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NEUral network-based single-Cell Annotation tool

Bioconductor version: Release (3.19)

NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types.

Author: Ziyi Li [aut], Hao Feng [aut, cre]

Maintainer: Hao Feng <hxf155 at>

Citation (from within R, enter citation("NeuCA")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

NeuCA Package User's Guide HTML R Script
Reference Manual PDF


biocViews Classification, DataImport, DataRepresentation, GeneExpression, NeuralNetwork, Preprocessing, RNASeq, Sequencing, SingleCell, Software, Transcription, Transcriptomics
Version 1.10.0
In Bioconductor since BioC 3.14 (R-4.1) (2.5 years)
License GPL-2
Depends R (>= 3.5.0), keras, limma, e1071, SingleCellExperiment, kableExtra
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Suggests BiocStyle, knitr, rmarkdown, networkD3
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Follow Installation instructions to use this package in your R session.

Source Package NeuCA_1.10.0.tar.gz
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macOS Binary (x86_64) NeuCA_1.10.0.tgz
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Source Repository (Developer Access) git clone
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