Spatial multi-omic map of human myocardial infarction.
Atrial Remodeling
/ genetics
Case-Control Studies
Chromatin
/ genetics
Chromatin Assembly and Disassembly
Epigenome
Gene Expression Profiling
Humans
Myocardial Infarction
/ genetics
Myocardium
/ metabolism
Myocytes, Cardiac
/ metabolism
Single-Cell Analysis
Time Factors
Ventricular Remodeling
/ genetics
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
30
11
2020
accepted:
29
06
2022
pubmed:
11
8
2022
medline:
27
8
2022
entrez:
10
8
2022
Statut:
ppublish
Résumé
Myocardial infarction is a leading cause of death worldwide
Identifiants
pubmed: 35948637
doi: 10.1038/s41586-022-05060-x
pii: 10.1038/s41586-022-05060-x
pmc: PMC9364862
doi:
Substances chimiques
Chromatin
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
766-777Subventions
Organisme : NHLBI NIH HHS
ID : R35 HL161185
Pays : United States
Organisme : European Research Council
ID : ERC-STG 677448
Pays : International
Organisme : European Research Council
ID : ERC-COG 101043403
Pays : International
Organisme : European Research Council
ID : ERC-STG 101040726
Pays : International
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Wong, N. D. Epidemiological studies of CHD and the evolution of preventive cardiology. Nat. Rev. Cardiol. 11, 276–289 (2014).
pubmed: 24663092
doi: 10.1038/nrcardio.2014.26
Niccoli, G. et al. Optimized treatment of ST-elevation myocardial infarction. Circ. Res. 125, 245–258 (2019).
pubmed: 31268854
doi: 10.1161/CIRCRESAHA.119.315344
Prabhu Sumanth, D. & Frangogiannis Nikolaos, G. The biological basis for cardiac repair after myocardial infarction. Circ. Res. 119, 91–112 (2016).
pubmed: 27340270
pmcid: 4922528
doi: 10.1161/CIRCRESAHA.116.303577
Litviňuková, M. et al. Cells of the adult human heart. Nature 588, 466–472 (2020).
pubmed: 32971526
pmcid: 7681775
doi: 10.1038/s41586-020-2797-4
Wang, L. et al. Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. Nat. Cell Biol. 22, 108–119 (2020).
pubmed: 31915373
doi: 10.1038/s41556-019-0446-7
Tucker, N. R. et al. Transcriptional and cellular diversity of the human heart. Circulation 142, 466–482 (2020).
pubmed: 32403949
pmcid: 7666104
doi: 10.1161/CIRCULATIONAHA.119.045401
Dodou, E., Verzi, M. P., Anderson, J. P., Xu, S.-M. & Black, B. L. Mef2c is a direct transcriptional target of ISL1 and GATA factors in the anterior heart field during mouse embryonic development. Development 131, 3931–3942 (2004).
pubmed: 15253934
doi: 10.1242/dev.01256
Yan, C., Zhu, M., Staiger, J., Johnson, P. F. & Gao, H. C5a-regulated CCAAT/enhancer-binding proteins β and δ are essential in Fcγ receptor-mediated inflammatory cytokine and chemokine production in macrophages. J. Biol. Chem. 287, 3217–3230 (2012).
pubmed: 22147692
doi: 10.1074/jbc.M111.280834
Kovary, K. & Bravo, R. The jun and fos protein families are both required for cell cycle progression in fibroblasts. Mol. Cell. Biol. 11, 4466–4472 (1991).
pubmed: 1908553
pmcid: 361310
Li, S., Wang, D.-Z., Wang, Z., Richardson, J. A. & Olson, E. N. The serum response factor coactivator myocardin is required for vascular smooth muscle development. Proc. Natl Acad. Sci. USA 100, 9366–9370 (2003).
pubmed: 12867591
pmcid: 170924
doi: 10.1073/pnas.1233635100
Ge, Y. et al. Switching macrophage gene expression from inflammation-resolution to hemorrhage-resolution by redirection of activating transcription factor 1 (ATF1) binding by SMARCA4, BACH1 and histone H3K9 acetylation. Atherosclerosis 315, e2 (2020).
