Feature selection via max-independent ratio and min-redundant ratio based on adaptive weighted kernel density estimation

2021 ◽  
Vol 568 ◽  
pp. 86-112
Author(s):  
Jianhua Dai ◽  
Ye Liu ◽  
Jiaolong Chen
Keyword(s):  
Feature Selection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Weighted Kernel

scholarly journals Nebulosa recovers single cell gene expression signals by kernel density estimation

2020 ◽  
Author(s):  
Jose Alquicira-Hernandez ◽  
Joseph E. Powell
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Dimensional Space ◽  
Kernel Density ◽  
R Package ◽  
Drop Out ◽  
Weighted Kernel ◽  
Low Dimensional

AbstractSummaryData sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised 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.Availability and implementationNebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa

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