ArXiv Pub Date : 2025-03-15

Jin-Hong Du, Larry Wasserman, Kathryn Roeder

{"title":"Simultaneous inference for generalized linear models with unmeasured confounders.","authors":"Jin-Hong Du, Larry Wasserman, Kathryn Roeder","doi":"","DOIUrl":"","url":null,"abstract":"Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It begins by disentangling marginal and uncorrelated confounding effects to recover the latent coefficients. Subsequently, latent factors and primary effects are jointly estimated through lasso-type optimization. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish the identification conditions of various effects and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41171488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks. 识别局部连接模式对兴奋-抑制网络动力学的影响。

ArXiv Pub Date : 2025-03-15

Yuxiu Shao, David Dahmen, Stefano Recanatesi, Eric Shea-Brown, Srdjan Ostojic

{"title":"Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks.","authors":"Yuxiu Shao, David Dahmen, Stefano Recanatesi, Eric Shea-Brown, Srdjan Ostojic","doi":"","DOIUrl":"","url":null,"abstract":"Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of specific connectivity patterns that go well beyond what is expected from independent connections. While decades of influential research have demonstrated the strong role of the basic EI cell type structure, to which extent additional connectivity features influence dynamics remains to be fully determined. Here we examine the effects of pairwise connectivity motifs on the linear dynamics in EI networks using an analytical framework that approximates the connectivity in terms of low-rank structures. This low-rank approximation is based on a mathematical derivation of the dominant eigenvalues of the connectivity matrix and predicts the impact on responses to external inputs of connectivity motifs and their interactions with cell-type structure. Our results reveal that a particular pattern of connectivity, chain motifs, have a much stronger impact on dominant eigenmodes than other pairwise motifs. An overrepresentation of chain motifs induces a strong positive eigenvalue in inhibition-dominated networks and generates a potential instability that requires revisiting the classical excitation-inhibition balance criteria. Examining effects of external inputs, we show that chain motifs can on their own induce paradoxical responses where an increased input to inhibitory neurons leads to a decrease in their activity due to the recurrent feedback. These findings have direct implications for the interpretation of experiments in which responses to optogenetic perturbations are measured and used to infer the dynamical regime of cortical circuits.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Conditional Point Cloud Diffusion Model for Deformable Liver Motion Tracking Via a Single Arbitrarily-Angled X-ray Projection.

ArXiv Pub Date : 2025-03-13

Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li, Shunyu Yan, Chenyang Shen, Jing Wang, You Zhang

{"title":"A Conditional Point Cloud Diffusion Model for Deformable Liver Motion Tracking Via a Single Arbitrarily-Angled X-ray Projection.","authors":"Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li, Shunyu Yan, Chenyang Shen, Jing Wang, You Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Deformable liver motion tracking using a single X-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion model-based framework for accurate and robust liver motion tracking from arbitrarily angled single X-ray projections (PCD-Liver), which estimates volumetric liver motion by solving deformable vector fields (DVFs) of a prior liver surface point cloud based on a single X-ray image. The model is patient-specific and consists of two main components: a rigid alignment model to estimate the liver's overall shifts and a conditional point cloud diffusion model that further corrects for liver surface deformations. Conditioned on motion-encoded features extracted from a single X-ray projection via a geometry-informed feature pooling layer, the diffusion model iteratively solves detailed liver surface DVFs in a projection angle-agnostic manner. The liver surface motion estimated by PCD-Liver serves as a boundary condition for a U-Net-based biomechanical model to infer internal liver motion and localize liver tumors. A dataset of ten liver cancer patients was used for evaluation. The accuracy of liver point cloud motion estimation was assessed using root mean square error (RMSE) and 95th-percentile Hausdorff distance (HD95), while liver tumor localization error was quantified using center-of-mass error (COME). The mean (standard deviation) RMSE, HD95, and COME of the prior liver or tumor before motion estimation were 8.86(1.51) mm, 10.88(2.56) mm, and 9.41(3.08) mm, respectively. After PCD-Liver motion estimation, the corresponding values improved to 3.59(0.28) mm, 4.29(0.62) mm, and 3.45(0.96) mm. Under highly noisy conditions, PCD-Liver maintained stable performance. This study presents an accurate and robust framework for deformable liver motion estimation and tumor localization in image-guided radiotherapy.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A theory of ecological invasions and its implications for eco-evolutionary dynamics.