doi: 10.1016/j.atherosclerosis.2020.10.021
Pirruccello, J. P. et al. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat. Commun. 11, 2254 (2020).
pubmed: 32382064
pmcid: 7206184
doi: 10.1038/s41467-020-15823-7
Ruetten, H., Dimmeler, S., Gehring, D., Ihling, C. & Zeiher, A. M. Concentric left ventricular remodeling in endothelial nitric oxide synthase knockout mice by chronic pressure overload. Cardiovasc. Res. 66, 444–453 (2005).
pubmed: 15914109
doi: 10.1016/j.cardiores.2005.01.021
Shook, B. A. et al. Myofibroblast proliferation and heterogeneity are supported by macrophages during skin repair. Science 362, eaar2971 (2018).
pubmed: 30467144
pmcid: 6684198
doi: 10.1126/science.aar2971
Pakshir, P. et al. Dynamic fibroblast contractions attract remote macrophages in fibrillar collagen matrix. Nat. Commun. 10, 1850 (2019).
pubmed: 31015429
pmcid: 6478854
doi: 10.1038/s41467-019-09709-6
Aoyagi, T. & Matsui, T. Phosphoinositide-3 kinase signaling in cardiac hypertrophy and heart failure. Curr. Pharm. Des. 17, 1818–1824 (2011).
pubmed: 21631421
pmcid: 3337715
doi: 10.2174/138161211796390976
Mak, T. W., Hauck, L., Grothe, D. & Billia, F. p53 regulates the cardiac transcriptome. Proc. Natl Acad. Sci. USA 114, 2331–2336 (2017).
pubmed: 28193895
pmcid: 5338492
doi: 10.1073/pnas.1621436114
Bergmann, M. W. WNT signaling in adult cardiac hypertrophy and remodeling: lessons learned from cardiac development. Circ. Res. 107, 1198–1208 (2010).
pubmed: 21071717
doi: 10.1161/CIRCRESAHA.110.223768
Ahern, B. M. et al. Myocardial-restricted ablation of the GTPase RAD results in a pro-adaptive heart response in mice. J. Biol. Chem. 294, 10913–10927 (2019).
pubmed: 31147441
pmcid: 6635439
doi: 10.1074/jbc.RA119.008782
Lotteau, S. et al. Acute genetic ablation of cardiac sodium/calcium exchange in adult mice: implications for cardiomyocyte calcium regulation, cardioprotection, and arrhythmia. J. Am. Heart Assoc. 10, e019273 (2021).
pubmed: 34472363
pmcid: 8649274
doi: 10.1161/JAHA.120.019273
Fernandez-Caggiano, M. et al. Mitochondrial pyruvate carrier abundance mediates pathological cardiac hypertrophy. Nat. Metab. 2, 1223–1231 (2020).
pubmed: 33106688
pmcid: 7610404
doi: 10.1038/s42255-020-00276-5
Hama, N. et al. Rapid ventricular induction of brain natriuretic peptide gene expression in experimental acute myocardial infarction. Circulation 92, 1558–1564 (1995).
pubmed: 7664440
doi: 10.1161/01.CIR.92.6.1558
Waspe, L. E., Ordahl, C. P. & Simpson, P. C. The cardiac β-myosin heavy chain isogene is induced selectively in α1-adrenergic receptor-stimulated hypertrophy of cultured rat heart myocytes. J. Clin. Invest. 85, 1206–1214 (1990).
pubmed: 2156896
pmcid: 296553
doi: 10.1172/JCI114554
Beggah, A. T. et al. Reversible cardiac fibrosis and heart failure induced by conditional expression of an antisense mRNA of the mineralocorticoid receptor in cardiomyocytes. Proc. Natl Acad. Sci. USA 99, 7160–7165 (2002).
