Michail Tsagris

Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas

A tool was developed to extract biologically relevant information from quantitative 3D image data, allowing the relationship between diverse regulatory networks and 3D organ growth to be revealed. Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach, we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of diverse regulatory networks to be integrated with 3D organ growth.

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthals fail-safe number. Although Rosenthals estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthals fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthals estimator.

Umbrella Reviews, Overviews of Reviews, and Meta-epidemiologic Studies: Similarities and Differences

This chapter describes umbrella reviews, overviews of reviews, and meta-epidemiologic studies focusing on their definitions, purposes, and classifications where appropriate and then elaborating on their similarities and differences. We may consider umbrella reviews as reviews integrating several types of study designs but typically randomized controlled trials and systematic reviews of such studies in a unifying fashion in order to address a content issue (e.g., whether or not a given drug is superior to another). Overviews of reviews are reviews of systematic reviews and meta-analyses which can focus on content or methodological issues. Finally, meta-epidemiologic studies focus on potentially different types of study designs but most typically on randomized trials and systematic reviews, usually across different content domains (e.g., topics or conditions), and mainly aim at addressing methodological issues.

An elliptically symmetric angular Gaussian distribution

We define a distribution on the unit sphere

Improved Classification for Compositional Data Using the ź-transformation

\mathbb {S}^{d-1}

Exploring the distribution for the estimator of Rosenthal's ‘fail-safe’ number of unpublished studies in meta-analysis

Sd-1 called the elliptically symmetric angular Gaussian distribution. This distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian distribution closely analogous to the Kent subfamily of the general Fisher–Bingham distribution. Like the Kent distribution, it has ellipse-like contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe.

Taking R to its limits: 70+ tips

BackgroundFeature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work we extend established constrained-based, feature-selection methods to high-dimensional “omics” temporal data, where the number of measurements is orders of magnitude larger than the sample size. The extension required the development of conditional independence tests for temporal and/or static variables conditioned on a set of temporal variables.ResultsThe algorithm is able to return multiple, equivalent solution subsets of variables, scale to tens of thousands of features, and outperform or be on par with existing methods depending on the analysis task specifics.ConclusionsThe use of this algorithm is suggested for variable selection with high-dimensional temporal data.

Meta-Analyses of Clinical Trials Versus Diagnostic Test Accuracy Studies

In compositional data analysis, an observation is a vector containing nonnegative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centers on the idea of using the α-transformation to transform the data and then to classify the transformed data via regularized discriminant analysis and the k-nearest neighbors algorithm. Using the α-transformation generalizes two rival approaches in compositional data analysis, one (when α=1) that treats the data as though they were Euclidean, ignoring the compositional constraint, and another (when α = 0) that employs Aitchison’s centered log-ratio transformation. A numerical study with several real datasets shows that whether using α = 1 or α = 0 gives better classification performance depends on the dataset, and moreover that using an intermediate value of α can sometimes give better performance than using either 1 or 0.