Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

Bob Chen; Marisol A Ramirez-Solano; Cody N Heiser; Qi Liu; Ken S Lau

doi:10.1016/j.xpro.2021.100450

Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

STAR Protoc. 2021 Apr 17;2(2):100450. doi: 10.1016/j.xpro.2021.100450. eCollection 2021 Jun 18.

Authors

Bob Chen^{1

2}, Marisol A Ramirez-Solano³, Cody N Heiser^{1

2}, Qi Liu³, Ken S Lau^{2

4

5}

Affiliations

¹ Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
² Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA.
³ Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴ Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁵ Vanderbilt Ingram Cancer Center, Nashville, TN, USA.

Abstract

Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as "one-stop shops" for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018), Heiser et al. (2020), and Heiser and Lau (2020).

Keywords: Bioinformatics; RNA-seq.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Databases, Nucleic Acid*
RNA-Seq*
Sequence Analysis, RNA*
Single-Cell Analysis*
Software*

Abstract

Publication types

MeSH terms

Grants and funding