Metacells untangle large and complex single-cell transcriptome networks.
Coarse-graining
Computational biology
Metacells
Single-cell transcriptomics
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
13 Aug 2022
13 Aug 2022
Historique:
received:
10
06
2022
accepted:
23
07
2022
entrez:
13
8
2022
pubmed:
14
8
2022
medline:
17
8
2022
Statut:
epublish
Résumé
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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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.
Identifiants
pubmed: 35963997
doi: 10.1186/s12859-022-04861-1
pii: 10.1186/s12859-022-04861-1
pmc: PMC9375201
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
336Subventions
Organisme : SNF Project Grant
ID : 31003A_173156
Informations de copyright
© 2022. The Author(s).
Références
Nature. 2018 Aug;560(7719):494-498
pubmed: 30089906
Cell. 2018 Aug 23;174(5):1293-1308.e36
pubmed: 29961579
Genome Biol. 2018 Feb 6;19(1):15
pubmed: 29409532
Cell. 2018 Jul 26;174(3):716-729.e27
pubmed: 29961576
Nat Biotechnol. 2019 Jun;37(6):685-691
pubmed: 31061482
Nat Neurosci. 2018 Feb;21(2):290-299
pubmed: 29335606
Nat Methods. 2020 Apr;17(4):405-413
pubmed: 32123397
Nat Methods. 2021 Jun;18(6):635-642
pubmed: 34059827
Oncoimmunology. 2020 Mar 12;9(1):1737369
pubmed: 32313720
Mol Syst Biol. 2019 Jun 19;15(6):e8746
pubmed: 31217225
Genome Res. 2021 Apr;31(4):677-688
pubmed: 33627473
Genome Biol. 2019 Aug 14;20(1):166
pubmed: 31412909
Elife. 2017 Dec 05;6:
pubmed: 29206104
Genome Biol. 2016 Jul 01;17(1):144
pubmed: 27368803
Genome Biol. 2022 Apr 19;23(1):100
pubmed: 35440087
Cell. 2015 May 21;161(5):1202-1214
pubmed: 26000488
Cell Syst. 2019 Jun 26;8(6):483-493.e7
pubmed: 31176620
Nat Methods. 2019 Jan;16(1):43-49
pubmed: 30573817
Nature. 2015 Sep 10;525(7568):251-5
pubmed: 26287467
Front Genet. 2020 Oct 06;11:786
pubmed: 33193561
Cell. 2021 Feb 4;184(3):792-809.e23
pubmed: 33545035
Genome Res. 2018 Jun;28(6):878-890
pubmed: 29724792
Mol Cell. 2017 Feb 16;65(4):631-643.e4
pubmed: 28212749
Bioinformatics. 2018 Apr 1;34(7):1246-1248
pubmed: 29228172
Genome Biol. 2014;15(12):550
pubmed: 25516281
Nat Methods. 2017 May;14(5):483-486
pubmed: 28346451
Nat Genet. 2000 May;25(1):25-9
pubmed: 10802651
Cell. 2021 Apr 1;184(7):1895-1913.e19
pubmed: 33657410
Nat Methods. 2019 Dec;16(12):1289-1296
pubmed: 31740819
Nat Methods. 2020 Feb;17(2):137-145
pubmed: 31792435
J Exp Med. 2021 Jan 4;218(1):
pubmed: 33601412
Nat Biotechnol. 2018 Jun;36(5):411-420
pubmed: 29608179
Genome Biol. 2019 Oct 11;20(1):206
pubmed: 31604482
Genomics Proteomics Bioinformatics. 2019 Apr;17(2):201-210
pubmed: 31202000
Bioinformatics. 2010 Jan 1;26(1):139-40
pubmed: 19910308
Science. 2020 Feb 21;367(6480):
pubmed: 32079746
Development. 2019 Jun 17;146(12):
pubmed: 31160421
Cell. 2020 Jun 25;181(7):1475-1488.e12
pubmed: 32479746
Nucleic Acids Res. 2017 Mar 17;45(5):e30
pubmed: 27899618
Sci Adv. 2020 Jul 08;6(28):eaba1983
pubmed: 32832599
Immunity. 2016 Feb 16;44(2):343-54
pubmed: 26872698
Nat Methods. 2019 Jun;16(6):479-487
pubmed: 31133762
Genome Biol. 2020 Aug 27;21(1):218
pubmed: 32854757
Genome Biol. 2019 Mar 19;20(1):59
pubmed: 30890159
Science. 2016 Apr 8;352(6282):189-96
pubmed: 27124452
Nat Commun. 2021 Sep 28;12(1):5692
pubmed: 34584091
Genome Biol. 2020 Feb 7;21(1):31
pubmed: 32033589
Science. 2020 Nov 13;370(6518):
pubmed: 33184181
Nat Genet. 2020 Jul;52(7):709-718
pubmed: 32601473
Nat Commun. 2017 Jan 16;8:14049
pubmed: 28091601
Immunity. 2019 May 21;50(5):1317-1334.e10
pubmed: 30979687
Cell. 2015 May 21;161(5):1187-1201
pubmed: 26000487
PLoS One. 2014 Oct 14;9(10):e109760
pubmed: 25314013
Nat Med. 2021 Mar;27(3):491-503
pubmed: 33619369