An article published in BMCBioInformatics presents SuperCell: a framework to merge highly similar cells into metacells and perform standard single-cell RNA sequencing data analyses at the metacell level.
Unlike clustering, the primary aim of metacells is not to identify groups of cells with a specific biological interpretation , but to simplify, accelerate and improve the analysis of scRNA-seq data. As such, the exact number of metacells is not meant to have a specific biological significance and can be fixed by the users based on the available computational resources.
) which leads to a significant risk of losing some of the biologically relevant heterogeneity of the single-cell data. For the construction of metacells, we used the walktrap algorithm. Owing to its hierarchical structure, this algorithm enables users to explore different graining levels without having to recompute the metacells for each choice of. This can be useful considering the heterogeneity in size and complexity of scRNA-seq datasets. Comparison with the Louvain clustering algorithm suggests that the walktrap algorithm provides a reasonable solution .
The high purity of metacells indicates that they mainly consist of cells of the same cell type. However, we also observed that a small fraction cells from rare cell types can be mixed with cells from other cell types in some metacells. To overcome this issue, we implemented the possibility to build metacells in a way that is consistent with a priori defined cell type annotations .
The metacell concept shares similarity with other computational approaches developed for scRNA-seq data analysis. Akin to imputation [], it averages signals over cells with high transcriptomic similarity. However, results of imputations can be difficult to use with very large datasets, since the total number of cells remains the same and the imputed gene expression matrices are less sparse than the original ones.
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