Robust self-supervised denoising of voltage imaging data using CellMincer.

Voltage imaging enables high-throughput investigation of neuronal activity, yet its utility is often constrained by a low signal-to-noise ratio (SNR). Conventional denoising algorithms, such as those based on matrix factorization, impose limiting assumptions about the noise process and the spatiotemporal structure of the signal. While deep learning based denoising techniques offer greater adaptability, existing approaches fail to fully exploit the fast temporal dynamics and unique short- and long-range dependencies within voltage imaging datasets. Here, we introduce CellMincer, a novel self-supervised deep learning method designed specifically for denoising voltage imaging datasets. CellMincer operates on the principle of masking and predicting sparse sets of pixels across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without the need for large temporal denoising contexts. We develop and utilize a physics-based simulation framework to generate realistic datasets for rigorous hyperparameter optimization and ablation studies, highlighting the key role of conditioning the denoiser on precomputed spatiotemporal auto-correlations to achieve 3-fold further reduction in noise. Comprehensive benchmarking on both simulated and real voltage imaging datasets, including those with paired patch-clamp electrophysiology (EP) as ground truth, demonstrates CellMincer's state-of-the-art performance. It achieves substantial noise reduction across the entire frequency spectrum, enhanced detection of subthreshold events, and superior cross-correlation with ground-truth EP recordings. Finally, we demonstrate how CellMincer's addition to a typical voltage imaging data analysis workflow improves neuronal segmentation, peak detection, and ultimately leads to significantly enhanced separation of functional phenotypes.

REPRODUCIBLE AND SHAREABLE QUANTIFICATIONS OF PATHOGENICITY.

There are now hundreds of thousands of pathogenicity assertions that relate genetic variation to disease, but most of this clinically utilized variation has no accepted quantitative disease risk estimate. Recent disease-specific studies have used control sequence data to reclassify large amounts of prior pathogenic variation, but there is a critical need to scale up both the pace and feasibility of such pathogenicity reassessments across human disease. In this manuscript we develop a shareable computational framework to quantify pathogenicity assertions. We release a reproducible "digital notebook" that integrates executable code, text annotations, and mathematical expressions in a freely accessible statistical environment. We extend previous disease-specific pathogenicity assessments to over 6,000 diseases and 160,000 assertions in the ClinVar database. Investigators can use this platform to prioritize variants for reassessment and tailor genetic model parameters (such as prevalence and heterogeneity) to expose the uncertainty underlying pathogenicity-based risk assessments. Finally, we release a website that links users to pathogenic variation for a queried disease, supporting literature, and implied disease risk calculations subject to user-defined and disease-specific genetic risk models in order to facilitate variant reassessments.