ArXiv Pub Date : 2025-03-13

Zhijie Feng, Emmy Blumenthal, Pankaj Mehta, Akshit Goyal

{"title":"A theory of ecological invasions and its implications for eco-evolutionary dynamics.","authors":"Zhijie Feng, Emmy Blumenthal, Pankaj Mehta, Akshit Goyal","doi":"","DOIUrl":"","url":null,"abstract":"Predicting the outcomes of species invasions is a central goal of ecology, a task made especially challenging due to ecological feedbacks. To address this, we develop a general theory of ecological invasions applicable to a wide variety of ecological models: including Lotka-Volterra models, consumer resource models, and models with cross feeding. Importantly, our framework remains valid even when invading evolved (non-random) communities and accounts for invasion-driven species extinctions. We derive analytical expressions relating invasion fitness to invader abundance, shifts in the community, and extinction probabilities. These results can be understood through a new quantity we term ``dressed invasion fitness'', which augments the traditional notion of invasion fitness by incorporating ecological feedbacks. We apply our theory to analyze short-term evolutionary dynamics through a series of invasions by mutants whose traits are correlated with an existing parent. We demonstrate that, generically, mutants and parents can coexist, often by driving the extinction of low-abundance species. We validate theoretical predictions against experimental datasets spanning ecosystems from plants to microbial protists. Our work highlights the central role of ecological feedbacks in shaping community responses to invasions and mutations, suggesting that parent-mutant coexistence is widespread in eco-evolutionary dynamics.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder. 混合效应深度学习通过量化和可视化批次效应，对单细胞 RNA 测序数据进行可解释的分析。

ArXiv Pub Date : 2025-03-13

Aixa X Andrade, Son Nguyen, Albert Montillo

{"title":"scMEDAL for the interpretable analysis of single-cell transcriptomics data with batch effect visualization using a deep mixed effects autoencoder.","authors":"Aixa X Andrade, Son Nguyen, Albert Montillo","doi":"","DOIUrl":"","url":null,"abstract":"scRNA-seq data has the potential to provide new insights into cellular heterogeneity and data acquisition; however, a major challenge is unraveling confounding from technical and biological batch effects. Existing batch correction algorithms suppress and discard these effects, rather than quantifying and modeling them. Here, we present scMEDAL, a framework for single-cell Mixed Effects Deep Autoencoder Learning, which separately models batch-invariant and batch-specific effects using two complementary autoencoder networks. One network is trained through adversarial learning to capture a batch-invariant representation, while a Bayesian autoencoder learns a batch-specific representation. Comprehensive evaluations spanning conditions (e.g., autism, leukemia, and cardiovascular), cell types, and technical and biological effects demonstrate that scMEDAL suppresses batch effects while modeling batch-specific variation, enhancing accuracy and interpretability. Unlike prior approaches, the framework's fixed- and random-effects autoencoders enable retrospective analyses, including predicting a cell's expression as if it had been acquired in a different batch via genomap projections at the cellular level, revealing the impact of biological (e.g., diagnosis) and technical (e.g., acquisition) effects. By combining scMEDAL's batch-agnostic and batch-specific latent spaces, it enables more accurate predictions of disease status, donor group, and cell type, making scMEDAL a valuable framework for gaining deeper insight into data acquisition and cellular heterogeneity.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing Human-Induced Variability in Quantitative Angiography for Robust and Explainable AI-Based Occlusion Prediction.

ArXiv Pub Date : 2025-03-13

Parmita Mondal, Mohammad Mahdi Shiraz Bhurwani, Swetadri Vasan Setlur Nagesh, Pui Man Rosalind Lai, Jason Davies, Elad Levy, Kunal Vakharia, Michael R Levitt, Adnan H Siddiqui, Ciprian N Ionita

引用次数: 0

Differentiable Folding for Nearest Neighbor Model Optimization.

ArXiv Pub Date : 2025-03-12

Ryan K Krueger, Sharon Aviran, David H Mathews, Jeffrey Zuber, Max Ward

{"title":"Differentiable Folding for Nearest Neighbor Model Optimization.","authors":"Ryan K Krueger, Sharon Aviran, David H Mathews, Jeffrey Zuber, Max Ward","doi":"","DOIUrl":"","url":null,"abstract":"The Nearest Neighbor model is the $textit{de facto}$ thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains $approx13,000$ underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in $textit{differentiable folding}$, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanoreceptive A$β$ primary afferents discriminate naturalistic social touch inputs at a functionally relevant time scale.

