214 research outputs found
Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within
biological pathways, the incorporation of prior pathways information into a
statistical model is expected to increase the power to detect true associations
in a genetic association study. Most existing pathways-based methods rely on
marginal SNP statistics and do not fully exploit the dependence patterns among
SNPs within pathways. We use a sparse regression model, with SNPs grouped into
pathways, to identify causal pathways associated with a quantitative trait.
Notable features of our "pathways group lasso with adaptive weights" (P-GLAW)
algorithm include the incorporation of all pathways in a single regression
model, an adaptive pathway weighting procedure that accounts for factors
biasing pathway selection, and the use of a bootstrap sampling procedure for
the ranking of important pathways. P-GLAW takes account of the presence of
overlapping pathways and uses a novel combination of techniques to optimise
model estimation, making it fast to run, even on whole genome datasets. In a
comparison study with an alternative pathways method based on univariate SNP
statistics, our method demonstrates high sensitivity and specificity for the
detection of important pathways, showing the greatest relative gains in
performance where marginal SNP effect sizes are small.Comment: 29 page
Deep Neural Networks for Anatomical Brain Segmentation
We present a novel approach to automatically segment magnetic resonance (MR)
images of the human brain into anatomical regions. Our methodology is based on
a deep artificial neural network that assigns each voxel in an MR image of the
brain to its corresponding anatomical region. The inputs of the network capture
information at different scales around the voxel of interest: 3D and orthogonal
2D intensity patches capture the local spatial context while large, compressed
2D orthogonal patches and distances to the regional centroids enforce global
spatial consistency. Contrary to commonly used segmentation methods, our
technique does not require any non-linear registration of the MR images. To
benchmark our model, we used the dataset provided for the MICCAI 2012 challenge
on multi-atlas labelling, which consists of 35 manually segmented MR images of
the brain. We obtained competitive results (mean dice coefficient 0.725, error
rate 0.163) showing the potential of our approach. To our knowledge, our
technique is the first to tackle the anatomical segmentation of the whole brain
using deep neural networks
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
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.Comment: in Bioinformatics 201
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