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For: de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res 2019;47:e95. [PMID: 31226206 DOI: 10.1093/nar/gkz543] [Cited by in Crossref: 56] [Cited by in F6Publishing: 39] [Article Influence: 28.0] [Reference Citation Analysis]
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10 Chen YC, Suresh A, Underbayev C, Sun C, Singh K, Seifuddin F, Wiestner A, Pirooznia M. IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis. Gigascience 2019;8:giz121. [PMID: 31574155 DOI: 10.1093/gigascience/giz121] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
11 Bernstein MN, Ma Z, Gleicher M, Dewey CN. CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology. iScience 2021;24:101913. [PMID: 33364592 DOI: 10.1016/j.isci.2020.101913] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
12 Wei Z, Zhang S. CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data. Bioinformatics 2021;37:i51-8. [PMID: 34252936 DOI: 10.1093/bioinformatics/btab286] [Reference Citation Analysis]
13 Wilson CM, Fridley BL, Conejo-Garcia JR, Wang X, Yu X. Wide and deep learning for automatic cell type identification. Comput Struct Biotechnol J 2021;19:1052-62. [PMID: 33613870 DOI: 10.1016/j.csbj.2021.01.027] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Wilson CM, Ospina OE, Townsend MK, Nguyen J, Moran Segura C, Schildkraut JM, Tworoger SS, Peres LC, Fridley BL. Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data. Cancers (Basel) 2021;13:3031. [PMID: 34204319 DOI: 10.3390/cancers13123031] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Doddahonnaiah D, Lenehan PJ, Hughes TK, Zemmour D, Garcia-Rivera E, Venkatakrishnan AJ, Chilaka R, Khare A, Kasaraneni A, Garg A, Anand A, Barve R, Thiagarajan V, Soundararajan V. A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets. Genes (Basel) 2021;12:898. [PMID: 34200671 DOI: 10.3390/genes12060898] [Reference Citation Analysis]
16 Su K, Yu T, Wu H. Accurate feature selection improves single-cell RNA-seq cell clustering. Brief Bioinform 2021:bbab034. [PMID: 33611426 DOI: 10.1093/bib/bbab034] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Ekiz HA, Conley CJ, Stephens WZ, O'Connell RM. CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments. BMC Bioinformatics 2020;21:191. [PMID: 32414321 DOI: 10.1186/s12859-020-3538-2] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 11.0] [Reference Citation Analysis]
18 Pasquini G, Rojo Arias JE, Schäfer P, Busskamp V. Automated methods for cell type annotation on scRNA-seq data. Comput Struct Biotechnol J 2021;19:961-9. [PMID: 33613863 DOI: 10.1016/j.csbj.2021.01.015] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
19 Shen Y, Chu Q, Timko MP, Fan L. scDetect: a rank-based ensemble learning algorithm for cell type identification of single-cell RNA sequencing in cancer. Bioinformatics 2021:btab410. [PMID: 34048541 DOI: 10.1093/bioinformatics/btab410] [Reference Citation Analysis]
20 Boufea K, Seth S, Batada NN. scID Uses Discriminant Analysis to Identify Transcriptionally Equivalent Cell Types across Single-Cell RNA-Seq Data with Batch Effect. iScience 2020;23:100914. [PMID: 32151972 DOI: 10.1016/j.isci.2020.100914] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 16.0] [Reference Citation Analysis]
21 Ma SX, Lim SB. Single-Cell RNA Sequencing in Parkinson's Disease. Biomedicines 2021;9:368. [PMID: 33916045 DOI: 10.3390/biomedicines9040368] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Forcato M, Romano O, Bicciato S. Computational methods for the integrative analysis of single-cell data. Brief Bioinform 2021;22:20-9. [PMID: 32363378 DOI: 10.1093/bib/bbaa042] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 11.0] [Reference Citation Analysis]
