Insights Into Comparative Analyses and Phylogenomic Implications of Acer (Sapindaceae) Inferred From Complete Chloroplast Genomes

Acer L. (Sapindaceae) is one of the most diverse and widespread plant genera in the Northern Hemisphere. It comprises 124–156 recognized species, with approximately half being native to Asia. Owing to its numerous morphological features and hybridization, this genus is taxonomically and phylogenetically ranked as one of the most challenging plant taxa. Here, we report the complete chloroplast genome sequences of five Acer species and compare them with those of 43 published Acer species. The chloroplast genomes were 149,103–158,458 bp in length. We conducted a sliding window analysis to find three relatively highly variable regions (psbN-rps14, rpl32-trnL, and ycf1) with a high potential for developing practical genetic markers. A total of 76–103 SSR loci were identified in 48 Acer species. The positive selection analysis of Acer species chloroplast genes showed that two genes (psaI and psbK) were positively selected, implying that light level is a selection pressure for Acer species. Using Bayes empirical Bayes methods, we also identified that 20 cp gene sites have undergone positive selection, which might result from adaptation to specific ecological niches. In phylogenetic analysis, we have reconfirmed that Acer pictum subsp. mono and A. truncatum as sister species. Our results strongly support the sister relationships between sections Platanoidea and Macrantha and between sections Trifoliata and Pentaphylla. Moreover, series Glabra and Arguta are proposed to promote to the section level. The chloroplast genomic resources provided in this study assist taxonomic and phylogenomic resolution within Acer and the Sapindaceae family.

Estimating smooth and sparse neural receptive fields with a flexible spline basis

Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron. Typically, such STRFs are assumed to be smooth and sparse. Current state-of-the-art approaches for estimating STRFs based empirical Bayes estimation encode such prior knowledge into a prior covariance matrix, whose hyperparameters are learned from the data, and thus provide STRF estimates with the desired properties even with little or noisy data. However, empirical Bayes methods are often not computationally efficient in high-dimensional settings, as encountered in sensory neuroscience. Here we pursued an alternative approach and encode prior knowledge for estimation of STRFs by choosing a set of basis function with the desired properties: a natural cubic spline basis. Our method is computationally efficient, and can be easily applied to Linear-Gaussian and Linear-Nonlinear-Poisson models as well as more complicated Linear-Nonlinear-Linear-Nonlinear cascade model or spike-triggered clustering methods. We compared the performance of spline-based methods to no-spline ones on simulated and experimental data, showing that spline-based methods consistently outperformed the no-spline versions.

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

SummaryVariability in datasets are not only the product of biological processes: they are also the product of technical biases. ComBat is one of the most widely used tool for correcting those technical biases, called batch effects, in microarray expression data.In this technical note, we present a new Python implementation of ComBat. While the mathematical framework is strictly the same, we show here that our implementation: (i) has similar results in terms of batch effects correction; (ii) is as fast or faster than the R implementation of ComBat and; (iii) offers new tools for the bioinformatics community to participate in its development.Availability and ImplementationpyComBat is implemented in the Python language and is available under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license at https://github.com/epigenelabs/pyComBat.

Empirical Bayes Estimation of Treatment Effects with Many A/B Tests: An Overview

The use of large-scale experimentation to screen product innovations is increasingly common. This is a practical guide on how to use treatment effect estimates from a large number of experiments to improve estimates of the effects of each experiment. When thousands of new features are A/B tested by internet companies, the winners tend to be a combination of good features and features that got lucky experimental draws. Empirical Bayes methods are a commonly used tool in statistics to separate good features from lucky draws. We give a user-friendly overview of both classic and recent approaches to this problem.

Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data

There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence. We show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide data set with high time resolution to show that BooteJTK provides more consistent rhythm detection than existing methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses. BooteJTK is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK .

Interval estimation of a population mean using existing knowledge or data on effect sizes

Bayes or empirical Bayes methods to improve inferential accuracy for a population mean has been widely adopted in medical research. As the joint prior distribution of both the mean and variance parameters can be difficult to specify or estimate, most of these methods have relied on certain level of simplifications of the joint prior, which could lead to difficulty in the interpretation of the posterior distribution or compromised inferential accuracy. We propose a framework of interval estimation using existing knowledge or data on the effect size to address this difficulty. Our method has two unique characteristics. First, the interpretation of the interval bears the spirit of both Frequentist and Bayesian thinking. For this reason, it will be called FB interval. Second, we define a new quantity, the hybrid effect size, which is a key quantity that mediates the construction of the FB interval when the population variance is unknown. A simulation study and a real data example are presented to evaluate and illustrate our method.