My group is focused on applying bioinformatics techniques to flow cytometry data. Flow cytometry is a technique that is widely used within the biomedical community. New high throughput methods can generate up to a thousand flow cytometry data files per day and each data file can consist of millions of multiparametric descriptions of individual cells. However flow cytometry lacks data standards for fully representing both the methods used and the data generated, hindering the handling, exchange and dissemination of scientific research. Consequently, there are a variety of challenges to archiving, analyzing and reporting the results of high throughput flow cytometry experiments. Furthermore, only rudimentary bioinformatics tools exist to manage and mine high throughput data. We are leading an international effort to develop a systemic approach to modeling, capturing, analyzing and disseminating flow cytometry data. Initial work is focused on developing a community-based standard for recording and reporting flow cytometry data. Software implementations of this standard (UML, XML, SQL, Java) and an ontology (Ontology for Biomedical Investigations) being created to facilitate data exchange between both software components and collaborative groups. Statistics packages (e.g., flowCore/flowUtils/flowClust) are also being developed to allow users to implement analyses in a high throughput fashion, as well as exchange these analyses in more meaningful ways then are currently available. We are also testing our high throughput analysis methods to analyze flow cytometry data on datasets from the British Columbia Cancer Agency and the British Children's Hospital including developing methods for the high throughput analysis of lymphoma, Graft versus Host Disease and innate immunity. Some of these data sets are also available as part of the flowCAP project. We are also developing user-friendly software that implements some of the new data analysis and visualization methods we and others have developed. My research is supported by the NIH, CIHR, Genome Canada, Genome BC, The Michael Smith Foundation for Health Research and the BC Cancer Agency.