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Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

Joseph Boen, Joel P. Wagner, Noemi Di Nanni
doi: https://doi.org/10.1101/2021.10.18.463991
Joseph Boen
1Summer Internship Program in Oncology Disease Area, Novartis Institutes of Biomedical Research, Cambridge MA, USA
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Joel P. Wagner
2Oncology Disease Area, Novartis Institutes of Biomedical Research, Cambridge MA, USA
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  • For correspondence: joel.wagner@novartis.com noemi.di_nanni@novartis.com
Noemi Di Nanni
3Oncology Disease Area, Novartis Institutes of Biomedical Research, Basel, Switzerland
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  • For correspondence: joel.wagner@novartis.com noemi.di_nanni@novartis.com
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ABSTRACT

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods’ results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

Competing Interest Statement

JPW is a shareholder of Novartis Pharmaceuticals.

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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 4.0 International license.
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Posted October 19, 2021.
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Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology
Joseph Boen, Joel P. Wagner, Noemi Di Nanni
bioRxiv 2021.10.18.463991; doi: https://doi.org/10.1101/2021.10.18.463991
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Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology
Joseph Boen, Joel P. Wagner, Noemi Di Nanni
bioRxiv 2021.10.18.463991; doi: https://doi.org/10.1101/2021.10.18.463991

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