Metacells untangle large and complex single-cell transcriptome networks

Abstract

Background Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Results We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. Conclusions SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

“…To begin, the highly-sparse high-throughput single-cell/nuclei RNA-seq data is coarse-grained by collapsing a k number of more similar cells identified at the low dimensional representation of the multidimensional RNA-seq data (26). This approach reduces sample size while also decreasing data sparsity, allowing us better to capture the strength of the relationship between genes' expression (22).…”

“…Furthermore, non-biological factors frequently affect data during library preparation, reducing our ability to detect biologically accurate correlation structures (21). To address those challenges in differential single-cell gene regulatory network analyses, we present SCORPION (Single-Cell Oriented Reconstruction of PANDA Individually Optimized Gene Regulatory Networks), a tool that uses coarse-graining of single-cell/nuclei RNA-seq data to reduce sparsity and improve the ability to detect the gene regulatory network's underlying correlation structure (22). The coarse-grained data generated is then used to reconstruct the gene regulatory network using a network refinement strategy through the PANDA (Passing Attributes between Networks for Data Assimilation) message passing algorithm (23).…”

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data

“…Metacells are far more granular than clusters and are optimized for homogeneity within cell groups rather than for separation between clusters. However, existing approaches [8][9][10] fail on scATAC-seq data; aggressively cull outliers (particularly inappropriate for disease studies, which are often driven by rare cell populations); and are poorly distributed across the phenotypic space. Consequently, metacells are not routinely used in single-cell analysis, and scATAC-seq data have remained underused.…”

Building and analyzing metacells in single-cell genomics data

“…These two pioneering studies reflect two main aspects of the usage of metacells: (i) enhancing signal in sparse scRNA-seq data and (ii) lowering computational burden due to the large size of single-cell genomics data. Since then, several other studies have built upon the metacell concept [39][40][41] , extending its application to other Starting from a single-cell profile matrix, space and metrics are first defined for identifying cells displaying high similarity in their profiles (e.g., high transcriptomic similarity in scRNA-seq data).…”