Biology subjects

Roeder, K.

Publications and source records attributed to Roeder, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Autism genes converge on three functional programs organized by neuronal subclass, developmental timing, and cortical patterning

Our companion sequencing study uncovered 253 genes robustly associated with autism spectrum disorder (ASD), yet the biological programs they impact, and the cellular, developmental, and spatial contexts in which they converge, remain unresolved. Here we systematically contextualize genes associated with ASD across neurodevelopment and cortical areas, integrating developmental single-cell and spatial atlases with gene- and isoform co-expression, regulatory, proteomic, and synaptic networks. Genetic burden concentrates within temporally-resolved neuronal subclasses: newborn excitatory neurons, immature interneurons, and maturing intratelencephalic lineages. Genes associated with ASD converge on three functional programs--gene regulation, neuronal morphogenesis, and synaptic transmembrane signaling machinery--resolved from 28 ASD-associated networks, several of which are directly regulated by ASD genes including MEF2C, SOX11, and FOXP2. These programs are spatially patterned, with risk genes exhibiting an increasing anterior-to-posterior cortical expression gradient, anchored in the primary visual cortex and driven by excitatory neuron gene-regulatory programs. Finally, ASD risk genes associated with more severe developmental phenotypes show broader excitatory neuron enrichment and less interneuron involvement. Together, these findings anchor ASD genetic vulnerability to specific neurodevelopmental lineages, epochs, and molecular substrates, and delineate the features that distinguish ASD from comorbidities with broader developmental impacts.

genomics↗

bioRxiv2026

Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

genomics↗

bioRxiv2026

Inferring disruption of directed graphs using LIKA reveals altered protein phosphorylation networks in schizophrenia

MotivationKinases regulate a multitude of protein functions, and their dysregulation is pivotal for many human diseases. Direct measurement of kinase activity, however, is often challenging; therefore, inferring activity from the behavior of their substrates is a widely adopted strategy. Nonetheless, traditional methods typically oversimplify the underlying network, ignoring that any particular substrate can be phosphorylated by multiple kinases. ResultsWe present LIKA, a likelihood-based framework for inferring kinase activity from phosphoproteomic data. By modeling the many-to-many structure of kinase-substrate interactions, LIKA achieves high efficiency, even with limited data, while capturing network complexity. Simulation and cell line analyses confirm the robustness and accuracy of LIKA. Importantly, analysis of a phosphoproteomic dataset from schizophrenia and control subjects reveals novel dysregulated kinases. Availability and ImplementationThe implementation code and publicly available data are provided at: https://github.com/lujingz/LIKA.

bioinformatics↗

bioRxiv2026

TIDEST: post-imputation differential expression testing for spatial transcriptomics data

Spatial transcriptomics is increasingly extended by computational reconstruction of unmeasured genes from matched single-cell RNA-sequencing references, enabling transcriptome-wide analyses from targeted or sparse assays. Yet downstream analyses typically treat reconstructed expression as experimentally observed, overlooking prediction error and latent spatial variation that can misattribute tissue architecture to biological regulation. Here we introduce TIDEST, a framework for statistically valid inference on reconstructed spatial transcriptomes. TIDEST calibrates reconstructed expression using measured genes and adjusts for latent spatial variation before differential expression analysis. Across realistic spatial tissue simulations, TIDEST controls false discoveries where existing approaches fail, while preserving power. Across mouse and human brain, glioblastoma and breast cancer, TIDEST changes biological interpretation by correcting misleading differential-expression calls and revealing disease-associated transcriptional programs obscured by spatial confounding. Our results show that prediction accuracy alone is insufficient for reliable biological discovery and establish valid statistical inference as an essential component of reconstructed spatial transcriptomics.

genomics↗

bioRxiv2026

Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Sequencing analyses have identified 185 ASD-associated genes, which implicate neurons, but the specific brain regions through which these neurons influence neurodevelopment remain unclear. Here, we integrate over one million single-cell RNA sequencing profiles from 20 regions of the developing human brain (4-23 post-conceptual weeks) using a new framework, STARMAPS (Sparse Task-specific Analysis for Revealing Molecular Associations in Particular Single-cell datasets). STARMAPS accounts for coordinated regional and developmental perturbations in gene expression, enabling robust cross-region comparison. We replicate prior findings that ASD-associated genes are enriched in excitatory neurons during mid-fetal development, and we extend these results to reveal distinct spatial signatures. Across 26 excitatory neuron subtypes, six clusters showed significant enrichment for ASD-associated genes. These clusters localize to both cortical and subcortical regions, including the motor, temporal, and visual cortex, as well as the thalamus and hypothalamus. Our findings support a major role for excitatory neurons across distributed brain circuits, implicating previously underappreciated subcortical structures in ASD etiology. By providing a statistically rigorous framework for spatiotemporal integration of single-cell data, STARMAPS enables refined mapping of molecular vulnerability across the developing human brain.

