Author

Tom W Ouellette

Ontario Institute for Cancer ResearchORCID 0000-0002-5052-7797Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1

Frequent Co-Authors

Philip AwadallaiD· 2 shared Amy Xin Wei ZhangiD· 1 shared Anna DoddiD· 1 shared Ashton Cook· 1 shared Ayah Elqaderi· 1 shared Barbara T. GrünwaldiD· 1 shared Daniela Bevacqua· 1 shared Erica Sophia Tsang· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersCancer Biology

Philip AwadallaORCID

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Author profile →

3 papers

Recent·Most cited

Transcriptomic, Genomic, and Clinical Characterization of Morphological Classes in Localized and Metastatic Pancreatic Cancer

Eugenia Flores Figueroa, Yuanchang Fang, Ayah Elqaderi, Maryam Monajemzadeh, Amy Xin Wei Zhang, Gun-Ho Jang, Michelle Chan‐Seng‐Yue, Karen Ng, Tom W Ouellette, Stephanie Ramotar, Daniela Bevacqua, Shawn Hutchinson, Rachel Ding, Sheng-Beng Liang, Syeda Mariam Hasnain, , Robert C. Grant

This study characterizes morphological classes in pancreatic cancer using transcriptomic, genomic, and clinical data to identify molecular associations and prognostic implications.

bioRxiv · Apr 10, 2026 · PreprintCancer Biology

A Pan-Cancer Single-Cell Compendium of Intratumoural Heterogeneity.

Ido Nofech-Mozes, Ashton Cook, Tom W Ouellette, Sara Hafezi‐Bakhtiari, Philip Awadalla, Sagi Abelson

This study analyzes over 3.6 million single cells from 1,000 tumors across 14 cancer types to identify heterogeneous cancer cell states linked to patient survival and therapeutic vulnerabilities.

bioRxiv · Jan 7, 2026 · PreprintCancer Biology

Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning

Tom W Ouellette, Philip Awadalla

This paper introduces TumE, a synthetic supervised learning method that improves accuracy and speed in inferring cancer evolution from single tumour biopsies.

PLoS Computational Biology · Apr 28, 2022Peer-reviewedBioinformatics