Abstract
Background:The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods:Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results:We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions:In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.
Highlights
Single-cell RNA-sequencing studies have opened the way for new data-driven definitions of cell identity and function
For the gold standard dataset consisting of three cell types, half of the tested clustering methods overestimated the true number of different cell types in the data
As a consequence of the greater number of estimated clusters, the ARI_truth of the other clustering methods is lower than 0.8. To see whether these methods split cell types into several clusters or instead assign cells types randomly to clusters, we investigate the homogeneity of the clustering solutions with respect to the known labeling
Summary
Single-cell RNA-sequencing (scRNA-seq) studies have opened the way for new data-driven definitions of cell identity and function. No longer is a cell’s type determined by arbitrary hierarchies and their respective predefined markers. A cell’s transcriptional and epigenomic profile can be used[1] to accomplish this task. This is achieved using computational methods for scRNA-seq that characterize cells into novel and known cell types. Characterization consists of two steps: (i) unsupervised or semi-supervised clustering of same or similar cells into nonoverlapping groups, and (ii) labeling clusters, i.e. determining the cell type, or related cell types, represented by the cluster. We focus on the first step of this process
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