genomics↗

bioRxiv2026

Estimating protein isoform abundances with PAQu

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce PAQu, a novel Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. PAQu offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that PAQu consistently outperforms competing methods in detecting differentially expressed protein isoforms and estimating their abundances. We use PAQu to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that PAQu can identify significant variations in isoform abundance levels not previously possible.

genomics↗

bioRxiv2026

Exploring RNA conformational ensembles in silico: progress and challenges

RNA function is intrinsically linked to its structural polymorphism, with molecules exploring the heterogeneous conformational ensembles resulting from complex energy landscapes. These landscapes arise from competing interactions, small energetic separations between microstates, and strong coupling to the environment, posing significant challenges for both experimental characterization and molecular simulation. In this chapter, we review current computational strategies that aim to explore RNA conformational ensembles in silico, with a specific focus on energy landscape-based approaches and atomistic simulations. We discuss key limitations related to sampling efficiency, force-field accuracy, and ensemble analysis, and illustrate their impact through case studies on a self-cleaving ribozyme and an H-type pseudoknot. Finally, we highlight emerging directions, including closer integration with experimental data and the growing role of machine learning, which will probably reinforce the predictive power of in silico RNA energy landscape exploration.

molecular biology↗

bioRxiv2025

DAWN-SCAPE: Discovering Associations With Networks through Shared Contextual Analysis of Phenotype and Expression

The molecular basis of phenotypes is often explored by contrasting gene expression from relevant tissue taken from individuals classified into phenotypic extremes, such as affected versus unaffected individuals. Analysis of this differential expression (DE) typically identifies many genes of interest. However, it is not clear which genes differ between extremes because they alter phenotypic liability and which show differences as a result of the extreme phenotype itself. We propose a formal model to distinguish between genes that are upstream and "cause" differential expression versus those for which differential expression is a result of the initial manifestation of phenotype. Relying on two sets of p-values, one from differential expression analysis and one from gene-specific association with phenotype (AP), and a gene coexpression or other gene-based network that serves as a bridge, our method identifies communities of genes more likely upstream or downstream of the phenotype. Our method consists of three major steps: 1) gene network construction, 2) evaluation of DE and AP signal within the network to infer hidden states, and 3) detection of gene communities. We apply our method to data that were generated to assess the biological basis of autism spectrum disorder (ASD) and Alzheimers disease (AD). Our results highlight neuronal and synaptic biology as being upstream of ASD, whereas downstream processes are all non-neuronal. For AD, our results are consistent with existing hypotheses; yet, they also lend support for a recent unifying hypothesis involving cofilin/actin biology.

genomics↗

bioRxiv2025

Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional Similarity

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique to determine the functionality of a gene by directly perturbing the DNA of the gene. One of the most intriguing recent problems is quantifying the similarity between CRISPR perturbations, for example, whether they upregulate the same set of downstream genes. In this context, genetic convergence refers to the phenomenon where CRISPR disruptions of different genes lead to a similar downstream outcome. Existing methods are mostly heuristic. We present XConTest, a two-step, cross-validated procedure for assessing the genetic convergence problem. The test statistics calculated from that procedure are approximately standard normal when the two perturbations have an orthogonal influence on the cell expression profile. We apply XConTest to two studies: an investigation of the common impact of a suite of autism genes, and a large-scale study of genes associated with immune response to determine sets of genes with common functionality.

genomics↗

bioRxiv2025

Predicting the unseen: a diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution

Predicting cellular responses to genetic perturbations is critical for advancing our understanding of gene regulation. While single-cell CRISPR perturbation assays such as Perturb-seq provide direct measurements of gene function, the scale of these experiments is limited by cost and feasibility. This motivates the development of computational approaches that can accurately infer responses to unmeasured perturbations from related experimental data. We introduce dbDiffusion, a generative framework that integrates diffusion models with classifier-free guidance derived from perturbation information, operating in latent space through a variational autoencoder (VAE). Diffusion models are probabilistic generative models that approximate data distributions by reversing a Markovian diffusion process, progressively denoising Gaussian noise into structured outputs. By exploiting biological similarities in gene expression profiles and relationships among perturbations, dbDiffusion enables the conditional generation of gene expressions for previously unobserved perturbations. In contrast to competing approaches, dbDiffusion does not rely on LLM or foundation models, which have been found to yield unsatisfactory results. Rather, it leverages embeddings derived from measured perturbations to generalize to unseen pertur-bations, effectively transferring information across related experimental conditions. In benchmarking against state-of-the-art methods on Perturb-seq datasets, dbDiffusion demonstrates superior accuracy in predicting perturbation responses. A methodological innovation of dbDiffusion is the integration of prediction-powered inference, which corrects for biases inherent in generative models and enables statistically rigorous downstream tasks, including identification of differentially expressed genes. By combining deep generative modeling with principled inference, dbDiffusion establishes a scalable computational framework for predicting and analyzing transcriptomic perturbation responses, significantly extending the utility of Perturb-seq experiments.

