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"Statistical and Computational Methods"
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Integrated diversity and network analyses reveal drivers of microbiome dynamics
Understanding microbiome dynamics requires capturing not only changes in microbial composition but also interactions between community members. Traditional approaches frequently overlook microbe-microbe interactions, limiting their ecological interpretation. Here, we introduce a novel computational framework that integrates compositional data with network-based analyses, significantly improving the detection of biologically meaningful patterns in community variation. By applying this framework to a large data set from the plant microbiota, we identify representative groups of interacting microbes driving differences across microhabitats and environmental conditions. Our analysis framework, implemented in an R package “mina,” provides robust tools allowing researchers to assess statistical differences between microbial networks and detect condition-specific interactions. Broadly applicable to microbiome data sets, our framework is aimed at enabling advances in our understanding of microbial interactions within complex communities.
Journal Article
Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification
The human microbiome profoundly influences health and disease, but current computational tools often overlook the evolutionary relationships among microbes, leading to incomplete or inaccurate interpretations of complex microbial data. Phylo-Spec provides a new way to understand the microbiome by combining microbial abundance, taxonomy, and phylogeny within a unified deep learning framework. This model not only improves the accuracy of health status classification but also highlights key microbial contributors linked to disease. By capturing both microbial diversity and evolutionary context, Phylo-Spec bridges the gap between bioinformatics and biological insight, offering a powerful and interpretable approach for advancing microbiome-based diagnostics and precision medicine.
Journal Article
Harnessing machine learning for metagenomic data analysis: trends and applications
Metagenomic sequencing has revolutionized our understanding of microbial ecosystems by enabling high-resolution profiling of microbes across diverse environments. However, the resulting data are high-dimensional, sparse, and noisy, posing challenges for downstream data analysis. Machine learning (ML) has provided an arsenal of tools to extract meaningful insights from such large and complex data sets. This review surveys the existing state of ML applications in metagenomic data analysis, from traditional supervised and unsupervised learning to time-series modeling, transfer learning, and newer directions such as causal ML and generative models. We highlight certain key challenges and delve into important issues like model interpretability, emphasizing the importance of explainable AI (XAI). We also compare ML with mechanistic models, commenting on their relative advantages, disadvantages, and prospects for synergy. Finally, we preview future directions, such as the incorporation of multi-omics data, synthetic data generation, and Agentic AI systems, highlighting the increasingly prominent role that AI and ML will play in the future of microbiome science.
Journal Article
MNetClass: a control-free microbial network clustering framework for identifying central subcommunities across ecological niches
MNetClass provides a valuable tool for microbiome network analysis, enabling the identification of key microbial subcommunities across diverse ecological niches. Implemented as an R package ( https://github.com/YihuaWWW/MNetClass ), it offers broad accessibility for researchers. Here, we systematically benchmarked MNetClass against existing microbial clustering methods on synthetic data using various performance metrics, demonstrating its superior efficacy. Notably, MNetClass operates without the need for control groups and effectively identifies central microbes, highlighting its potential as a robust framework for advancing microbiome research.
Journal Article
How to do linguistics with R : data exploration and statistical analysis
This book provides a linguist with a statistical toolkit for exploration and analysis of linguistic data. It employs R, a free software environment for statistical computing, which is increasingly popular among linguists. How to do Linguistics with R: Data exploration and statistical analysis is unique in its scope, as it covers a wide range of classical and cutting-edge statistical methods, including different flavours of regression analysis and ANOVA, random forests and conditional inference trees, as well as specific linguistic approaches, among which are Behavioural Profiles, Vector Space Models and various measures of association between words and constructions. The statistical topics are presented comprehensively, but without too much technical detail, and illustrated with linguistic case studies that answer non-trivial research questions. The book also demonstrates how to visualize linguistic data with the help of attractive informative graphs, including the popular ggplot2 system and Google visualization tools.This book has a companion website: http://doi.org/10.1075/z.195.website
eBook
Parameter Space Compression Underlies Emergent Theories and Predictive Models
The microscopically complicated real world exhibits behavior that often yields to simple yet quantitatively accurate descriptions. Predictions are possible despite large uncertainties in microscopic parameters, both in physics and in multiparameter models in other areas of science. We connect the two by analyzing parameter sensitivities in a prototypical continuum theory (diffusion) and at a self-similar critical point (the Ising model). We trace the emergence of an effective theory for long-scale observables to a compression of the parameter space quantified by the eigenvalues of the Fisher Information Matrix. A similar compression appears ubiquitously in models taken from diverse areas of science, suggesting that the parameter space structure underlying effective continuum and universal theories in physics also permits predictive modeling more generally.
Journal Article
Controlled Architectures in LbL Films for Sensing and Biosensing
This chapter contains sections titled:
Introduction
LbL‐Based Sensors and Biosensors
Special Architectures for Sensing and Biosensing
Statistical and Computational Methods to Treat the Data
Conclusions and Perspectives
References
Book Chapter
Coexistence in the two-dimensional May-Leonard model with random rates
We employ Monte Carlo simulations to numerically study the temporal evolution and transient oscillations of the population densities, the associated frequency power spectra, and the spatial correlation functions in the (quasi-) steady state in two-dimensional stochastic May-Leonard models of mobile individuals, allowing for particle exchanges with nearest-neighbors and hopping onto empty sites. We therefore consider a class of four-state three-species cyclic predator-prey models whose total particle number is not conserved. We demonstrate that quenched disorder in either the reaction or in the mobility rates hardly impacts the dynamical evolution, the emergence and structure of spiral patterns, or the mean extinction time in this system. We also show that direct particle pair exchange processes promote the formation of regular spiral structures. Moreover, upon increasing the rates of mobility, we observe a remarkable change in the extinction properties in the May-Leonard system (for small system sizes): (1) as the mobility rate exceeds a threshold that separates a species coexistence (quasi-) steady state from an absorbing state, the mean extinction time as function of system size
N
crosses over from a functional form ∼
e
c
N
/
N
(where c is a constant) to a linear dependence; (2) the measured histogram of extinction times displays a corresponding crossover from an (approximately) exponential to a Gaussian distribution. The latter results are found to hold true also when the mobility rates are randomly distributed.
Journal Article
Asymptotics of work distributions: the pre-exponential factor
We determine the complete asymptotic behaviour of the work distribution in driven stochastic systems described by Langevin equations. Special emphasis is put on the calculation of the pre-exponential factor which makes the result free of adjustable parameters. The method is applied to various examples and excellent agreement with numerical simulations is demonstrated. For the special case of parabolic potentials with time-dependent frequencies, we derive a universal functional form for the asymptotic work distribution.
Journal Article