Computational Cell Cycle Analysis of Single Cell RNA-Seq Data

被引：0

作者：

Moussa, Marmar ^{[1
]}

Mandoiu, Ion I. ^{[2
]}

机构：

[1] Univ Connecticut, Sch Med, Farmington, CT 06030 USA

[2] Univ Connecticut, Storrs, CT USA

来源：

COMPUTATIONAL ADVANCES IN BIO AND MEDICAL SCIENCES | 2021年 / 12686卷

基金：

美国国家科学基金会;

关键词：

scRNA-Seq; Cell cycle; Cell order; Gene smoothness score; REVEALS; GENES;

D O I：

10.1007/978-3-030-79290-9_7

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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/.

引用

页码：71 / 87

页数：17

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