25 papers · ranked by Valyu relevance
Chen Yang, Xianyang Zhang, Jun Chen
Coarsening Visium HD resolution from 8 to 64 µm can flip cell-type co-localization from negative to positive (***r = −0.12 → +0.80*), yet many widely used compositional deconvolution workflows require coarsening or subsampling at million-bin scale. Here we introduce FlashDeconv, which combines leverage-score importance…
Luis Alonso, Idoia Ochoa, Ángel Rubio
Sequencing-based spatial transcriptomics has revolutionized the study of tissue architecture, but its ‘spots’ often contain multiple cells, creating a key computational challenge, termed deconvolution, to decipher each spot’s cell-type composition. Reference-free deconvolution methods avoid the need for a matched…
Chen Yang, Xianyang Zhang, Jun Chen
Coarsening Visium HD resolution from 8 to 64 μm can flip cell-type colocalization from negative to positive $((r=-0.12\rightarrow+0.80))$, yet many widely used compositional deconvolution workflows require coarsening or subsampling at million-bin scale. Here we introduce FlashDeconv, which combines leverage-score…
Weiyi Wu, Xinwen Xu, Chongyang Gao, Xingjian Diao + 3 more
Whole slide images, with their gigapixel-scale panoramas of tissue samples, are pivotal for precise disease diagnosis. However, their analysis is hindered by immense data size and scarce annotations. Existing MIL methods face challenges due to the fundamental imbalance where a single bag-level label must guide the…
Kubal, Sharvaj, Graham, Naomi + 10 more
We present an approach to denoising spatial transcriptomics images that is particularly effective for uncovering cell identities in the regime of ultra-low sequencing depths, and also allows for interpolation of gene expression. The method – Spatial Transcriptomics via Adaptive Regularization and Kernels (STARK) –…
Phuong Vo, Yuehua Cui
Cell-type deconvolution has been instrumental for the analysis of spatial transcriptomics (ST) data to reveal underlying tissue heterogeneity. Although reference-based methods have been widely explored, practical limitations, particularly the need for matched single-cell RNA-seq data sets, highlight the value of robust…
Juan Liang, Jiuxi Huang, Chenxi Xi, Yun Wang + 2 more
The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix…
Wen-Ting Wang, Wei-Ying Wu, Hao-Yun Huang, Xuan-Chun Wang
We present the Spatial Adapter, a parameter-efficient post-hoc layer that equips any frozen first-stage predictor with a structured spatial representation of its residual field and an induced closed-form spatial covariance. The adapter operates as a cascade second stage on residuals, jointly learning a spatially…
Anirban Chakraborty, Brian Neelon, Andrew Lawson, Peggi Angel + 2 more
As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial…
Daniel Tinoco, Raquel Menezes, Carlos Baquero, Alexandra Silva
Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and…
Art Taychameekiatchai, Xiaowei Zhan, Guanghua Xiao, Peifeng Ruan
Spatial transcriptomics technologies enable measurement of gene expression while preserving spatial tissue organization, but they remain highly sensitive to technical variability such as library size differences, slide-level effects, and spatial artifacts. Most existing normalization approaches treat normalization as a…
Kylie Yeung, Christine Tobler, Rolf F. Schulte, Benjamin White + 8 more
Image reconstruction in Magnetic Resonance Imaging (MRI) is fundamentally a linear inverse problem, such that the image can be recovered via explicit pseudoinversion of the encoding matrix by solving \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb}…
Authors not listed
Collective variables (CVs) are essential for interpreting and accelerating rare events in molecular simulations. However, their design remains limited by the requirement of differentiability with respect to atomic coordinates. This constraint excludes many powerful structural descriptors that are routinely used for…
Sofia Agostoni, Lisa Cuneo, Christian Daniele, Giacomo Garré + 4 more
Image Scanning Microscopy (ISM) is a fluorescence imaging technique that combines detector-array acquisition and computational reconstruction to achieve the theoretical resolution of an ideal confocal microscope, i.e., one operating with an infinitesimally small pinhole, while maintaining high signal-to-noise ratio.…
Ryan P Pitsinger, Murthy N Guddati
Objective. Shear wave elastography (SWE) is widely used for elasticity imaging, but conventional implementations remain confined to two-dimensional (2D) measurement planes and lack sensitivity to viscosity, an emerging biomarker linking shear wave dissipation and disease progression. The objective of this work is to…
Authors not listed
In the United States, people of color are disproportionately and unjustly exposed to air pollution. Historically, environmental policy has emphasized aggregate emission reductions; yet major emission reduction scenarios do not sufficiently mitigate relative exposure disparities. Here, we show that without focusing on…
Ville-Veikko Wettenhovi, Ari Hietanen, Nargiza Djurabekova, Kati Niinimäki + 2 more
Purpose Iterative model-based image reconstruction algorithms in cone beam computed tomography (CBCT) require repetitive forward and backward projection operations. We compare the quality of the branchless distance-driven (BDD) projector in iterative CBCT reconstruction with ray- and voxel-based methods in both regular…
Stefano Aleotti, Davide Bianchi, Florian Bossmann, Marco Donatelli + 1 more
We study the graph Laplacian operator as a regularizer in a generalized Tikhonov framework for linear ill-posed problems. The Laplacian is updated iteratively from the current reconstruction, so that progressively sharper structural information about the solution is fed into the regularization term. We introduce three…
Mohammad Faiz Iqbal Faiz, Elliot Jokl, Rachel Jennings, Karen Piper Hanley + 3 more
Spatial transcriptomics is rapidly advancing toward single cell level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical…
Macrina Lobo, Ziqi Zhang, Xiuwei Zhang
Spot-based spatial transcriptomics (ST) captures aggregated transcriptomic profiles at spatial locations (spots) in tissue slices. Cell type deconvolution methods decode each spot and estimate the proportion of every cell type in the spot, necessary for uncovering spatial cell type distributions for further downstream…
Jiayu Weng, Alec Kirkley
Regionalization aims to partition a spatial domain into contiguous regions that share similar characteristics, enabling more effective spatial analysis, policy making, and resource management. Existing approaches for spatial regionalization typically rely on static spatial snapshots rather than evolving time series.…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Authors not listed
Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…