FlowCAP - Flow Cytometry: Critical Assessment of Population Identification Methods

Flow cytometry has been widely used by immunologists and cancer biologists for more than 30 years as a biomedical research tool to distinguish different cell types in mixed populations based on the expression of cellular markers. It has also become a widely used diagnostic tool for clinicians to identify abnormal cell populations associated with disease. In the last decade, advances in instrumentation and reagent technologies have enabled simultaneous single-cell measurement of tens of surface and intracellular markers, as well as tens of signaling molecules, positioning flow cytometry to play an even bigger role in medicine and systems biology. However, the rapid expansion of flow cytometry applications has outpaced the functionality of traditional analysis tools used to interpret flow cytometry data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For these reasons a reliable automated approach to flow cytometric analysis is desirable. While there has been a growing interest among the scientific community in developing these methods, guidance for end users about appropriate use and application of these methods is scarce.

In response to this need, we are pleased to announce the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) project. The goal of FlowCAP is to advance the development of computational methods for the identification of cell populations of interest in flow cytometry data. FlowCAP will provide the means to objectively test these methods, first by comparison to manual analysis by experts using common datasets, and second by comparison to synthetic data sets having known properties.

FlowCAP will consist of three parts:

1) The collection of de-identified data sets for prediction from the experimental community that will be shared among the algorithm development community as a common reference for analysis;
2) The collection of population subset predictions (gates) from the computational biology community derived from these common reference data sets using existing and novel algorithmic approaches; and
3) The assessment and discussion of the results in comparison with the manual gating gold standard.