Biology subjects

Gorin, G.

Publications and source records attributed to Gorin, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Biological and technical spatial variation in spatial transcriptomics

Spatial transcriptomics technologies enable the identification of spatially variable genes that can shed light on tissue structure and function. However, spatial variability of gene expression can be due not only to biological function but also technical artifact. We develop a model for spatially variable capture that forms part of a bursty transcription model, allowing us to distinguish biological variability from spatially variable technical noise. Our approach is implemented in a program called stipple, which we demonstrate can be useful for identifying and interpreting spatial gene expression variation from spatial transcriptomics assays.

bioinformatics↗

bioRxiv2026

Empty drops in scRNA-seq uncover the surprising prevalence of sequestered neuropeptide mRNA and pervasive sequencing artifacts

The empty drops in single-cell sequencing experiments are an underexplored resource. As such, they present a substrate to ask questions orthogonal to standard single-cell sequencing workflows, calibrate statistical models using simple internal controls, and detect technical outliers which would be otherwise challenging to distinguish from real biology. In this case study, we report a relatively simple procedure to detect sequencing artifacts and make recommendations to reduce the risk of erroneous quantifications. In addition, we report the surprising abundance and co-expression of mRNA coding for neuropeptide-related genes in the empty drops, possibly reflecting underlying physiology.

bioinformatics↗

bioRxiv2026

DEPower: approximate power analysis with DESeq2

Rigorous experimental design, including formal power analysis, is a cornerstone of reproducible RNA sequencing (RNA-seq) research. The design of RNA-seq experiments requires computing the minimum sample number required to identify an effect of a particular size at a predefined significance level. Ideally, the statistical test used for the analysis of experimental data should match the test used for sample size determination; however, few tools use the assumptions of the popular differential expression testing framework DESeq2, and most opt for simulation-based rather than analytical approaches. Grounded in the DESeq2 model framework, we derive sample size requirements for both single-cell and bulk RNA-seq experiments delivered as a web-based tool for power analysis, DEPower, available at https://poweranalysis-fb.streamlit.app/ that makes rigorous RNA-seq study design accessible to all researchers.

bioinformatics↗

bioRxiv2025

Male XIST expression in cardiac pseudo-glia does not induce X chromosome inactivation

XIST is a long non-coding RNA that mediates the process of X chromosome inactivation in females, and has not been previously observed in males outside of developmental, pathological, and ectopic contexts. We report the unprecedented endogenous expression of XIST in 226 male heart samples across 15 single-cell RNA sequencing studies. The male expression of XIST is specific to a rare cell type typically annotated as neurons or glia. We investigate a variety of explanations and cross-reference the expression of XIST against age, sex, and pathology, finding signatures of a non-canonical inhibitory program, potentially mediated by a truncated transcript. Interestingly, the highest-powered datasets exhibit XIST upregulation in heart disease and comorbidity donors relative to healthier controls. Furthermore, in humans we identify a male-specific association of the XIST genomic locus with myocardial infarction (p = 1.3 x 10-4). Taken together, our findings suggest that the pathology of cardiomyopathy may involve uncharacterized, therapeutically actionable non-coding RNA pathways that operate through the cardiac nervous system, and underscore the importance of genomic investigations of the X chromosome.

bioinformatics↗

bioRxiv2024

Stochastic Modeling of Biophysical Responses to Perturbation

Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the how behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.

genomics↗

bioRxiv2024

A Biophysical Model for ATAC-seq Data Analysis

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) can be used to identify open chromatin regions, providing complementary information to RNA-seq which measures gene expression by sequencing. Single-cell "multiome" methods offer the possibility of measuring both modalities simultaneously in cells, raising the question of how to analyze them jointly, and also the extent to which the information they provide is better than unregistered data where single-cell ATAC-seq and single-cell RNA-seq are performed on the same sample, but on different cells. We propose and motivate a biophysical model for chromatin dynamics and subsequent transcription that can be used with multiome data, and use it to assess the benefits of multiome data over unregistered single-cell RNA-seq and single-cell ATAC-seq. We also show that our model provides a biophysically grounded approach to integration of open chromatin data with other modalities.

