Open AccessSINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data
Author(s) -
Chuanqi Wang,
Jun Li
Publication year - 2019
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/btz801
Subject(s) - sinc function , classifier (uml) , scaling , computer science , artificial neural network , artificial intelligence , pattern recognition (psychology) , deep sequencing , data mining , invariant (physics) , mathematics , biology , biochemistry , geometry , genome , mathematical physics , computer vision , gene
Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly.
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