for data manipulation, and (4) techniques and statistical methods for data analysis. There are a number of challenges associated with the analysis of these large, complex flow cytometry data sets. Powerful analysis tools are needed to properly explore and analyze data sets in which each sample has many stimuli, cell subpopulations, and phosphoprotein measurements. This adds another layer of complexity to flow cytometry data sets. There is also a growing appreciation that it is important to assess cells not only in their quiescent state, but also in response to various stimuli. Recent advances in instrumentation such as 4 and 5 color laser systems and the availability of reagents and protocols for assessing internal proteins and their phosphorylation state are serving to make flow cytometry a very important tool for understanding disease processes in human biology. Flow cytometers measure individual cells, and thus are capable of revealing subtleties of biology that other technologies cannot detect. Flow cytometry is a high-information content platform that is increasingly becoming a high-throughput platform as well.