Processing single-cell RNA-seq datasets using SingCellaR

Guanlin Wang; Wei Xiong Wen; Adam J Mead; Anindita Roy; Bethan Psaila; Supat Thongjuea

doi:10.1016/j.xpro.2022.101266

Processing single-cell RNA-seq datasets using SingCellaR

STAR Protoc. 2022 Apr 1;3(2):101266. doi: 10.1016/j.xpro.2022.101266. eCollection 2022 Jun 17.

Authors

Guanlin Wang^{1

2}, Wei Xiong Wen^{1

2}, Adam J Mead^{1

3}, Anindita Roy^{1

4

3}, Bethan Psaila^{1

3}, Supat Thongjuea^{1

2

3}

Affiliations

¹ MRC Molecular Haematology Unit, MRC WIMM, University of Oxford, Oxford OX3 9DS, UK.
² Centre for Computational Biology, Medical Research Council Weatherall Institute of Molecular Medicine (MRC WIMM), University of Oxford, Oxford OX3 9DS, UK.
³ National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford OX4 2PG, UK.
⁴ Department of Paediatrics, Children's Hospital, John Radcliffe Hospital, and MRC WIMM, University of Oxford, Oxford OX3 9DS, UK.

Abstract

Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021).

Keywords: Bioinformatics; RNAseq; Single Cell; Stem Cells; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Data Analysis*
Exome Sequencing
Humans
RNA-Seq
Single-Cell Analysis* / methods
Transcriptome / genetics

Abstract

Publication types

MeSH terms

Grants and funding