Computational prediction of manually gated rare cells in flow cytometry data.
Computational prediction of manually gated rare cells in flow cytometry data.
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流式细胞术数据中手动门控稀有细胞的计算预测。
DOI：
10.1002/cyto.a.22654
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Qiu,Peng
中科院分区：
文献类型：
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作者：
Qiu,Peng
稀有细胞鉴定是流式细胞仪数据分析中一个有趣而富有挑战性的问题。在文献中，手动门控是一种流行的方法，用于提取流式细胞仪数据并深入到感兴趣的稀有细胞，基于测量的蛋白质标记物的先验知识和对数据的视觉检查。已经提出了几种稀有细胞识别的计算算法。为了比较现有算法并促进新的发展，FlowCAP-III提出了一个专注于这一问题的计算挑战。挑战提供了202个训练样本的流式细胞仪数据，以及每个训练样本的两种手动门控稀有细胞类型，分别约占细胞的0.02%和0.04%。此外，还提供了203个测试样本的流式细胞仪数据，并邀请参与者通过计算识别测试样本中的稀有细胞。通过与测试样品的手动选通进行比较，评估识别结果的准确性。我们参与了挑战，并开发了一种结合Hellinger散度、下采样技巧和集成支持向量机的方法。我们的方法在挑战中达到了最高的准确率。©2015国际细胞学促进会
Rare cell identification is an interesting and challenging question in flow cytometry data analysis. In the literature, manual gating is a popular approach to distill flow cytometry data and drill down to the rare cells of interest, based on prior knowledge of measured protein markers and visual inspection of the data. Several computational algorithms have been proposed for rare cell identification. To compare existing algorithms and promote new developments, FlowCAP‐III put forward one computational challenge that focused on this question. The challenge provided flow cytometry data for 202 training samples and two manually gated rare cell types for each training sample, roughly 0.02 and 0.04% of the cells, respectively. In addition, flow cytometry data for 203 testing samples were provided, and participants were invited to computationally identify the rare cells in the testing samples. Accuracy of the identification results was evaluated by comparing to manual gating of the testing samples. We participated in the challenge, and developed a method that combined the Hellinger divergence, a downsampling trick and the ensemble SVM. Our method achieved the highest accuracy in the challenge. © 2015 International Society for Advancement of Cytometry