bioinformatics↗

bioRxiv2024

Trajectory inference from single-cell genomics data with a process time model

Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment.

bioinformatics↗

bioRxiv2023

Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data

Multimodal, single-cell genomics technologies enable simultaneous capture of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell types, with applications ranging from inferring kinetic differences between cells, to the role of stochasticity in driving heterogeneity. However, current methods for determining cell types or clusters present in multimodal data often rely on ad hoc or independent treatment of modalities, and assumptions ignoring inherent properties of the count data. To enable interpretable and consistent cell cluster determination from multimodal data, we present meK-Means (mechanistic K-Means) which integrates modalities and learns underlying, shared biophysical states through a unifying model of transcription. In particular, we demonstrate how meK-Means can be used to cluster cells from unspliced and spliced mRNA count modalities. By utilizing the causal, physical relationships underlying these modalities, we identify shared transcriptional kinetics across cells, which induce the observed gene expression profiles, and provide an alternative definition for clusters through the governing parameters of cellular processes.

genomics↗

bioRxiv2023

Studying stochastic systems biology of the cell with single-cell genomics data

Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

biophysics↗

bioRxiv2023

The telegraph process is not a subordinator

Investigations of transcriptional models by Amrhein et al. outline a strategy for connecting steady-state distributions to process dynamics. We clarify its limitations: the strategy holds for a very narrow class of processes, which excludes an example given by the authors.

biophysics↗

bioRxiv2023

Mechanistic modeling with a variational autoencoder for multimodal single-cell RNA sequencing data

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strateg for treating multimodal datasets generated by high-throughput, single-cell genomic assays.

biophysics↗

bioRxiv2022

Transient and delay chemical master equations

The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a bursty or switching promoter to a fairly general set of Markovian or deterministically delayed monomolecular RNA interconversion reactions with no feedback. We provide numerical solutions for the RNA copy number distributions the models induce, and solve several systems with splicing and degradation. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.

biophysics↗

bioRxiv2022

Spectral neural approximations for models of transcriptional dynamics

The advent of high-throughput transcriptomics provides an opportunity to advance mechanistic understanding of transcriptional processes and their connections to cellular function at an un-precedented, genome-wide scale. These transcriptional systems, which involve discrete, stochastic events, are naturally modeled using Chemical Master Equations (CMEs), which can be solved for probability distributions to fit biophysical rates that govern system dynamics. While CME models have been used as standards in fluorescence transcriptomics for decades to analyze single species RNA distributions, there are often no closed-form solutions to CMEs that model multiple species, such as nascent and mature RNA transcript counts. This has prevented the application of standard likelihood-based statistical methods for analyzing high-throughput, multi-species transcriptomic datasets using biophysical models. Inspired by recent work in machine learning to learn solutions to complex dynamical systems, we leverage neural networks and statistical understanding of system distributions to produce accurate approximations to a steady-state bivariate distribution for a model of the RNA life-cycle that includes nascent and mature molecules. The steady-state distribution to this simple model has no closed-form solution and requires intensive numerical solving techniques: our approach reduces likelihood evaluation time by several orders of magnitude. We demonstrate two approaches, where solutions are approximated by (1) learning the weights of kernel distributions with constrained parameters, or (2) learning both weights and scaling factors for parameters of kernel distributions. We show that our strategies, denoted by kernel weight regression (KWR) and parameter scaled kernel weight regression (psKWR), respectively, enable broad exploration of parameter space and can be used in existing likelihood frameworks to infer transcriptional burst sizes, RNA splicing rates, and mRNA degradation rates from experimental transcriptomic data. Statement of significanceThe life-cycles of RNA molecules are governed by a set of stochastic events that result in heterogeneous gene expression patterns in genetically identical cells, resulting in the vast diversity of cellular types, responses, and functions. While stochastic models have been used in the field of fluorescence transcriptomics to understand how cells exploit and regulate this inherent randomness, biophysical models have not been widely applied to high-throughput transcriptomic data, as solutions are often intractable and computationally impractical to scale. Our neural approximations of solutions to a two-species transcriptional system enable efficient inference of rates that drive the dynamics of gene expression, thus providing a scalable route to extracting mechanistic information from increasingly available multi-species single-cell transcriptomics data.