ArXiv Pub Date : 2025-03-12

Shan Xu, Steven C Hauser, Saad S Nagi, James A Jablonski, Merat Rezaei, Ewa Jarocka, Andrew G Marshall, Håkan Olausson, Sarah McIntyre, Gregory J Gerling

{"title":"Mechanoreceptive A$β$ primary afferents discriminate naturalistic social touch inputs at a functionally relevant time scale.","authors":"Shan Xu, Steven C Hauser, Saad S Nagi, James A Jablonski, Merat Rezaei, Ewa Jarocka, Andrew G Marshall, Håkan Olausson, Sarah McIntyre, Gregory J Gerling","doi":"","DOIUrl":"","url":null,"abstract":"Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered social touch expressions. Leveraging statistical and classification analyses, we found that single units of multiple mechanoreceptive A$beta$ subtypes, especially slowly adapting type II (SA-II) and fast adapting hair follicle afferents (HFA), can reliably differentiate social touch expressions at accuracies similar to human recognition. We then identified the most informative firing patterns of SA-II and HFA afferents, which indicate that average durations of 3-4 s of firing provide sufficient discriminative information. Those two subtypes also exhibit robust tolerance to spike-timing shifts of up to 10-20 ms, varying with touch expressions due to their specific firing properties. Greater shifts in spike-timing, however, can change a firing pattern's envelope to resemble that of another expression and drastically compromise an afferent's discrimination capacity. Altogether, the findings indicate that SA-II and HFA afferents differentiate the skin contact of social touch at time scales relevant for such interactions, which are 1-2 orders of magnitude longer than those for non-social touch.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals. 利用机器学习分析静息态 fMRI BOLD 信号识别阿片类药物使用障碍的大脑功能网络

ArXiv Pub Date : 2025-03-11

Ahmed Temtam, Megan A Witherow, Liangsuo Ma, M Shibly Sadique, F Gerard Moeller, Khan M Iftekharuddin

{"title":"Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals.","authors":"Ahmed Temtam, Megan A Witherow, Liangsuo Ma, M Shibly Sadique, F Gerard Moeller, Khan M Iftekharuddin","doi":"","DOIUrl":"","url":null,"abstract":"Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests time-frequency characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to traditional analysis techniques. However, existing studies of OUD analyze BOLD signals using measures computed across all time points. This study, for the first time in the literature, employs data-driven machine learning (ML) for time-frequency analysis of local neural activity within key functional networks to differentiate OUD subjects from healthy controls (HC). We obtain time-frequency features based on rs-fMRI BOLD signals from the default mode network (DMN), salience network (SN), and executive control network (ECN) for 31 OUD and 45 HC subjects. Then, we perform 5-fold cross-validation classification (OUD vs. HC) experiments to study the discriminative power of functional network features while taking into consideration significant demographic features. The DMN and SN show the most discriminative power, significantly (p < 0.05) outperforming chance baselines with mean F1 scores of 0.7097 and 0.7018, respectively, and mean AUCs of 0.8378 and 0.8755, respectively. Follow-up Boruta ML analysis of selected time-frequency (wavelet) features reveals significant (p < 0.05) detail coefficients for all three functional networks, underscoring the need for ML and time-frequency analysis of rs-fMRI BOLD signals in the study of OUD.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Omics Framework for Survival Mediation Analysis of High-Dimensional Proteogenomic Data.

ArXiv Pub Date : 2025-03-11

Seungjun Ahn, Weijia Fu, Maaike van Gerwen, Lei Liu, Zhigang Li

{"title":"A Multi-Omics Framework for Survival Mediation Analysis of High-Dimensional Proteogenomic Data.","authors":"Seungjun Ahn, Weijia Fu, Maaike van Gerwen, Lei Liu, Zhigang Li","doi":"","DOIUrl":"","url":null,"abstract":"Survival analysis plays a crucial role in understanding time-to-event (survival) outcomes such as disease progression. Despite recent advancements in causal mediation frameworks for survival analysis, existing methods are typically based on Cox regression and primarily focus on a single exposure or individual omics layers, often overlooking multi-omics interplay. This limitation hinders the full potential of integrated biological insights. In this paper, we propose SMAHP, a novel method for survival mediation analysis that simultaneously handles high-dimensional exposures and mediators, integrates multi-omics data, and offers a robust statistical framework for identifying causal pathways on survival outcomes. This is one of the first attempts to introduce the accelerated failure time (AFT) model within a multi-omics causal mediation framework for survival outcomes. Through simulations across multiple scenarios, we demonstrate that SMAHP achieves high statistical power, while effectively controlling false discovery rate (FDR), compared with two other approaches. We further apply SMAHP to the largest head-and-neck carcinoma proteogenomic data, detecting a gene mediated by a protein that influences survival time.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11952585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ArXiv最新文献