Simultaneous dimension reduction and adjustment for confounding variation
Simultaneous dimension reduction and adjustment for confounding variation
复制标题
同时降维和调整混杂变化
DOI：
10.1073/pnas.1617317113
复制
发表时间：
2016-12-20
影响因子：
11.1
通讯作者：
Wong, Wing Hung
中科院分区：
文献类型：
--
作者：
Lin, Zhixiang;Yang, Can;Wong, Wing Hung
意义随着高通量技术的进步，分析高维数据已成为一项常见的任务。降维方法已被应用于可视化和识别高维数据中的主导模式。在高通量生物实验中经常观察到的混杂因素会影响这些方法和其他下游分析的性能。在这里，我们发展了一种降维与混杂效应调整相结合的方法。我们的方法能够捕获潜在的模式，如人类大脑外显子阵列数据集、模型生物ENCODE RNA测序数据集和模拟所展示的那样。降维方法通常应用于高通量的生物数据集。然而，结果可能会受到混杂因素的阻碍，无论是生物因素还是技术因素。在这项研究中，我们扩展了主成分分析(PCA)，提出了AC-PCA用于同时降维和调整混杂(AC)变异。我们表明，AC-PCA可以调整(I)存在于人脑外显子阵列数据集中的个体供体之间的变异，以及(Ii)模式生物ENCODE RNA测序数据集中不同物种的变异。我们的方法能够恢复新皮质区域的解剖结构，并捕捉到胚胎发育过程中物种之间的共同变异。针对基因选择问题，我们对AC-PCA算法进行了稀疏约束扩展，提出并实现了一种高效的算法。本文提出的方法也可应用于更一般的设置。R包和matlab源代码可在https://github.com/linzx06/AC-PCA.上获得
Significance With the advancement in high-throughput technologies, analyzing high-dimensional data has become a common task. Dimension reduction methods have been applied to visualize and identify dominant patterns in high-dimensional data. Confounding factors, commonly observed in high-throughput biological experiments, can affect the performance of these methods, and other downstream analysis. Here, we develop a method by coupling dimension reduction with the adjustment for confounder effects. Our method is able to capture the underlying patterns, as demonstrated by a human brain exon array dataset, a model organism ENCODE RNA sequencing dataset, and simulations. Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing dataset. Our approach is able to recover the anatomical structure of neocortical regions and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA.