We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.

中文翻译：

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流式细胞仪数据中最大差异细胞群的自动识别

我们引入了一种称为 SpecEnr（特异性富集）的新细胞群评分，并描述了一种从流式细胞术数据中发现稳健且准确的候选生物标志物的方法。我们的方法确定了一类新的候选生物标志物，我们将其定义为驱动细胞群体，其丰度与样本类别（例如疾病）相关，但不是相关群体变化的结果。我们表明，我们发现的驱动细胞群也可以使用基于晶格的可视化工具轻松解释。我们的方法在 R 包 flowGraph 中实现，可在 GitHub (github.com/aya49/flowGraph) 和 BioConductor 上免费获得。