Single Cell Comparison: Mixed Cell Lines


A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development. 


Here, we present the data from our cell line mixture (HEK293 and NIH3T3) experiments. These consist of two experiments (Mixture1 and Mixture2), each with multiple scRNA-seq methods. This dataset allowed us to better understand the ability of different methods to detect gene expression, in particular, distinguishing reads that are not derived from the same cell (detected as aligning to a different species). These data have been sampled to an equal number of reads per cell (see details in our preprint). The expression data are counts based (based on UMI counts for all methods except for Smart-seq2, which is based on read counts). One can switch between different experiments / methods / annotations by clicking the view options bar and using the tool bar at the right of the screen. Each method is displayed as a plot, with nGene mapping to the mouse genome (mm10) on the y-axis, nGene mapping to the human genome (hg19) on the x- axis. 


More details about the data in this portal can be found in out readme (README_Mixture.docx, available under the download tab) and in our preprint (

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Systematic comparative analysis of single cell RNA-sequencing methods