MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions

By Yael Baran, A. Bercovich, Arnau Sebé-Pedrós, Yaniv Lubling, A. Giladi, Elad Chomsky, Zohar Meir, Michael Hoichman, A. Lifshitz, Amos Tanay. Genome Biology, 2019

335 citations recorded by the source corpus.

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

Subfields: Cancer Research, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f20eae9b26631af5bf58b8266268a546120df19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Immunology

In Molecular Biology

Citations in the corpus

Comparison and evaluation of statistical error models for scRNA-seq (2021, methodology): “5 [42] for three different values of “K” parameter (200, and 300, and 400) with all other parameters as defaults.”
Single-Cell Analysis of Crohn’s Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy (2019, background): “Selecting highly variable genes: Similarly as in other publications (Baran et al., 2018; Jaitin et al., 2014; Paul et al., 2015), we selected genes with variability that could not be explained by multinomial sampling variance.”
Dysfunctional CD8+ T cells form a proliferative, dynamically regulated compartment within human melanoma (2018, positive): “We used the MetaCell algorithm (Baran et al., 2018) to identify homogeneous and robust groups of cells (‘‘metacells’’; STAR Methods) from scRNA-seq data, resulting in a detailed map of 324 metacells organized into seven broad lineages, including T cells (characterized by expression of CD3), NK cells”
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients (2020, positive): “In total, 50,615 cellspassedqualitycontrolandwereanalyzedusingtheMetaCell algorithm (Baran et al., 2019) (Figure 3A; STAR Methods).”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Strategies that aggregate similar cells into pseudo-bulk profiles or metacells 146,157,158 have been reported to be beneficial 87 .”
Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner (2019, methodology): “In Tables S1-S4, we refer to the log2 of a metacell’s footprint, which is the gene-wise average UMI count per metacell normalized by the median UMI count across all metacells (Baran et al., 2018).”
TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment (2022, positive): “characterized mini-cluster methods (29,30).”