pubmed: 11997477
pmcid: 124545
doi: 10.1073/pnas.102673599
Bakker, M. L. et al. T-box transcription factor TBX3 reprogrammes mature cardiac myocytes into pacemaker-like cells. Cardiovasc. Res. 94, 439–449 (2012).
pubmed: 22419669
doi: 10.1093/cvr/cvs120
Jiang, J. et al. Cardiac myosin binding protein C regulates postnatal myocyte cytokinesis. Proc. Natl Acad. Sci. USA 112, 9046–9051 (2015).
pubmed: 26153423
pmcid: 4517252
doi: 10.1073/pnas.1511004112
Piroddi, N. et al. Myocardial overexpression of ANKRD1 causes sinus venosus defects and progressive diastolic dysfunction. Cardiovasc. Res. 116, 1458–1472 (2020).
pubmed: 31688894
doi: 10.1093/cvr/cvz291
Hill, C. et al. Inhibition of AP-1 signaling by JDP2 overexpression protects cardiomyocytes against hypertrophy and apoptosis induction. Cardiovasc. Res. 99, 121–128 (2013).
pubmed: 23612584
doi: 10.1093/cvr/cvt094
Kramann, R. et al. Perivascular Gli1+ progenitors are key contributors to injury-induced organ fibrosis. Cell Stem Cell 16, 51–66 (2015).
pubmed: 25465115
doi: 10.1016/j.stem.2014.11.004
Hulsmans, M. et al. Macrophages facilitate electrical conduction in the heart. Cell 169, 510–522.e20 (2017).
pubmed: 28431249
pmcid: 5474950
doi: 10.1016/j.cell.2017.03.050
Vieira, J. M. et al. The cardiac lymphatic system stimulates resolution of inflammation following myocardial infarction. J. Clin. Invest. 128, 3402–3412 (2018).
pubmed: 29985167
pmcid: 6063482
doi: 10.1172/JCI97192
Armulik, A., Abramsson, A. & Betsholtz, C. Endothelial/pericyte interactions. Circ. Res. 97, 512–523 (2005).
pubmed: 16166562
doi: 10.1161/01.RES.0000182903.16652.d7
Deshpande, S. S., Angkeow, P., Huang, J., Ozaki, M. & Irani, K. Rac1 inhibits TNF‐α‐induced endothelial cell apoptosis: dual regulation by reactive oxygen species. FASEB J. 14, 1705–1714 (2000).
pubmed: 10973919
doi: 10.1096/fj.99-0910com
Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. Nature 589, 281–286 (2021).
pubmed: 33176333
doi: 10.1038/s41586-020-2941-1
Li, Z. et al. Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nat. Commun. 12, 6386 (2021).
pubmed: 34737275
pmcid: 8568974
doi: 10.1038/s41467-021-26530-2
Bugg, D. et al. MBNL1 drives dynamic transitions between fibroblasts and myofibroblasts in cardiac wound healing. Cell Stem Cell 29, 419–433.e10 (2022).
pubmed: 35176223
doi: 10.1016/j.stem.2022.01.012
Davis, J. et al. MBNL1-mediated regulation of differentiation RNAs promotes myofibroblast transformation and the fibrotic response. Nat. Commun. 6, 10084 (2015).
pubmed: 26670661
doi: 10.1038/ncomms10084
Kramann, R. et al. Pharmacological GLI2 inhibition prevents myofibroblast cell-cycle progression and reduces kidney fibrosis. J. Clin. Invest. 125, 2935–2951 (2015).
pubmed: 26193634
pmcid: 4563736
doi: 10.1172/JCI74929
Lawler, J. Thrombospondin-1 as an endogenous inhibitor of angiogenesis and tumor growth. J. Cell. Mol. Med. 6, 1–12 (2002).
pubmed: 12003665
pmcid: 6740251
doi: 10.1111/j.1582-4934.2002.tb00307.x
Alexanian, M. et al. A transcriptional switch governs fibroblast activation in heart disease. Nature 595, 438–443 (2021).
