Quality Controlling Your Flow Cytometry Data for Improved Experimental Outcomes

High-dimensional flow cytometry generates a wealth of data. Computational approaches can help to simplify the complexity of large datasets; however these methods must be carefully optimized, and it is imperative that the original data be controlled for quality to avoid bias in the results. During this webinar, we will discuss potential pitfalls encountered during computational analysis of high-dimensional flow cytometry and provide possible solutions. This webinar is directed to those users with basic to medium experience in flow cytometry who may be approaching computational tools for the first time.