arXiv subjects

Timothy Barry

Publications and source records attributed to Timothy Barry.

3 recordsLinked to original sources

arXiv2025

The permuted score test for robust differential expression analysis

Negative binomial (NB) regression is a popular method for identifying differentially expressed genes in genomics data, such as bulk and single-cell RNA sequencing data. However, NB regression makes stringent parametric and asymptotic assumptions, which can fail to hold in practice, leading to excess false positive and false negative results. We propose the permuted score test, a new strategy for robust regression based on permuting score test statistics. The permuted score test provably controls type-I error across a much broader range of settings than standard NB regression while nevertheless approximately matching standard NB regression with respect to power (when the assumptions of standard NB regression obtain) and computational efficiency. We accelerate the permuted score test by leveraging emerging techniques for sequential Monte-Carlo testing and novel algorithms for efficiently computing GLM score tests. We apply the permuted score test to real and simulated RNA sequencing data, finding that it substantially improves upon the error control of existing NB regression implementations, including DESeq2. The permuted score test could enhance the reliability of differential expression analysis across diverse biological contexts.

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arXiv2024

Multiple testing with anytime-valid Monte Carlo p-values

In contemporary problems involving genetic or neuroimaging data, thousands of hypotheses need to be tested. Due to their high power, and finite sample guarantees on type-I error under weak assumptions, Monte Carlo permutation tests are often considered as gold standard for these settings. However, the enormous computational effort required for (thousands of) permutation tests is a major burden. In this paper, we integrate recently constructed anytime-valid permutation p-values into a broad class of multiple testing procedures, including the Benjamini-Hochberg procedure. This allows to fully adapt the number of permutations to the underlying data and thus, for example, to the number of rejections made by the multiple testing procedure. Even though this data-adaptive stopping can induce dependencies between the p-values that violate the usual assumptions of the Benjamini-Hochberg procedure, we prove that our approach controls the false discovery rate under mild assumptions. Furthermore, our method provably decreases the required number of permutations substantially without compromising power. On a real genomics data set, our method reduced the computational time from more than three days to less than four minutes while increasing the number of rejections.

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arXiv2022

Exponential family measurement error models for single-cell CRISPR screens

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present substantial statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens -- "thresholded regression" -- exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g., Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several novel insights.

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