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Volumn 35, Issue 16, 2019, Pages 2865-2867

M3Drop: Dropout-based feature selection for scRNASeq

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; FEATURE SELECTION; PREVALENCE; SIMULATION; SINGLE CELL RNA SEQ; GENOME; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE;

EID: 85065838422     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bty1044     Document Type: Article
Times cited : (184)

References (11)
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    • Islam, S. et al. (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods, 11, 163-166.
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    • Kiselev, V. Y. et al. (2018) scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods, 15, 359-362.
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  • 8
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    • Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
    • Macosko, E. Z. et al. (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161, 1202-1214.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.