Abstract
A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.
Highlights
Studies focusing on rare cell populations are becoming increasingly common owing to technological advances such as high-speed, multi-parametric flow cytometry, and emerging biomedical applications like stem cell therapy, and single cell analysis
We developed a new computational framework FLARE for FLow cytometric Analysis of Rare Events, it may be applicable to other platforms that generate multi-marker data per cell
FLARE is based on a hierarchical Bayesian model, and employs parallel computation for its high-speed high-precision analysis
Summary
Studies focusing on rare cell populations are becoming increasingly common owing to technological advances such as high-speed, multi-parametric flow cytometry, and emerging biomedical applications like stem cell therapy, and single cell analysis. We describe the our new hierarchical Bayesian model, FLARE, for FLow cytometric Analysis of Rare Events, to identify cell populations from multiple samples and detecting rare cell populations. We developed a new computational framework FLARE for FLow cytometric Analysis of Rare Events, it may be applicable to other platforms that generate multi-marker data per cell.
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