Journal article
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
PLOS COMPUTATIONAL BIOLOGY | PUBLIC LIBRARY SCIENCE | Published : 2018
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 ge..
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Awarded by National Health and Medical Research Council (NHMRC)
Awarded by Silicon Valley Community Foundation
Funding Acknowledgements
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.