Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons

TL;DR

This paper introduces a sparse multi-view matrix factorisation method to analyze gene expression across multiple tissues, decomposing variance into shared and tissue-specific components, and linking gene variation to epigenetic factors.

Contribution

The paper presents a novel sMVMF algorithm that extends PCA to jointly analyze multi-tissue gene expression data, identifying tissue-specific and shared variance components.

Findings

Successfully modeled gene expression in three tissues using sMVMF

Prioritized tissue-specific genes based on variation patterns

Linked adipose-specific gene expression to epigenetic effects

Abstract

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.