Author

Peter Halmos

Princeton University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Assay:spatial transcriptomics· 4 Approach:optimal transport· 3 Approach:statistical model· 3 Research Focus:dimensionality reduction· 2 Organism:human· 2 Organism:mouse· 2 Research Focus:trajectory inference· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

Benjamin J. RaphaeliD· 4 shared Julian GoldiD· 3 shared Xinhao LiuiD· 3 shared Erik P. StorrsiD· 1 shared Feng CheniD· 1 shared Hongyu ZhengiD· 1 shared Li Ding· 1 shared Li DingiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 23 mutual co-author links

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4 shared papersBioinformatics

Benjamin J. RaphaelORCID

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Author profile →

4 papers

Recent·Most cited

Riemannian Metric Learning for Alignment of Spatial Multiomics

Peter Halmos, Yufan Xia, Benjamin J. Raphael

This paper introduces MGW, a metric-learning framework for aligning spatial multi-omics data across different modalities, showing improved alignment over existing methods.

Bioinformatics · Jul 1, 2026Peer-reviewedBioinformatics

Multimodal spatial alignment and morphology mapping with MOSAICField

Xinhao Liu, Hongyu Zheng, Peter Halmos, Julian Gold, Erik P. Storrs, Li Ding, Benjamin J. Raphael

MOSAICField aligns spatial slices across modalities to reconstruct 3D tissue models and map morphology, outperforming existing methods on simulated and prostate cancer data.

bioRxiv · Mar 13, 2026 · PreprintBioinformatics

DeST-OT: Alignment of Spatiotemporal Transcriptomics Data

Peter Halmos, Xinhao Liu, Julian Gold, Feng Chen, Li Ding, Benjamin J. Raphael

DeST-OT aligns spatiotemporal transcriptomics data from developmental timepoints using optimal transport to model cell growth, death, and differentiation.

Cell Systems · Jan 27, 2025Peer-reviewedBioinformatics

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

Peter Halmos, Julian Gold, Xinhao Liu, Benjamin J. Raphael

This paper introduces HM-OT to infer cell type differentiation maps from developmental transcriptomics time series by simultaneously clustering cells and learning transitions between cell types.

Lecture notes in computer science · Jan 1, 2025Peer-reviewedBioinformatics