Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
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DOI：
10.1007/s12561-021-09331-5
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发表时间：
2022-01-21
影响因子：
1
通讯作者：
Zhao, Hongyu
中科院分区：
文献类型：
--
作者：
Liu, Tianqi;Yuan, Ming;Zhao, Hongyu
人类大脑的时空基因表达数据提供了对大脑发育过程中基因调控的时空模式的见解。大多数现有的分析这些数据的方法分别考虑空间和时间分布，隐含的假设是，不同的大脑区域以相似的轨迹发展，并且基因表达的空间模式在不同的时间点保持相似。虽然这些分析可能有助于描绘基因调控空间或时间，他们不能表征异质性在不同的大脑区域的时间动态，或随着时间的推移基因调控的空间模式的演变。在这篇文章中，我们开发了一种基于低秩张量分解的统计方法，以更有效地分析时空基因表达数据。我们推广经典的主成分分析（PCA），这是只适用于数据矩阵，张量PCA，可以同时捕捉空间和时间的影响。我们还提出了一个有效的算法，结合张量展开和幂迭代，以有效地估计张量主成分，并提供保证其统计性能。数值实验进一步证明了所提出的方法的优点。我们的方法的时空大脑表达数据的应用提供了在大脑中的基因调控模式的见解。
Spatiotemporal gene expression data of the human brain offer insights on the spatial and temporal patterns of gene regulation during brain development. Most existing methods for analyzing these data consider spatial and temporal profiles separately, with the implicit assumption that different brain regions develop in similar trajectories, and that the spatial patterns of gene expression remain similar at different time points. Although these analyses may help delineate gene regulation either spatially or temporally, they are not able to characterize heterogeneity in temporal dynamics across different brain regions, or the evolution of spatial patterns of gene regulation over time. In this article, we develop a statistical method based on low-rank tensor decomposition to more effectively analyze spatiotemporal gene expression data. We generalize the classical principal component analysis (PCA), which is applicable only to data matrices, to tensor PCA that can simultaneously capture spatial and temporal effects. We also propose an efficient algorithm that combines tensor unfolding and power iteration to estimate the tensor principal components efficiently, and provide guarantees on their statistical performance. Numerical experiments are presented to further demonstrate the merits of the proposed method. An application our method to a spatiotemporal brain expression data provides insights on gene regulation patterns in the brain.