Sparse multi-view matrix factorisation：多组织比较的多变量方法
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
摘要：
群体中的基因表达水平在不同组织间差异很大。这种异质性是由遗传变异性和环境因素引起的，预计与疾病的发展有关。丰富的实验数据现在能够识别跨组织共享的基因表达谱的特征，以及那些组织特异性的特征。虽然目前大多数研究关注的是通过比较组织间的平均表达谱来表征差异表达，但也认为组织间基因表达差异的显著差异也可能与对组织发育和功能重要的分子机制相关。我们提出了一种稀疏多视图矩阵分解（sMVMF）算法，以联合分析多个组织中的基因表达测量，其中每个组织提供了不同的“视图”的基本生物体。所提出的方法可以被解释为主成分分析的扩展，因为它提供了将每个组织中的总样本方差分解为两个分量之和的方法：一个捕获跨组织共享的方差，另一个分离组织特异性方差。sMVMF已用于联合建模三种组织（脂肪、皮肤和LCL）中的mRNA表达谱，这些组织可用于大型且表型良好的双胞胎队列TwinsUK。使用sMVMF，我们能够根据基因的变异模式是否特定于每个组织来优先考虑基因。此外，利用DNA甲基化图谱，我们提供了支持性证据，即脂肪特异性基因表达模式可能是由表观遗传效应驱动的。
英文摘要：
Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked to disease development. The abundance of experimental data now enables the identification of features of gene expression profiles that are shared across tissues, and those that are tissue-specific. While most current research is concerned with characterising differential expression by comparing mean expression profiles across tissues, it is also believed that a significant difference in a gene expression's variance across tissues may also be associated to molecular mechanisms that are important for tissue development and function. We propose a sparse multi-view matrix factorisation (sMVMF) algorithm to jointly analyse gene expression measurements in multiple tissues, where each tissue provides a different "view" of the underlying organism. The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances. sMVMF has been used to jointly model mRNA expression profiles in three tissues - adipose, skin and LCL - which are available for a large and well-phenotyped twins cohort, TwinsUK. Using sMVMF, we are able to prioritise genes based on whether their variation patterns are specific to each tissue. Furthermore, using DNA methylation profiles available, we provide supporting evidence that adipose-specific gene expression patterns may be driven by epigenetic effects.
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