Automated analysis of flow cytometry data: a systematic review of recent methods

Flow cytometry (FCM) is a very well-known method that is broadly used in clinical and research laboratories. Both clinical and research laboratories have been the target domains of FCM applications. The key research question in this particular field is “how to effectively automate FCM data analysis?”. To answer this question, this paper systematically reviews current advances in the automation of FCM data analysis. All recent techniques have been studied in a way readers can recognize current trends, challenges, limitations and future directions. For future research, we have identified three main venues. First, the identification of the number of clusters prior to starting cell population identification is still a challenging process. Second, automating the process of cluster labeling still requires more improvement to be fully automated. Last, benchmark datasets are essential in order for researchers to be able to comparatively evaluate different techniques of FCM data analysis under fixed conditions.We end up this paper with a discussion about how flow cytometry data analysis techniques and datasets are correlated with open source technology.