  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.
