Processing single-cell RNA-seq datasets using SingCellaR

被引：6

作者：

Wang, Guanlin ^{[1
,2
]}

Wen, Wei Xiong ^{[1
,2
]}

Mead, Adam J. ^{[1
,5
]}

Roy, Anindita ^{[1
,3
,4
,5
]}

Psaila, Bethan ^{[1
,5
]}

Thongjuea, Supat ^{[1
,2
,5
]}

机构：

[1] Univ Oxford, MRC Mol Haematol Unit, MRC WIMM, Oxford OX3 9DS, England

[2] Univ Oxford, Ctr Computat Biol, Med Res Council, Weatherall Inst Mol Med MRC WIMM, Oxford OX3 9DS, England

[3] Univ Oxford, Childrens Hosp, John Radcliffe Hosp, Dept Paediat, Oxford OX3 9DS, England

[4] Univ Oxford, MRC WIMM, Oxford OX3 9DS, England

[5] Oxford Biomed Res Ctr, Natl Inst Hlth Res NIHR, Oxford OX4 2PG, England

来源：

STAR PROTOCOLS | 2022年 / 3卷 / 02期

基金：

英国惠康基金; 英国医学研究理事会;

关键词：

Bioinformatics; RNAseq; Single Cell; Stem Cells; Systems biology;

D O I：

10.1016/j.xpro.2022.101266

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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).

引用

页数：55

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