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Computational cell cycle analysis of single cell RNA-Seq data

View ORCID ProfileMarmar Moussa, View ORCID ProfileIon I. Măndoiu
doi: https://doi.org/10.1101/2020.11.21.392613
Marmar Moussa
1University of Connecticut Health Center, Farmington, CT, USA
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  • For correspondence: marmar.moussa@gmail.com
Ion I. Măndoiu
2University of Connecticut, Storrs, CT, USA
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Abstract

The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • marmar.moussa{at}uconn.edu, ion{at}engr.uconn.edu

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.
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Posted November 22, 2020.
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Computational cell cycle analysis of single cell RNA-Seq data
Marmar Moussa, Ion I. Măndoiu
bioRxiv 2020.11.21.392613; doi: https://doi.org/10.1101/2020.11.21.392613
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Computational cell cycle analysis of single cell RNA-Seq data
Marmar Moussa, Ion I. Măndoiu
bioRxiv 2020.11.21.392613; doi: https://doi.org/10.1101/2020.11.21.392613

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