scRNA-seq mixology: towards better benchmarking of single cell RNA-seq protocols and analysis methods
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 to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods. 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 3 distinct cancer cell lines. Altogether we generated 10 datasets using a combination of droplet and plate-based scRNA-seq protocols, with varying data quality, population heterogeneity and noise levels. Using these benchmark datasets, we compared different protocols, evaluated the spike-in standard and multiple data analysis methods for tasks ranging from normalization and imputation, to clustering, trajectory analysis and data integration. Evaluation of methods across multiple datasets revealed some that performed well in general and others that suited specific situations. Our dataset and analysis provide a comprehensive comparison framework for benchmarking most popular scRNA-seq analysis tasks.