Abstract We propose a new method for identification and visualization of cell-sub groups in uncompensated multi-color flow cytometry data. The method combines annealing-based model-free expectation-maximization to identify cell sub-groups and joint diagonalization on clustered data for better visualization. The proposed method was evaluated on a real, publicly available 8-color flow cytometry dataset manually gated beforehand for lymphocytes. The results obtained in three separable scenarios indicate that the method accurately identifies cell subgroups while properly adjusting visualization of identified cell groups by reducing the spectral overlap between the different fluorochrome channels.

中文翻译：

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未补偿流式细胞术数据中细胞亚群的识别和可视化

摘要 我们提出了一种在未补偿的多色流式细胞术数据中识别和可视化细胞亚群的新方法。该方法结合了基于退火的无模型期望最大化来识别细胞子组和聚类数据上的联合对角化，以实现更好的可视化。所提出的方法是在一个真实的、公开可用的 8 色流式细胞术数据集上进行评估的，该数据集预先为淋巴细胞手动门控。在三个可分离场景中获得的结果表明，该方法可以准确识别细胞亚群，同时通过减少不同荧光染料通道之间的光谱重叠来适当调整已识别细胞群的可视化。