Biology subjects

Halmos, P.

Publications and source records attributed to Halmos, P..

4 recordsLinked to original sources

bioRxiv2026

Multimodal spatial alignment and morphology mapping with MOSAICField

Recent efforts to build comprehensive tissue and tumor atlases leverage diverse spatial technologies to measure transcriptomic, proteomic, epigenetic, and other modalities with hundreds to thousands of features at thousands to millions of spatially resolved locations in a tissue slice. Integrating such data across spatial technologies that differ in molecular features, spatial resolution, and tissue morphology remains a major challenge. We introduce MultimOdal Spatial Alignment and Integration with Coordinate neural Field (MOSAICField), a unified framework for aligning spatial slices across arbitrary combinations of experimental modalities. MOSAICField computes two types of spatial alignments across multiple slices from the same tissue: physical alignment, which reconstructs a contiguous 3D model of the original tissue, and morphological alignment, which maps distinct morphological or anatomical structures, such as ducts, veins, or neurons, that may traverse the tissue at different angles relative to the direction of slicing. MOSAICField computes both alignments using a deep neural network that estimates a nonlinear deformation field with a multimodal feature loss. We evaluate MOSAICField on simulated data and a prostate cancer sample from the Human Tumor Atlas Network (HTAN), containing more than a dozen spatial slices with multimodal profiling data. MOSAICField constructs an accurate 3D tumor model, tracks the architecture of the prostatic ductal system, and improves analysis of features within and across modalities, outperforming existing methods.

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bioRxiv2025

Riemannian Metric Learning for Alignment of Spatial Multiomics

Recent spatial technologies measure the transcriptome, epigenome, proteome, metabolome and other modalities from thousands of cells across a tissue. Most assays typically profile only one modality from a tissue slice, raising the question of how to align spatial data from heterogeneous feature spaces. While multiple approaches have been developed for multi-modal integration of single-cell datasets, few existing techniques perform spatial alignment across arbitrary modalities incorporating both spatial and feature information. We introduce Manifold Gromov-Wasserstein (MGW), a metric-learning framework that exploits the product structure of spatial multiomics to infer modality-specific Riemannian pull-back metrics with neural fields. MGW aligns Riemannian distances induced by these metrics via Gromov-Wasserstein optimal transport, yielding a hyperparameter-free cost across arbitrary modalities sharing a spatial base. The formulation enjoys theoretical invariances - including orthogonal transformations of the spatial and feature domains as well as global feature scalings. We demonstrate the advantages of MGW on multiple alignment tasks, including Stereo-Seq spatiotemporal transcriptomics of mouse embryo, Xenium and Visium spatial transcriptomics of colorectal cancer, and spatial metabolomics-transcriptomics from human striatum and kidney cancer. MGW recovers biologically meaningful correspondences and spatially coherent tissue structures, outperforming existing OT- and non-OT-based multi-modal baselines. Code availabilitySoftware is available at https://github.com/raphael-group/MGW

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bioRxiv2025

Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport

Deriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints; (2) coarse-graining these trajectories into a differentiation map, or collection of transitions between cell types, rather than individual cells. We introduce Hidden-Markov Optimal Transport (HM -OT), an algorithm that simultaneously groups cells into cell types and learns transitions between these cell types from developmental transcriptomics time series. HM -OT uses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost. We assume that the law governing cell-type trajectories is characterized by the joint law on consecutive time points, tantamount to a Markov assumption on these latent trajectories. HM -OT can learn these clusterings in a fully unsupervised manner or can generate the least-cost cell type differentiation map consistent with a given set of cell type labels. We validate the unsupervised clusters and cell type differentiation map output by HM -OT on a Stereo-seq dataset of zebrafish development, and we demonstrate the scalability of HM -OT to a massive Stereo-seq dataset of mouse embryonic development. Code availabilitySoftware is available at https://github.com/raphael-group/HM-OT

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bioRxiv2024

DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain. Code availabilitySoftware is available at https://github.com/raphael-group/DeST_OT

bioinformatics↗