scPipe: a flexible data 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 to label individual cells and molecules 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 barcodes 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. We developed scPipe, an R package that allows barcode demultiplexing, read alignment and gene-level quantification and quality control of raw sequencing data generated by multiple 3 prime end sequencing protocols that include CEL-seq, MARS-seq and Drop-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 scRNA-seq analysis tools available in R/Bioconductor. The scPipe R package is available from https://github.com/LuyiTian/scPipe.