New protocols and computational tools for scRNAseq analysis

Abstract

The fast development of single cell RNA sequencing (scRNAseq) presents new challenges in data analysis and opportunities for protocol development. To address challenges in data preprocessing, I 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. Results from scPipe can be used as input for downstream analyses and can be easily incorporated into R-based pipelines with other tools. In order to compare different computational methods for scRNAseq data, I generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create `pseudo cells' from up to five distinct cancer cell lines. Multiple datasets were generated using both droplet and plate-based scRNAseq protocols and processed by scPipe. We found pipelines suited to different types of data for different tasks. Our data and analysis provide a comprehensive framework for benchmarking most common scRNA-seq analysis steps. Finally, I developed single cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, I performed a comprehensive characterization of the full-length isoform landscape in single cells of different types and species and found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. The datasets, protocols and tools that I developed and generated are useful resources for the single cell research community.