Flow cytometry is an indispensable tool for interrogating the immune system in research, drug discovery and diagnostic applications. The platform has undergone tremendous expansion over the past decade and now, modern cytometers are able to analyze 10 to 40 parameters per single cell, for millions of cells per sample, and hundreds of samples per experiment. However, as the number of parameters, samples, and experiments increases, so too does the data analysis burden. Current flow cytometry software is unable to meet the needs of cutting-edge applications due to limitations in computational power and lack of advanced data visualization tools. In this proposal, we describe the further development of our cloud-based cytometry software that is built upon a proprietary distributed computing engine. Harnessing the power of elastic cloud computing, our solution can analyze data faster than all currently available applications, and can scale to handle even the most massive data sets. In this grant, we will further harden core features such as gating and compensation, implement data visualization tools that are directly connected to the raw data, and enable analysis across multiple experiments. The result of our work will be an end-to-end solution for advanced flow cytometry analysis, built upon a scalable, secure, and distributed cloud architecture that can meet the demands of current, as well as future, researchers.
The goal of this grant proposal is to develop a web-based tool for analyzing flow cytometry data. Flow cytometry is used by researchers to measure the impact of drugs, monitor patients in clinical trials, and characterize diseases. Our software platform will enable researchers to analyze their data faster, gain more insight, and collaborate more effectively.
