Dataset Information

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.

SUBMITTER: Bilous M

PROVIDER: S-EPMC9375201 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Metacells untangle large and complex single-cell transcriptome networks.

Bilous Mariia M Tran Loc L Cianciaruso Chiara C Gabriel Aurélie A Michel Hugo H Carmona Santiago J SJ Pittet Mikael J MJ Gfeller David D

BMC bioinformatics 20220813 1

<h4>Background</h4>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.<h4>Results</h4>We develop a framework called SuperCell to merge highly similar cells into met ...[more]

PMID: 35963997

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Project description:BackgroundDeep-branching phylogenetic relationships are often difficult to resolve because phylogenetic signals are obscured by the long history and complexity of evolutionary processes, such as ancient introgression/hybridization, polyploidization, and incomplete lineage sorting (ILS). Phylogenomics has been effective in providing information for resolving both deep- and shallow-scale relationships across all branches of the tree of life. The olive family (Oleaceae) is composed of 25 genera classified into five tribes with tribe Oleeae consisting of four subtribes. Previous phylogenetic analyses showed that ILS and/or hybridization led to phylogenetic incongruence in the family. It was essential to distinguish phylogenetic signal conflicts, and explore mechanisms for the uncertainties concerning relationships of the olive family, especially at the deep-branching nodes.ResultsWe used the whole plastid genome and nuclear single nucleotide polymorphism (SNP) data to infer the phylogenetic relationships and to assess the variation and rates among the main clades of the olive family. We also used 2608 and 1865 orthologous nuclear genes to infer the deep-branching relationships among tribes of Oleaceae and subtribes of tribe Oleeae, respectively. Concatenated and coalescence trees based on the plastid genome, nuclear SNPs and multiple nuclear genes suggest events of ILS and/or ancient introgression during the diversification of Oleaceae. Additionally, there was extreme heterogeneity in the substitution rates across the tribes. Furthermore, our results supported that introgression/hybridization, rather than ILS, is the main factor for phylogenetic discordance among the five tribes of Oleaceae. The tribe Oleeae is supported to have originated via ancient hybridization and polyploidy, and its most likely parentages are the ancestral lineage of Jasmineae or its sister group, which is a "ghost lineage," and Forsythieae. However, ILS and ancient introgression are mainly responsible for the phylogenetic discordance among the four subtribes of tribe Oleeae.ConclusionsThis study showcases that using multiple sequence datasets (plastid genomes, nuclear SNPs and thousands of nuclear genes) and diverse phylogenomic methods such as data partition, heterogeneous models, quantifying introgression via branch lengths (QuIBL) analysis, and species network analysis can facilitate untangling long and complex evolutionary processes of ancient introgression, paleopolyploidization, and ILS.

Dataset Information

Metacells untangle large and complex single-cell transcriptome networks.

Background

Results

Conclusions

Publications

Metacells untangle large and complex single-cell transcriptome networks.

Similar Datasets

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