Nebulosa recovers single-cell gene expression signals by kernel density estimation | Zendy

José Alquicira-Hernández | Zendy; Joseph E. Powell | Zendy

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Nebulosa recovers single-cell gene expression signals by kernel density estimation

Author(s) -

José Alquicira-Hernández,

Joseph E. Powell

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab003

Subject(s) - kernel density estimation , kernel (algebra) , r package , computer science , computational biology , expression (computer science) , biology , pattern recognition (psychology) , artificial intelligence , mathematics , statistics , computational science , combinatorics , estimator , programming language

Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualized in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.

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