pubmed: 34163071
pmcid: 8341289
doi: 10.1038/s41586-021-03674-1
Forte, E. et al. Cross-priming dendritic cells exacerbate immunopathology after ischemic tissue damage in the heart. Circulation 143, 821–836 (2021).
pubmed: 33297741
doi: 10.1161/CIRCULATIONAHA.120.044581
Dick, S. A. et al. Three tissue resident macrophage subsets coexist across organs with conserved origins and life cycles. Sci. Immunol. 7, eabf7777 (2022).
pubmed: 34995099
doi: 10.1126/sciimmunol.abf7777
MacDonald, L. et al. COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1+ neutrophils and CD14
pmcid: 8328085
doi: 10.1172/jci.insight.147413
Morse, C. et al. Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis. Eur. Respir. J. 54, 1802441 (2019).
pubmed: 31221805
pmcid: 8025672
doi: 10.1183/13993003.02441-2018
Bevan, L. et al. Specific macrophage populations promote both cardiac scar deposition and subsequent resolution in adult zebrafish. Cardiovasc. Res. 116, 1357–1371 (2020).
pubmed: 31566660
doi: 10.1093/cvr/cvz221
DeLeon-Pennell, K. Y. et al. CD36 is a matrix metalloproteinase-9 substrate that stimulates neutrophil apoptosis and removal during cardiac remodeling. Circ. Cardiovasc. Genet. 9, 14–25 (2016).
pubmed: 26578544
doi: 10.1161/CIRCGENETICS.115.001249
Ismahil, M. A. et al. Remodeling of the mononuclear phagocyte network underlies chronic inflammation and disease progression in heart failure: critical importance of the cardiosplenic axis. Circ. Res. 114, 266–282 (2014).
pubmed: 24186967
doi: 10.1161/CIRCRESAHA.113.301720
Shiraishi, M. et al. Alternatively activated macrophages determine repair of the infarcted adult murine heart. J. Clin. Invest. 126, 2151–2166 (2016).
pubmed: 27140396
pmcid: 4887176
doi: 10.1172/JCI85782
Bergmann, O. et al. Dynamics of cell generation and turnover in the human heart. Cell 161, 1566–1575 (2015).
pubmed: 26073943
doi: 10.1016/j.cell.2015.05.026
Yekelchyk, M., Guenther, S., Preussner, J. & Braun, T. Mono- and multi-nucleated ventricular cardiomyocytes constitute a transcriptionally homogenous cell population. Basic Res. Cardiol. 114, 36 (2019).
pubmed: 31399804
pmcid: 6689038
doi: 10.1007/s00395-019-0744-z
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
pubmed: 28846088
pmcid: 5623139
doi: 10.1038/nmeth.4407
Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1, 263–280 (2022).
Tippani, M. et al. VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data. bioRxiv https://www.biorxiv.org/content/10.1101/2021.08.04.452489v2 (2022).
Curaj, A., Simsekyilmaz, S., Staudt, M. & Liehn, E. et al. Minimal invasive surgical procedure of inducing myocardial infarction in mice. J. Vis. Exp. 99, e52197 (2015).
Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).
pubmed: 20513764
pmcid: 2878938
doi: 10.1083/jcb.201004104
Bahry, E. et al. RS-FISH: precise, interactive, fast, and scalable FISH spot detection. Preprint at bioRxiv https://doi.org/10.1101/2021.03.09.434205 (2021).
Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2021).
pubmed: 34795433
pmcid: 9010346
doi: 10.1038/s41587-021-01094-0
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
pubmed: 22930834
pmcid: 5554542
doi: 10.1038/nmeth.2089
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Germain, P.-L., Lun, A., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 10, 979 (2021).
pubmed: 35814628
doi: 10.12688/f1000research.73600.1
O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20, 210 (2019).
pubmed: 31623682
pmcid: 6796327
doi: 10.1186/s13059-019-1830-0
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
pubmed: 22287627
pmcid: 3378882
doi: 10.1093/nar/gks042
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).
pubmed: 33633365
pmcid: 8012210
doi: 10.1038/s41588-021-00790-6
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
pubmed: 18798982
pmcid: 2592715
doi: 10.1186/gb-2008-9-9-r137
Li, Z. et al. Identification of transcription factor binding sites using ATAC-seq. Genome Biol. 20, 45 (2019).
pubmed: 30808370
pmcid: 6391789
doi: 10.1186/s13059-019-1642-2
Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).
pubmed: 31701148
Badia-i-Mompel, P. et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Adv. 2, vbac016 (2022).
doi: 10.1093/bioadv/vbac016
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
pubmed: 25722852
pmcid: 4342193
doi: 10.1186/s13742-015-0047-8
Ulirsch, J. C. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51, 683–693 (2019).
pubmed: 30858613
pmcid: 6441389
doi: 10.1038/s41588-019-0362-6
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
pubmed: 30954476
pmcid: 6625319
doi: 10.1016/j.cels.2018.11.005
Hansen, B. B. & Klopfer, S. O. Optimal full matching and related designs via network flows. J. Comput. Graph. Stat. 15, 609–627 (2006).
doi: 10.1198/106186006X137047
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
pubmed: 27571553
doi: 10.1038/nmeth.3971
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
pubmed: 28825706
pmcid: 5623146
doi: 10.1038/nmeth.4401
Freeman, L. C. Centrality in social networks conceptual clarification. Soc. Networks 1, 215–239 (1978).
doi: 10.1016/0378-8733(78)90021-7
Page L. et al. The PageRank Citation Ranking: Bringing Order to the Web. (Stanford IfoLab, 1999).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).
pubmed: 35027729
doi: 10.1038/s41587-021-01139-4
Schubert, M. et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 9, 20 (2018).
pubmed: 29295995
pmcid: 5750219
doi: 10.1038/s41467-017-02391-6
Holland, C. H., Szalai, B. & Saez-Rodriguez, J. Transfer of regulatory knowledge from human to mouse for functional genomics analysis. Biochim. Biophys. Acta 1863, 194431 (2020).
doi: 10.1016/j.bbagrm.2019.194431
Zhu, J., Sun, S. & Zhou, X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 22, 184 (2021).
pubmed: 34154649
pmcid: 8218388
doi: 10.1186/s13059-021-02404-0
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
pubmed: 21546393
pmcid: 3106198
doi: 10.1093/bioinformatics/btr260
Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692 (2021).
pmcid: 8689983
doi: 10.1093/nar/gkab1028
Grant, A. O. Cardiac ion channels. Circ. Arrhythm. Electrophysiol. 2, 185–194 (2009).
pubmed: 19808464
doi: 10.1161/CIRCEP.108.789081
Tanevski, J., Flores, R. O. R., Gabor, A., Schapiro, D. & Saez-Rodriguez, J. Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biol. 23, 97 (2022).
Pawlowsky-Glahn V. & Buccianti A. Compositional Data Analysis: Theory and Applications. (John Wiley & Sons, 2011).
doi: 10.1002/9781119976462
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).
pubmed: 27909575
pmcid: 5112579
Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat. Commun. 13, 3224 (2022).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
pubmed: 32103204
doi: 10.1038/s41596-020-0292-x
Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021).
pubmed: 33749993
pmcid: 7983032
doi: 10.15252/msb.20209923
Nagai, J. S., Leimkühler, N. B., Schaub, M. T., Schneider, R. K. & Costa, I. G. CrossTalkeR: analysis and visualization of ligand–receptor networks. Bioinformatics 37, 4263–4265 (2021).
pubmed: 35032393
doi: 10.1093/bioinformatics/btab370
Alquicira-Hernandez, J. & Powell, J. E. Nebulosa recovers single cell gene expression signals by kernel density estimation. Bioinformatics 37, 2485–2487 (2021).
doi: 10.1093/bioinformatics/btab003
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).