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Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

Allen W Zhang, Ciara O'Flanagan, Elizabeth Chavez, Jamie LP Lim, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N McAlpine, Samuel Aparicio, Christian Steidl, Kieran R Campbell, Sohrab P Shah
doi: https://doi.org/10.1101/521914
Allen W Zhang
1 University of British Columbia;
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Ciara O'Flanagan
2 British Columbia Cancer Research Centre;
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Elizabeth Chavez
2 British Columbia Cancer Research Centre;
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Jamie LP Lim
2 British Columbia Cancer Research Centre;
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Andrew McPherson
2 British Columbia Cancer Research Centre;
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Matt Wiens
2 British Columbia Cancer Research Centre;
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Pascale Walters
2 British Columbia Cancer Research Centre;
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Tim Chan
2 British Columbia Cancer Research Centre;
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Brittany Hewitson
2 British Columbia Cancer Research Centre;
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Daniel Lai
2 British Columbia Cancer Research Centre;
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Anja Mottok
3 Ulm University Medical Centre;
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Clementine Sarkozy
2 British Columbia Cancer Research Centre;
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Lauren Chong
2 British Columbia Cancer Research Centre;
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Tomohiro Aoki
2 British Columbia Cancer Research Centre;
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Xuehai Wang
2 British Columbia Cancer Research Centre;
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Andrew P Weng
2 British Columbia Cancer Research Centre;
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Jessica N McAlpine
2 British Columbia Cancer Research Centre;
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Samuel Aparicio
2 British Columbia Cancer Research Centre;
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Christian Steidl
2 British Columbia Cancer Research Centre;
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Kieran R Campbell
1 University of British Columbia;
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  • For correspondence: kieran.campbell@stat.ubc.ca
Sohrab P Shah
4 Memorial Sloan Kettering Cancer Center
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  • For correspondence: shahs3@mskcc.org
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Abstract

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

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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.
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Posted January 16, 2019.
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Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers
Allen W Zhang, Ciara O'Flanagan, Elizabeth Chavez, Jamie LP Lim, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N McAlpine, Samuel Aparicio, Christian Steidl, Kieran R Campbell, Sohrab P Shah
bioRxiv 521914; doi: https://doi.org/10.1101/521914
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Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers
Allen W Zhang, Ciara O'Flanagan, Elizabeth Chavez, Jamie LP Lim, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N McAlpine, Samuel Aparicio, Christian Steidl, Kieran R Campbell, Sohrab P Shah
bioRxiv 521914; doi: https://doi.org/10.1101/521914

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