Haematology dimension reduction, a large scale application to regular care haematology data

Huibert-Jan Joosse, Chontira Chumsaeng, Albert Huisman, Imo Hoefer, Wouter W van Solinge, Saskia Haitjema, Bram van Es
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Abstract

Background: The routine diagnostic process increasingly entails the processing of high-volume and high-dimensional data. This processing may provide scaling issues that limit the implementation of these types of data into research as well as integrated diagnostics in routine care. Here, we investigate whether we can use existing dimension reduction techniques to provide visualisations and analyses for a complete bloodcount (CBC) while maintaining representativeness of the original data. We considered over 3 million CBC measurements encompassing over 70 parameters of cell frequency, size and complexity from the UMC Utrecht UPOD database. We evaluated PCA as an example of a linear dimension reduction techniques and UMAP, TriMap and PaCMAP as non-linear dimension reduction techniques. We assessed their technical performance using quality metrics for dimension reduction as well as biological representation by evaluating preservation of diurnal, age and sex patterns, cluster preservation and the identification of leukemia patients. Results: We found that PCA performs systematically better than the UMAP, TriMap and PaCMAP in representing the underlying data. Biological relevance was retained for periodicity in the data. However, we also observed a decrease in predictive performance of the reduced data for both age and sex, as well as an overestimation of clusters within the reduced data. Finally, we were able to identify the diverging patterns for leukemia patients after use of dimensionality reduction methods. Conclusions: We conclude that for hematology data, the use of unsupervised dimension reduction techniques should be limited to data visualization applications, as implementing them in diagnostic pipelines may lead to decreased quality of integrated diagnostics in routine care.
血液学降维,常规护理血液学数据的大规模应用
背景:常规诊断过程越来越多地需要处理大量高维数据。这种处理方式可能会产生缩放问题,从而限制将这些类型的数据应用于研究以及常规护理中的综合诊断。在此,我们研究了能否利用现有的降维技术为全血细胞计数(CBC)提供可视化和分析,同时保持原始数据的代表性。我们考虑了乌得勒支大学医学中心 UPOD 数据库中的 300 多万次 CBC 测量,其中包括 70 多个细胞频率、大小和复杂性参数。我们评估了作为线性降维技术范例的 PCA,以及作为非线性降维技术的 UMAP、TriMap 和 PaCMAP。我们使用降维质量指标评估了它们的技术性能,并通过评估昼夜、年龄和性别模式的保留情况、聚类保留情况以及白血病患者的识别情况,评估了它们的生物代表性。结果:我们发现,在表示基础数据方面,PCA 的表现明显优于 UMAP、TriMap 和 PaCMAP。数据的周期性保留了生物学相关性。不过,我们也观察到,缩减后的数据对年龄和性别的预测性能都有所下降,而且缩减后的数据中的聚类也被高估了。最后,在使用降维方法后,我们能够识别白血病患者的分化模式。结论我们得出的结论是,对于血液学数据,无监督降维技术的使用应仅限于数据可视化应用,因为在诊断管道中使用这些技术可能会导致常规护理中的综合诊断质量下降。
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