A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data
A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data
复制标题
一种数据驱动的生物医学数据模式挖掘降维算法
DOI：
10.1038/s41551-020-00635-3
复制
发表时间：
2020-11-02
影响因子：
28.1
通讯作者：
Xing, Lei
中科院分区：
文献类型：
--
作者：
Islam, Md Tauhidul;Xing, Lei
一种广泛适用的降维算法可以揭示一系列生物医学相关数据集的潜在趋势，降维算法被广泛应用于数据的可视化、压缩、探索和分类。然而，普遍适用的解决方案仍然没有。在这里，我们报告了一个准确的和广泛适用的数据驱动的降维算法。该算法被我们命名为“特征增强嵌入机”（FEM），首先学习数据的结构和数据成分的固有特征（如集中趋势和分散），对数据进行降噪，增加成分的分离，然后将数据投影到更低的维度上。我们表明，该技术在揭示蛋白质表达和单细胞RNA测序、计算机断层扫描、脑电图和可穿戴生理传感器数据集的潜在主导趋势方面是有效的。
A broadly applicable algorithm for dimensionality reduction can reveal underlying trends in a range of biomedically relevant datasets.Dimensionality reduction is widely used in the visualization, compression, exploration and classification of data. Yet a generally applicable solution remains unavailable. Here, we report an accurate and broadly applicable data-driven algorithm for dimensionality reduction. The algorithm, which we named 'feature-augmented embedding machine' (FEM), first learns the structure of the data and the inherent characteristics of the data components (such as central tendency and dispersion), denoises the data, increases the separation of the components, and then projects the data onto a lower number of dimensions. We show that the technique is effective at revealing the underlying dominant trends in datasets of protein expression and single-cell RNA sequencing, computed tomography, electroencephalography and wearable physiological sensors.