23 Zelco A, Börjesson V, de Kanter JK, Lebrero-Fernandez C, Lauschke VM, Rocha-Ferreira E, Nilsson G, Nair S, Svedin P, Bemark M, Hagberg H, Mallard C, Holstege FCP, Wang X. Single-cell atlas reveals meningeal leukocyte heterogeneity in the developing mouse brain. Genes Dev 2021;35:1190-207. [PMID: 34301765 DOI: 10.1101/gad.348190.120] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Miao Z, Moreno P, Huang N, Papatheodorou I, Brazma A, Teichmann SA. Putative cell type discovery from single-cell gene expression data. Nat Methods 2020;17:621-8. [DOI: 10.1038/s41592-020-0825-9] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 20.0] [Reference Citation Analysis]
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26 Michielsen L, Reinders MJT, Mahfouz A. Hierarchical progressive learning of cell identities in single-cell data. Nat Commun 2021;12:2799. [PMID: 33990598 DOI: 10.1038/s41467-021-23196-8] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
27 Kuppe C, Ibrahim MM, Kranz J, Zhang X, Ziegler S, Perales-Patón J, Jansen J, Reimer KC, Smith JR, Dobie R, Wilson-Kanamori JR, Halder M, Xu Y, Kabgani N, Kaesler N, Klaus M, Gernhold L, Puelles VG, Huber TB, Boor P, Menzel S, Hoogenboezem RM, Bindels EMJ, Steffens J, Floege J, Schneider RK, Saez-Rodriguez J, Henderson NC, Kramann R. Decoding myofibroblast origins in human kidney fibrosis. Nature 2021;589:281-6. [PMID: 33176333 DOI: 10.1038/s41586-020-2941-1] [Cited by in Crossref: 44] [Cited by in F6Publishing: 39] [Article Influence: 44.0] [Reference Citation Analysis]
28 Chen S, Yan G, Zhang W, Li J, Jiang R, Lin Z. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nat Commun 2021;12:2177. [PMID: 33846355 DOI: 10.1038/s41467-021-22495-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
29 Sun S, Zhu J, Ma Y, Zhou X. Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol 2019;20:269. [PMID: 31823809 DOI: 10.1186/s13059-019-1898-6] [Cited by in Crossref: 55] [Cited by in F6Publishing: 32] [Article Influence: 27.5] [Reference Citation Analysis]
30 Cortal A, Martignetti L, Six E, Rausell A. Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nat Biotechnol 2021. [PMID: 33927417 DOI: 10.1038/s41587-021-00896-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
31 Duan B, Zhu C, Chuai G, Tang C, Chen X, Chen S, Fu S, Li G, Liu Q. Learning for single-cell assignment. Sci Adv 2020;6:eabd0855. [PMID: 33127686 DOI: 10.1126/sciadv.abd0855] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Shao X, Yang H, Zhuang X, Liao J, Yang P, Cheng J, Lu X, Chen H, Fan X. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res 2021:gkab775. [PMID: 34500471 DOI: 10.1093/nar/gkab775] [Reference Citation Analysis]
33 Cao ZJ, Wei L, Lu S, Yang DC, Gao G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST. Nat Commun 2020;11:3458. [PMID: 32651388 DOI: 10.1038/s41467-020-17281-7] [Cited by in Crossref: 24] [Cited by in F6Publishing: 13] [Article Influence: 24.0] [Reference Citation Analysis]
34 Yang X, Gao S, Wang T, Yang B, Dang N, Ye K. gCAnno: a graph-based single cell type annotation method. BMC Genomics 2020;21:823. [PMID: 33228535 DOI: 10.1186/s12864-020-07223-4] [Reference Citation Analysis]
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36 Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CS, Aparicio S, Baaijens J, Balvert M, Barbanson B, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BPF, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Raczkowski L, Reinders M, Ridder J, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biol 2020;21:31. [PMID: 32033589 DOI: 10.1186/s13059-020-1926-6] [Cited by in Crossref: 197] [Cited by in F6Publishing: 134] [Article Influence: 197.0] [Reference Citation Analysis]
37 Ma W, Su K, Wu H. Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction. Genome Biol 2021;22:264. [PMID: 34503564 DOI: 10.1186/s13059-021-02480-2] [Reference Citation Analysis]
38 Liu J, Fan Z, Zhao W, Zhou X. Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges. Front Genet 2021;12:655536. [PMID: 34135939 DOI: 10.3389/fgene.2021.655536] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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