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. This becomes problematic when dealing with complex problems and large data sets. 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. Introductionįlow cytometry is a high-information content platform that is increasingly becoming a high-throughput platform as well. We present this package and illustrate some of the ways in which it can be used. To provide this capability, we have developed a Bioconductor package called flowFlowJo that can import gates defined by the commercial package FlowJo and work with them in a manner consistent with the other flow packages in Bioconductor.
#EXPORTING DATA FLOWJO 10 MANUAL#
The ability to retrieve the results and work with both them and the raw data is critical our experience points to the importance of bioinformatics tools that will allow us to examine gating robustness, combine manual and automated gating, and perform exploratory data analysis. This is easily accomplished in commercial flow cytometry packages but it is difficult to work computationally with the results of this process.
#EXPORTING DATA FLOWJO 10 SERIES#
In flow cytometry, different cell types are usually selected or “gated” by a series of 1- or 2-dimensional geometric subsets of the measurements made on each cell.
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