10,494 research outputs found
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Phylogenetic comparative analysis is an approach to inferring evolutionary
process from a combination of phylogenetic and phenotypic data. The last few
years have seen increasingly sophisticated models employed in the evaluation of
more and more detailed evolutionary hypotheses, including adaptive hypotheses
with multiple selective optima and hypotheses with rate variation within and
across lineages. The statistical performance of these sophisticated models has
received relatively little systematic attention, however. We conducted an
extensive simulation study to quantify the statistical properties of a class of
models toward the simpler end of the spectrum that model phenotypic evolution
using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and
why these methods break down so that users can apply them with greater
understanding of their strengths and weaknesses. Our analysis identifies three
key determinants of performance: a discriminability ratio, a signal-to-noise
ratio, and the number of taxa sampled. Interestingly, we find that
model-selection power can be high even in regions that were previously thought
to be difficult, such as when tree size is small. On the other hand, we find
that model parameters are in many circumstances difficult to estimate
accurately, indicating a relative paucity of information in the data relative
to these parameters. Nevertheless, we note that accurate model selection is
often possible when parameters are only weakly identified. Our results have
implications for more sophisticated methods inasmuch as the latter are
generalizations of the case we study.Comment: 38 pages, in press at Systematic Biolog
Differential expression analysis with global network adjustment
<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p>
<p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p>
<p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p>
A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters
This paper proposes a hierarchical, multi-resolution framework for the
identification of model parameters and their spatially variability from noisy
measurements of the response or output. Such parameters are frequently
encountered in PDE-based models and correspond to quantities such as density or
pressure fields, elasto-plastic moduli and internal variables in solid
mechanics, conductivity fields in heat diffusion problems, permeability fields
in fluid flow through porous media etc. The proposed model has all the
advantages of traditional Bayesian formulations such as the ability to produce
measures of confidence for the inferences made and providing not only
predictive estimates but also quantitative measures of the predictive
uncertainty. In contrast to existing approaches it utilizes a parsimonious,
non-parametric formulation that favors sparse representations and whose
complexity can be determined from the data. The proposed framework in
non-intrusive and makes use of a sequence of forward solvers operating at
various resolutions. As a result, inexpensive, coarse solvers are used to
identify the most salient features of the unknown field(s) which are
subsequently enriched by invoking solvers operating at finer resolutions. This
leads to significant computational savings particularly in problems involving
computationally demanding forward models but also improvements in accuracy. It
is based on a novel, adaptive scheme based on Sequential Monte Carlo sampling
which is embarrassingly parallelizable and circumvents issues with slow mixing
encountered in Markov Chain Monte Carlo schemes
Quantifying the behavioural relevance of hippocampal neurogenesis
Few studies that examine the neurogenesis--behaviour relationship formally
establish covariation between neurogenesis and behaviour or rule out competing
explanations. The behavioural relevance of neurogenesis might therefore be
overestimated if other mechanisms account for some, or even all, of the
experimental effects. A systematic review of the literature was conducted and
the data reanalysed using causal mediation analysis, which can estimate the
behavioural contribution of new hippocampal neurons separately from other
mechanisms that might be operating. Results from eleven eligible individual
studies were then combined in a meta-analysis to increase precision
(representing data from 215 animals) and showed that neurogenesis made a
negligible contribution to behaviour (standarised effect = 0.15; 95% CI = -0.04
to 0.34; p = 0.128); other mechanisms accounted for the majority of
experimental effects (standardised effect = 1.06; 95% CI = 0.74 to 1.38; p =
1.7 ).Comment: To be published in PLoS ON
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