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

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.

Author summary

Biotechnologies that allow researchers to measure gene activity in individual cells are growing in popularity. This has resulted in an avalanche of custom analysis methods designed to deal with the complex data that arises from this technology. Although hundreds of analysis methods are available, relatively few deal with raw data processing in a holistic way. Our scPipe software has been developed to fill this gap. scPipe is the first fully integrated R package that deals with the raw sequencing reads from single cell gene expression studies, processing them to the point where biologically interesting downstream analyses can take place. By following community developed standards, scPipe is compatible with many other software packages for single cell analysis available from the open-source Bioconductor project, facilitating a complete beginning to end analysis of single cell gene expression data. This allows various biological questions to be answered, ranging from the identification of novel cell types to the discovery of new marker genes. scPipe promotes reproducibility and makes it easier for researchers to share results and code.