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

MOTIVATION Within any given tissue, gene expression levels can vary extensively among individuals. Such heterogeneity can be caused by genetic and epigenetic variability and may contribute to disease. 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 characterizing differential expression by comparing mean expression profiles across tissues, it is believed that a significant difference in a gene expression's variance across tissues may also be associated with molecular mechanisms that are important for tissue development and function. RESULTS We propose a sparse multi-view matrix factorization (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 obtained from a large and well-phenotyped twins cohort, TwinsUK. Using sMVMF, we are able to prioritize 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. AVAILABILITY AND IMPLEMENTATION Python code is available at http://wwwf.imperial.ac.uk/~gmontana/. CONTACT giovanni.montana@kcl.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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