Author

Claire Marceaux

The University of MelbourneORCID 0000-0002-9107-8772Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:spatial transcriptomics· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1

Frequent Co-Authors

Marie-Liesse Asselin-LabatiD· 3 shared Belinda PhipsoniD· 2 shared Jack D. HywoodiD· 2 shared Kenta YokoteiD· 2 shared Agus SalimiD· 1 shared Chin Wee TaniD· 1 shared Daniel BateyiD· 1 shared Dharmesh Dinesh BhuvaiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Marie-Liesse Asselin-LabatORCID

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3 papers

Recent·Most cited

TNF-mediated myeloid-instructed CD14+CD4+ T cells within the tumor microenvironment are associated with poor survival in non-small cell lung cancer.

Claire Marceaux, Ilariya Tarasova, Daniel Batey, Kenta Yokote, Velimir Gayevskiy, Reema Jain, Marina Leiwe, Laurie Choux, Lucy Riley, Nina Tubau Ribera, Edward Yang, Jack D. Hywood, Michael Christie, Phillip N. Antippa, Terence Paul Speed, , Marie-Liesse Asselin-Labat

The paper identifies CD14+CD4+ T cells in NSCLC tumor microenvironment driven by TNF, linked to poor patient survival.

Cell Reports Medicine · Feb 1, 2026Peer-reviewedCancer Biology

Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

Jeremy VanderDoes, Claire Marceaux, Kenta Yokote, Marie-Liesse Asselin-Labat, Gregory Rice, Jack D. Hywood

This paper uses random forests to analyze spatial cell interactions in tumor microenvironments and shows their predictive power for patient outcomes.

PLoS Computational Biology · Jun 14, 2024Peer-reviewedBioinformatics

Library size confounds biology in spatial transcriptomics data

Dharmesh Dinesh Bhuva, Chin Wee Tan, Agus Salim, Claire Marceaux, Marie A. Pickering, Jinjin Chen, Malvika Kharbanda, Xinyi Jin, Ning Liu, Kristen Feher, Givanna Putri, Wayne D. Tilley, Theresa E. Hickey, Marie-Liesse Asselin-Labat, Belinda Phipson, Melissa J. Davis

This study shows that library size in spatial transcriptomics data confounds biological signals and recommends against normalizing it unless necessary.

Genome biology · Apr 18, 2024Peer-reviewedBioinformatics