genomics↗

bioRxiv2025

A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism

MotivationGene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies. ResultsWe use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate. Availability and ImplementationThe implementation code and publicly available data are provided at: https://github.com/HaeunM/TADA-RD.

bioinformatics↗

bioRxiv2025

Causal differential expression analysis under unmeasured confounders with causarray

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce causarray, a robust causal inference framework for analyzing array-based genomic data at both pseudo-bulk and single-cell levels under unmeasured confounding. causarray integrates a generalized confounder adjustment method to account for unmeasured confounders and employs semiparametric inference with flexible machine learning techniques to ensure robust statistical estimation of treatment effects. Benchmarking results show that causarray robustly separates treatment effects from confounders while preserving biological signals across diverse settings. We also apply causarray to two single-cell genomic studies: (1) an in vivo Perturb-seq study of autism risk genes in developing mouse brains and (2) a case-control study of Alzheimers disease using three human brain transcriptomic datasets. In these applications, causarray identifies clustered causal effects of multiple autism risk genes and consistent causally affected genes across Alzheimers disease datasets, uncovering biologically relevant pathways directly linked to neuronal development and synaptic functions that are critical for understanding disease pathology.

genomics↗

bioRxiv2024

Augmented Doubly Robust Post-Imputation Inference for Proteomic data

Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such data is challenging due to the large proportion of missing values. A common strategy to address this issue is to utilize an imputed dataset, which often introduces systematic bias into down-stream analyses if the imputation errors are ignored. In this paper, we propose a statistical framework inspired by doubly robust estimators that offers valid and efficient inference for proteomic data. Our framework combines powerful machine learning tools, such as variational autoencoders, to augment the imputation quality with high-dimensional peptide data, and a parametric model to estimate the propensity score for debiasing imputed outcomes. Our estimator is compatible with the double machine learning framework and has provable properties. Simulation studies verify its empirical superiority over other existing procedures. In application to both single-cell proteomic data and bulk-cell Alzheimers Disease data our method utilizes the imputed data to gain additional, meaningful discoveries and yet maintains good control of false positives.

bioinformatics↗

bioRxiv2023

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

BackgroundSingle-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. ResultsWe develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. ConclusionseSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

genomics↗

bioRxiv2023

Evaluating and Improving Health Equity and Fairness of Polygenic Scores

Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.

genetics↗

bioRxiv2023

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method -- SCEPTRE low-MOI -- that resolves these analysis challenges and demonstrates improved calibration and power.

genomics↗

bioRxiv2022

Chemical bonds in collagen rupture selectivelyunder tensile stress

Collagen fibres are the main constituent of the extracellular matrix, and fulfil an important role in the structural stability of living multicellular organisms. An open question is how collagen absorbs pulling forces, and if the applied forces are strong enough to break bonds, what mechanisms underlie this process. As experimental studies on this topic are challenging, simulations are an important tool to further our understanding of these mechanisms. Here, we present pulling simulations of collagen triple helices, revealing the molecular mechanisms induced by tensile stress. At lower forces, pulling alters the configuration of proline residues leading to an effective absorption of applied stress. When forces are strong enough to introduce bond ruptures, these are located preferentially in X-position residues. Reduced backbone flexibility, for example through mutations or cross linking, weakens tensile resistance, leading to localised ruptures around these perturbations. In fibre-like segments, a significant overrepresentation of ruptures in proline residues compared to amino acid contents is observed. This study confirms the important role of proline in the structural stability of collagen, and adds detailed insight into the molecular mechanisms underlying this observation.

molecular biology↗

bioRxiv2022

Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation

Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly. Existing mosaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embeddings for the latent cell space and are not designed for accurate imputation of missing molecular layers. By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and impute multimodal datasets with mosaic measurements. A key advance is the use of a missing mask for learning the conditional distribution of unobserved modalities and features, which makes scVAEIT flexible to combine different panels of measurements from multimodal datasets accurately and in an end-to-end manner. Imputing the masked features serves as a supervised learning procedure while preventing overfitting by regularization. Focusing on gene expression, protein abundance, and chromatin accessibility, we validate that scVAEIT robustly imputes the missing modalities and features of cells biologically different from the training data. scVAEIT also adjusts for batch effects while maintaining the biological variation, which provides better latent representations for the integrated datasets. We demonstrate that scVAEIT significantly improves integration and imputation across unseen cell types, different technologies, and different tissues. Significance StatementSingle-cell multimodal assays provide an unprecedented opportunity for investigating heterogeneity of cell types and novel associations with disease and development. Although analyses of such multimodal datasets have the potential to provide new insights that cannot be inferred with a single modality, access typically requires the integration of multiple data sources. We propose a probabilistic variational autoencoder model for mosaic integration, which involves merging data sources that include features shared across datasets and features exclusive to a single data source. Our model is designed to provide a lower dimensional representation of the cells for visualization, clustering, and other downstream tasks; accurate imputation of missing features and observations; and transfer learning for robustly imputing new datasets when only partial measurements are available.

genetics↗