scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data

Luyi Tian; Shian Su; Xueyi Dong; Daniela Amann-Zalcenstein; Christine Biben; Azadeh Seidi; Douglas J Hilton; Shalin H Naik; Matthew E Ritchie

doi:10.1371/journal.pcbi.1006361

scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data

PLoS Comput Biol. 2018 Aug 10;14(8):e1006361. doi: 10.1371/journal.pcbi.1006361. eCollection 2018 Aug.

Authors

Luyi Tian^{1

2}, Shian Su¹, Xueyi Dong^{1

3}, Daniela Amann-Zalcenstein¹, Christine Biben¹, Azadeh Seidi⁴, Douglas J Hilton^{1

2}, Shalin H Naik¹, Matthew E Ritchie^{1

2

5}

Affiliations

¹ Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
² Department of Medical Biology, The University of Melbourne, Parkville, Australia.
³ College of Life Science, Zhejiang University, Hangzhou, Zhejiang Province, P.R. China.
⁴ Australian Genome Research Facility, Parkville, Australia.
⁵ School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia.

Abstract

Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorporate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses. To this end, we developed scPipe, an R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple protocols that include CEL-seq, MARS-seq, Chromium 10X, Drop-seq and Smart-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of open-source scRNA-seq analysis tools available in R/Bioconductor and beyond. The scPipe R package is available for download from https://www.bioconductor.org/packages/scPipe.

Publication types

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

MeSH terms

Animals
Base Sequence
Computational Biology / methods*
High-Throughput Nucleotide Sequencing
Humans
RNA / genetics
Sequence Analysis, RNA / methods*
Single-Cell Analysis / methods*
Software

Substances

RNA

Grants and funding

This work was supported by the National Health and Medical Research Council (NHMRC) Project Grants (GNT1143163 to MER, GNT1124812 to SHN and MER, GNT1062820 to SHN), Fellowship GNT1104924 to MER, the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (grant number 2018-182819 to MER), a Melbourne Research Scholarship to LT, Genomics Innovation Hub, Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.