35,172 research outputs found
Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)
Background: When conducting a meta-analysis of a continuous outcome,
estimated means and standard deviations from the selected studies are required
in order to obtain an overall estimate of the mean effect and its confidence
interval. If these quantities are not directly reported in the publications,
they need to must be estimated from other reported summary statistics, such as
the median, the minimum, the maximum, and quartiles. Methods: We propose a
simulation-based estimation approach using the Approximate Bayesian Computation
(ABC) technique for estimating mean and standard deviation based on various
sets of summary statistics found in published studies. We conduct a simulation
study to compare the proposed ABC method with the existing methods of Hozo et
al. (2005), Bland (2015), and Wan et al. (2014). Results: In the estimation of
the standard deviation, our ABC method performs best in skewed or heavy-tailed
distributions. The average relative error (ARE) approaches zero as sample size
increases. In the normal distribution, our ABC performs well. However, the Wan
et al. method is best since it is based on the normal distribution assumption.
When the distribution is skewed or heavy-tailed, the ARE of Wan et al. moves
away from zero even as sample size increases. In the estimation of the mean,
our ABC method is best since the AREs converge to zero. Conclusion: ABC is a
flexible method for estimating the study-specific mean and standard deviation
for meta-analysis, especially with underlying skewed or heavy-tailed
distributions. The ABC method can be applied using other reported summary
statistics such as the posterior mean and 95% credible interval when Bayesian
analysis has been employed
Phase diagram of a 2D Ising model within a nonextensive approach
In this work we report Monte Carlo simulations of a 2D Ising model, in which
the statistics of the Metropolis algorithm is replaced by the nonextensive one.
We compute the magnetization and show that phase transitions are present for
. A phase diagram (critical temperature vs. the entropic
parameter ) is built and exhibits some interesting features, such as phases
which are governed by the value of the entropic index . It is shown that
such phases favors some energy levels of magnetization states. It is also
showed that the contribution of the Tsallis cutoff is essential to the
existence of phase transitions
Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome
Escherichia coli has long been regarded as a model organism in the study of codon usage bias (CUB). However, most studies in this organism regarding this topic have been computational or, when experimental, restricted to small datasets; particularly poor attention has been given to genes with low CUB. In this work, correspondence analysis on codon usage is used to classify E.coli genes into three groups, and the relationship between them and expression levels from microarray experiments is studied. These groups are: group 1, highly biased genes; group 2, moderately biased genes; and group 3, AT-rich genes with low CUB. It is shown that, surprisingly, there is a negative correlation between codon bias and expression levels for group 3 genes, i.e. genes with extremely low codon adaptation index (CAI) values are highly expressed, while group 2 show the lowest average expression levels and group 1 show the usual expected positive correlation between CAI and expression. This trend is maintained over all functional gene groups, seeming to contradict the E.coli–yeast paradigm on CUB. It is argued that these findings are still compatible with the mutation–selection balance hypothesis of codon usage and that E.coli genes form a dynamic system shaped by these factors
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