Bioinformatics Seminar
Time:
Venue: Na
11 June 2019
NaTools for preprocessing and benchmarking single cell RNA-sequencing data
Matthew RitchieWEHI Epigenetics & Development
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years ; bringing with it new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq data to address various biological questions. However ; the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically evaluate the performance of the many methods available. Here ; we designed and carried out a realistic benchmark experiment that included mixtures of single cells or 'pseudo cells' created by sampling admixtures of cells or RNA from up to 5 distinct cancer cell lines. Altogether we generated 13 datasets using droplet and plate-based scRNA-seq protocols ; compared multiple data analysis methods in combination for tasks ranging from normalization and imputation ; to clustering ; trajectory analysis and data integration. Evaluation across 3 ;913 analyses (methods x benchmark dataset combinations) using our CellBench Bioconductor software revealed pipelines suited to different types of data for different tasks. Our datasets and analyses present a comprehensive comparison framework for benchmarking most common scRNA-seq analysis tasks.;