Department of Statistics, UCLA

A brief overview of the papers and commentaries in this special edition.

Ambulance response times in Santa Barbara County for 2006 are analyzed using point process techniques, including kernel intensity estimates and K-functions. Clusters of calls result in significantly higher response times, and this effect is quantified. In particular, calls preceded by other calls within 20 km and within the previous hour are significantly more likely to result in violations. This effect appears to be especially pronounced within semi-rural neighborhoods.

[WestJEM. 2009;10:42-47.]

Introductory remarks

The manuscript considers multivariate functional data analysis with a known graphical model among the functional variables representing their conditional relationships (e.g., brain region-level fMRI data with a prespecified connectivity graph among brain regions). Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model, and cannot preserve knowledge of a given graph. We propose a method for multivariate functional analysis that exactly conforms to a given inter-variable graph. We first show the equivalence between partially separable functional GGM and graphical Gaussian processes (GP), proposed recently for constructing optimal multivariate covariance functions that retain a given graphical model. The theoretical connection helps to design a new algorithm that leverages Dempster's covariance selection for obtaining the maximum likelihood estimate of the covariance function for multivariate functional data under graphical constraints. We also show that the finite term truncation of functional GGM basis expansion used in practice is equivalent to a low-rank graphical GP, which is known to oversmooth marginal distributions. To remedy this, we extend our algorithm to better preserve marginal distributions while respecting the graph and retaining computational scalability. The benefits of the proposed algorithms are illustrated using empirical experiments and a neuroimaging application.

This repository contains the R source code and derived data products to reproduce analyses in the paper: ‘A Twenty-First Century Structural Change in Antarctica’s Sea ice System’ by Marilyn N. Raphael, Thomas J. Maierhofer, Ryan L. Fogt, William R. Hobbs, and Mark S. Handcock. It appears in Nature-Communications Earth & Environment, 6, 131 (2025), under DOI: 10.1038/s43247-025-02107-5. There is also a detailed support site on GitHub: https://github.com/RaphaelLab/StructuralChangeInAntarcticSeaIceSystem

Understanding how cells respond differently to perturbation is crucial in cell biology, but existing methods often fail to accurately quantify and interpret heterogeneous single-cell responses. Here we introduce the perturbation-response score (PS), a method to quantify diverse perturbation responses at a single-cell level. Applied to single-cell perturbation datasets such as Perturb-seq, PS outperforms existing methods in quantifying partial gene perturbations. PS further enables single-cell dosage analysis without needing to titrate perturbations, and identifies buffered and sensitive response patterns of essential genes, depending on whether their moderate perturbations lead to strong downstream effects. PS reveals differential cellular responses on perturbing key genes in contexts such as T cell stimulation, latent HIV-1 expression and pancreatic differentiation. Notably, we identified a previously unknown role for the coiled-coil domain containing 6 (CCDC6) in regulating liver and pancreatic cell fate decisions. PS provides a powerful method for dose-to-function analysis, offering deeper insights from single-cell perturbation data.

Abstract: From 1979 to 2016, total Antarctic sea ice extent experienced a positive trend with record winter maxima in 2012 and 2014. Record summer minima followed within the period 2017-2024, raising the possibility that the Antarctic sea ice system might be changing state. Here we use a Bayesian reconstruction of Antarctic sea ice extent which extends the record back to 1899, to show that the sequence of extreme minima in summer Antarctic sea ice extent is unlikely to have happened in the 20th century. We show that they represent a structural change in the sea ice system, manifest by increased persistence in the sea ice extent anomalies and a strongly reduced tendency to return to the mean state. Further, our analysis suggests that we may no longer rely on the past, long-term, behavior of the sea ice system to predict its future state. Extreme conditions may characterize the future state of Antarctic sea ice.

We study an Expectation-Maximization (EM) algorithm for estimating product-term regression models with missing data. The study of such problems in the frequentist tradition has thus far been restricted to an EM algorithm method using full numerical integration. However, under most missing data patterns, we show that this problem can be solved analytically, and numerical approximations are only needed under specific conditions. Thus we propose a hybrid EM algorithm, which uses analytic solutions when available and approximate solutions only when needed. The theoretical framework of our algorithm is described herein, along with three empirical experiments using both simulated and real data. We demonstrate that our algorithm provides greater estimation accuracy, exhibits robustness to distributional violations, and confers higher power to detect interaction effects. We conclude with a discussion of extensions and topics of further research.

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.