arXiv subjects

Polina Arsenteva

Publications and source records attributed to Polina Arsenteva.

4 recordsLinked to original sources

arXiv2025

Bootstrap inference for linear regression between variables that are never jointly observed: application in in vivo experiments

In modern experimental science, there is a common problem of estimating the coefficients of a linear regression in a context where the variables of interest cannot be observed simultaneously. When there is a categorical variable that is observed on all statistical units, we consider two estimators of linear regression that take this additional information into account: an estimator based on moments and an estimator based on optimal transport theory. These estimators are shown to be consistent and asymptotically Gaussian under weak hypotheses. The asymptotic variance has no explicit expression, except in some special cases, for which reason a stratified bootstrap approach is developed to construct confidence intervals for the estimated parameters, whose consistency is also shown. A simulation study evaluating and comparing the finite sample performance of these estimators demonstrates the advantages of the bootstrap approach in several realistic scenarios. An application to in vivo experiments, conducted in the context of studying radio-induced adverse effects in mice, revealed important relationships between the biomarkers of interest that could not be identified with the considered naive approach.

stat.ME↗

arXiv2025

Joint clustering with alignment for temporal data in a one-point-per-experiment setting

Temporal data, obtained in the setting where it is only possible to observe one time point per experiment, is widely used in different research fields, yet remains insufficiently addressed from the statistical point of view. Such data often contain observations of a large number of entities, in which case it is of interest to identify a small number of representative behavior types. In this paper, we propose a new method that simultaneously performs clustering and alignment of temporal objects inferred from these data, providing insight into the relationships between entities. Simulations confirm the ability of the proposed approach to leverage multiple properties of the complex data we target such as accessible uncertainties, correlations and a small number of time points. We illustrate it on real data encoding cellular response to a radiation treatment with high energy, supported with the results of an enrichment analysis.

stat.ME↗

arXiv2024

Extending Kernel Testing To General Designs

Kernel-based testing has revolutionized the field of non-parametric tests through the embedding of distributions in an RKHS. This strategy has proven to be powerful and flexible, yet its applicability has been limited to the standard two-sample case, while practical situations often involve more complex experimental designs. To extend kernel testing to any design, we propose a linear model in the RKHS that allows for the decomposition of mean embeddings into additive functional effects. We then introduce a truncated kernel Hotelling-Lawley statistic to test the effects of the model, demonstrating that its asymptotic distribution is chi-square, which remains valid with its Nystrom approximation. We discuss a homoscedasticity assumption that, although absent in the standard two-sample case, is necessary for general designs. Finally, we illustrate our framework using a single-cell RNA sequencing dataset and provide kernel-based generalizations of classical diagnostic and exploration tools to broaden the scope of kernel testing in any experimental design.

stat.ME↗

arXiv2024

Kernel-Based Testing for Single-Cell Differential Analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

stat.ML↗