bioinformatics↗

bioRxiv2022

Monod: mechanistic analysis of single-cell RNA sequencing count data

Single-cell RNA sequencing analysis centers on illuminating cell diversity and understanding the transcriptional mechanisms underlying cellular function. These datasets are large, noisy, and complex. Current analyses prioritize noise removal and dimensionality reduction to tackle these challenges and extract biological insight. We propose an alternative, physical approach to leverage the stochasticity, size, and multimodal nature of these data to explicitly distinguish their biological and technical facets while revealing the underlying regulatory processes. With the Python package Monod, we demonstrate how nascent and mature RNA counts, present in most published datasets, can be meaningfully "integrated" under biophysical models of transcription. By utilizing variation in these modalities, we can identify transcriptional modulation not discernible though changes in average gene expression, quantitatively compare mechanistic hypotheses of gene regulation, analyze transcriptional data from different technologies within a common framework, and minimize the use of opaque or distortive normalization and transformation techniques.

bioinformatics↗

bioRxiv2022

RNA velocity unraveled

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

biophysics↗

bioRxiv2021

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. We argue that answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a stochastic transcription rate coupled to a discrete stochastic RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. Although biophysically distinct, these models are mathematically similar, and we show they are hard to distinguish without comparing whole predicted probability distributions. Our work illustrates the importance of theory-guided data collection, and introduces a general framework for constructing and solving mathematically nontrivial continuous-discrete stochastic models. Significance StatementThe interpretation of transcriptomic observations requires detailed models of biophysical noise that can be compared and fit to experimental data. Models of intrinsic noise, describing stochasticity in molecular reactions, and extrinsic noise, describing cell-to-cell variation, are particularly common. However, integrating and solving them is challenging, and previous results are largely limited to summary statistics. We examine two mechanistically grounded stochastic models of transcriptional variation and demonstrate that (1) well-known regimes naturally emerge in limiting cases, and (2) the choice of noise model significantly affects the RNA distributions, but not the lower moments, offering a route to model identification and inference. This approach provides a simple and biophysically interpretable means to construct and unify models of transcriptional variation.

biophysics↗

bioRxiv2021

Length Biases in Single-Cell RNA Sequencing of pre-mRNA

Single-molecule pre-mRNA and mRNA sequencing data can be modeled and analyzed using the Markov chain formalism to yield genome-wide insights into transcription. However, quantitative inference with such data requires careful assessment and understanding of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data, and explore the mechanistic implications. A biological explanation for this phenomenon within our modeling framework requires unrealistic transcriptional parameters, leading us to posit a length-based model of capture bias. We provide solutions for this model, and use them to find concordant and mechanistically plausible parameter trends across data from multiple single-cell RNA-seq experiments in several species.

biophysics↗

bioRxiv2021

Analytical solutions of the chemical master equation with bursty production and isomerization reactions

Splicing cascades that alter gene products post-transcriptionally also affect expression dynamics. We study a class of processes and associated distributions that emerge from a bursty promoter model coupled to a path graph of downstream mRNA splicing, and more generally examine the behavior of finite-activity jump drivers coupled to a directed acyclic graph of splicing with one or more roots. These solutions provide full time-dependent joint distributions for an arbitrary number of species, offering qualitative and quantitative insights about how splicing can regulate expression dynamics. Finally, we derive a set of quantitative constraints on the minimum complexity necessary to reproduce gene co-expression patterns using synchronized burst models. We validate these findings by analyzing long-read sequencing data, where we find evidence of expression patterns largely consistent with these constraints.

biophysics↗