Critical assessment of automated flow cytometry data analysis techniques

TLDR

Traditional flow cytometry data processing relies on subjective manual gating, but recent computational methods have been developed to identify cell populations in multidimensional data. The study aimed to compare automated flow cytometry data processing algorithms to manual gating by using the FlowCAP challenges to assess population identification and sample classification performance. The authors used the FlowCAP challenges, which involve mammalian cell population identification and sample classification, to evaluate algorithm performance against manual gating and external variables. The first FlowCAP challenges showed that several algorithms performed comparably to manual gating and could identify characteristics linked to external variables, indicating that automated methods have reached a mature level of accuracy for reliable use in FCM data analysis.

Abstract

In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.