Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we 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. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normalization and imputation to clustering, trajectory analysis and data integration. Evaluation revealed 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.
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Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data
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We thank C. Weeden and M.-L. Asselin-Labat for providing the cell lines used in this study, J. Schreuder and D. Lin for assistance in conducting experiments and I. Virshup for assistance in the data integration analysis. This work was supported by funding from the National Health and Medical Research Council (NHMRC) Project Grants (No. GNT1143163 to M.E.R., No. GNT1124812 to S.H.N. and M.E.R., and No. GNT1062820 to S.H.N.), Fellowship Nos. GNT1104924 to M.E.R. and GNT1087415 to K.A.L.C., the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (grant no. 2018-182819 to MER and no. 2018-182885 to K.A.L.C.), a Melbourne Research Scholarship to L.T., the Genomics Innovation Hub, the Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
(a) Ternary plot and plate layout of the RNA mixture design, which involved extracting RNA from 3 cell lines (H2228, H1975 and HCC827) in bulk, and mixing it in known proportions to get 8 mixtures that were then diluted to single cell equivalent amounts ranging from 3.75pg to 30pg. (b) Ternary plot of cell mixtures. Various 9 cell combinations were obtained using the same 3 cell lines. The number of replicates for each combination varies, as does the number of low quality samples included.
Supplementary Figure 2 Violin plots of quality control metrics for cells from each benchmarking dataset.
(a) The proportion of reads that map to introns. (b) The proportion of reads that map to exons. (c) The number of reads that map to exons. (d) The total number of counts per cell after UMI deduplication. (e) The amplification rate, which is defined by the ratio between the reads mapping to exons and the UMI counts after UMI deduplication. This measure reflects the library complexity. The sample size for each dataset is shown in Supplementary Table 1.
Supplementary Figure 3 Visualization of representative benchmarking datasets using t-SNE and UMAP and violin plot of the number of doublets.
(a) t-SNE and UMAP visualizations of 4 datasets. From left to right: 10X single cell using 3 cell lines; 10X single cell using 5 cell lines; a CEL-seq2 cell mixture and a CEL-seq2 RNA mixture. Each point represents a cell or ‘pseudo cell’ and the number of cells in each plot is indicated in the title. (b) Violin plot of doublets in each dataset, identified using Demuxlet (DBL: doublet, SNG: single cell). The number of single cells and doublets are shown on top of each violin. Doublets were excluded when calculating the performance metrics.
Silhouette widths calculated using the known biological groups after data have been normalized by different methods. The input to the silhouette width calculation is the distance between cells, which have been calculated using either (a) the gene expression matrix with the 1,000 most highly variable genes or (b) the first two PCs obtained from PCA. The sample sizes in this plots are the same as shown in Fig. 2a.
Supplementary Figure 5 Example clustering results and summary of clustering performance using ARI and the number of clusters.
(a) Examples of clustering results visualized by PCA (top) and t-SNE (bottom), with different colours representing the cluster assignments made by the selected method. The sample sizes are 340 for RNAmix_CEL-seq2, 285 for cellmix3 and 274 for sc_CEL-seq2. Coefficients obtained from linear models fitted using the ARI (b) or the number of clusters (c) as dependent variables, and experimental design, normalization methods, imputation methods and clustering methods as covariates. The coefficients measure whether particular features have positive or negative associations with the dependent variables.
Supplementary Figure 6 Coefficients from linear models used to quantify the impact different methods have on the trajectory analysis and data integration results.
Linear models were fitted using the evaluation metrics as dependent variables, with experimental design, normalization methods, imputation methods and either trajectory analysis or data integration methods as covariates. Positive coefficients indicates that a method is positively associated with the performance metrics. The evaluation metrics used as dependent variables for each plot were: (a) the correlations between calculated pseudotime and ground truth; (b) the overlap between the calculated trajectory and the known trajectory; (c) the average silhouette width of the known groups and (d) the kBET acceptance rate.
Supplementary Figure 7 Visualization of results from the trajectory analysis methods evaluated in our study.
Results for the RNAmix_Sort-seq, cellmix2 and cellmix1 analyses are shown. The dimension reduction method chosen for each method was as follows: PCA for Slingshot and TSCAN, DDR tree for Monocle2, diffusion map for DPT and locally linear embedding (LLE) for SLICER. The sample sizes are 266 for cellmix1, 268 for cellmix2 and 296 for RNAmix_Sort-seq.
Supplementary Figure 8 Additional data integration results for the single cell and RNA mixture datasets.
The kBET acceptance rate versus silhouette width coefficient for each method for the two combinations that had the highest silhouette width from the RNA mixture (a) and single cell (b) data integration analyses. The silhouette width assesses the ability of a given method to group biologically similar cells together while kBET assesses whether different batches are homogeneous after batch effect correction (scMerge_s: supervised scMerge; scMerge_us: unsupervised scMerge) (c) Additional PCA plots from the RNA mixture analysis and (d) t-SNE (perplexity = 30) plots from the single cell analysis to visualize the results of different method combinations. Cells are coloured according to protocol/batch information (t-SNE for MNNs and scanorama were based on batch corrected expression matrices). The samples sizes are n=636 and n=5,319 in panels c and d respectively.
Supplementary Figure 9 Performance of clustering on the RNA mixture datasets after data integration is applied.
Scatter plot of the ARI versus the number of clusters detected for the top performing normalization and imputation combinations. The true number of clusters for the RNA mixture experiment is 7.
Supplementary Figs. 1–9
Summary of the benchmarking datasets generated.
Summary of the data characteristics and data analysis tasks that can be compared by each experimental design.
Summary of integrative methods used to combine data from different protocols and datasets.
Individual performance metrics obtained from benchmarking analysis, organized by task.
Run times for different analysis pipelines.
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Tian, L., Dong, X., Freytag, S. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods 16, 479–487 (2019). https://doi.org/10.1038/s41592-019-0425-8
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