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. 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. 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.

Flowjo 10 not opening