---
title: "CellDot: Optimal-transport decontamination for imaging-based spatial transcriptomics"
id: "biorxiv-0-accurate-and-scalable-decontamination-of-imaging-based-spatial-transcriptomics"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-accurate-and-scalable-decontamination-of-imaging-based-spatial-transcriptomics"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.09.750350v1?rss=1"
published_at: "2026-09-15T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CellDot: Optimal-transport decontamination for imaging-based spatial transcriptomics
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-accurate-and-scalable-decontamination-of-imaging-based-spatial-transcriptomics
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.09.750350v1?rss=1)
- **Published At:** 2026-09-15T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Imaging-based **spatial transcriptomics** offers molecule-resolved, in situ gene expression profiling but is vulnerable to misassigned transcripts from segmentation errors, transcript spillover and 3D cell overlap. - Misassigned transcripts can distort cell-level expression profiles, obscure biological signals and complicate downstream interpretation. - Current decontamination approaches either discard suspect expression and lose signal or lack biologically grounded criteria for transcript reassignment. - The authors introduce **CellDot**, an optimal-transport framework that decides for each transcript whether to retain it in its host cell, reassign it to a neighboring cell, or remove it as background. - CellDot integrates reference-guided expression compatibility, spatial information and data-adaptive constraints to enable molecule-level correction that is both accurate and traceable. - Evaluations were performed across multiple human tumor datasets, where CellDot outperformed existing decontamination methods according to the authors' assessments. - CellDot restored spatial expression patterns consistent with independent cross-platform measurements and improved recovery of cellular states, intercellular communication and spatial niche programs. - The authors report CellDot is scalable and applicable to whole-transcriptome Atera datasets, which they state other methods could not process. - The work is presented as a preprint; it has not been peer reviewed and the manuscript reports no competing interests.
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Yuheng Chen 1 The Hong Kong University of Science and Technology; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yuheng%2BChen%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Chen%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYuheng%2BChen%2B) Yuyao Liu 2 HKUST * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yuyao%2BLiu%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Liu%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYuyao%2BLiu%2B) Zitong Chao 2 HKUST * [Find this author on Google 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PDF](https://www.biorxiv.org/content/10.64898/2026.09.09.750350v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5781862/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Imaging-based spatial transcriptomics enables molecule-resolved profiling of gene expression and tissue organization in situ. However, segmentation errors, transcript spillover and three-dimensional cell overlap can introduce misassigned transcripts into cell-level expression profiles, compromising biological interpretation and obscuring genuine signals. Existing methods either remove suspect expression at the cost of signal loss or lack a biologically grounded criterion for transcript assignment. Here we present CellDot, an optimal-transport framework that determines the fate of each transcript by retaining it in its host cell, reassigning it to a plausible neighboring cell or removing it as background. By integrating reference-guided expression compatibility with spatial information and data-adaptive constraints, CellDot enables accurate and traceable molecule-level correction while preserving biologically meaningful variation. In evaluations across multiple human tumor datasets, CellDot exhibited superior performance compared to existing decontamination methods, successfully restoring spatial expression patterns that matched independent cross-platform measurements. Moreover, it significantly enhanced the recovery of cellular states, intercellular communication, and spatial niche programs. Our experiments using real data demonstrated CellDot's scalability and established it as the only method applicable to a whole-transcriptome Atera dataset, underscoring its distinct advantages in the field of spatial transcriptomics. ### Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.09.09.750350v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/09/15/2026.09.09.750350.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport Yuheng Chen, Yuyao Liu, Zitong Chao, Shi Han, Yeqin Zeng, Baichen Yu, Fan Zhang, Angela Wu, Jiguang Wang, Hao Chen, Jiashun Xiao, Can Yang bioRxiv 2026.09.09.750350; doi: https://doi.org/10.64898/2026.09.09.750350 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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