Flow Cytometry Bioinformatics
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. Consequently, there are a variety of challenges to archiving, analyzing and reporting the results of high throughput flow cytometry experiments. My group is developing big data analysis algorithms, including machine learning approaches, 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 BC Cancer and the British Children's Hospital including developing methods for the high throughput analysis of lymphoma, Graft versus Host Disease and innate immunity. We are also developing user-friendly software that implements some of the new data analysis and visualization methods we and others have developed. We also collaborate with big pharma to analyze (primarily immunotherapy) clinical trial data.
My research is supported by the NIH, CIHR, Genome Canada, Genome BC, The Michael Smith Foundation for Health Research and BC Cancer.