Computational cell cycle analysis of single cell RNA-Seq data
Update: 2020-11-22
Description
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.21.392613v1?rss=1
Authors: Moussa, M. M. R., Mandoiu, I. I.
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, GSS (Gene Smoothness Score) 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/.
Copy rights belong to original authors. Visit the link for more info
http://biorxiv.org/cgi/content/short/2020.11.21.392613v1?rss=1
Authors: Moussa, M. M. R., Mandoiu, I. I.
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, GSS (Gene Smoothness Score) 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/.
Copy rights belong to original authors. Visit the link for more info
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