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Adaptive Dropout Imputer (ADImpute)

Bioconductor version: Release (3.19)

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

Author: Ana Carolina Leote [cre, aut]

Maintainer: Ana Carolina Leote <anacarolinaleote at>

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


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:

ADImpute tutorial HTML R Script
Reference Manual PDF


biocViews GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Software, Transcriptomics
Version 1.14.0
In Bioconductor since BioC 3.12 (R-4.0) (3.5 years)
License GPL-3 + file LICENSE
Depends R (>= 4.0)
Imports checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils
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Source Package ADImpute_1.14.0.tar.gz
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