Flow cytometry (FCM) is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles. It is commonly used for cancer diagnosis and treatment monitoring along with other areas like drug discovery and stem cell research. The manual analysis of flow cytometry data is currently the biggest hurdle in the application of the technology. It is not only subjective and time consuming, but the complexity of datasets has made it impossible to access the full amount of information embedded within them.
This project aimed to overcome these bottlenecks in cell analysis with the development of four software algorithms to address three separate challenges in automating analysis of flow cytometry data. Firstly ‘flowGraph’ identifies similar samples in large studies to better help understand the effects of treatment. Then ‘flowLearn’ automates the analysis of large numbers of samples using characteristics recorded by a human expert. Finally, an algorithm was developed to identify cells that are similar to each other so they can be counted together.
These algorithms help advance the technology in automating flow cytometry data analysis and address major challenges of flow cytometry. These tools have been made available to other researchers to use on their own data and have been applied independently as part of studies on cancer therapy clinical trials.